Description:TECHNICAL FIELD
The present disclosure relates to the field of automobiles, and more particularly to a method and system for fault detection in a particulate filter deployed in automobiles.
BACKGROUND
Due to continuous rise in air pollution and a consequent deterioration of air quality across the globe, there has been a stricter enforcement of emission standards against exhaust gas emitted from automobiles. For compliance, the automobiles may include an advanced Exhaust After-treatment System (EAS) to restrict the emission of environmentally harmful gases such as NOx, CO2, SO2, and particulate matter (PM) within a permissible range. The EAS may include a particulate filter, such as a Diesel Particulate filter (DPF), which captures and traps PM from the exhaust gases. Over time, these particles are burned off through a process called regeneration, ensuring that the filter remains effective.
However, after prolonged use, the particulate filter may incur a fault, such as a through hole, a blind hole, and the like, which may result in particulate matter escaping along with the exhaust gas passing therethrough. Detecting faults in a particulate filter has significant importance in upholding the proper functioning of the EAS and may play a crucial role in ensuring compliance with emissions regulations. In conventional systems, faults within the particulate filter may be identified by incorporating a Particulate Matter (PM) sensor downstream of the particulate filter. This sensor can monitor PM levels in the exhaust gas downstream of the particulate filter. Subsequently, the data collected by the PM sensor may be used to identify any potential faults or anomalies that may be present in the particulate filter. For example, the data collected by the PM sensor may be analyzed by an onboard processing unit to determine the amount of particulate matter mass. If the amount of particulate matter mass exceeds a threshold, a fault may be detected in the particulate filter. Another method involves the utilization of a differential pressure sensor placed across the particulate filter to sense variations in exhaust gas pressure. The obtained differential pressure (DP) data may also be subjected to analysis to determine a DP value across the particulate filter. If the DP value decreases over a predefined time period, the fault may be identified in the particulate filter.
However, the installation of PM sensor in conventional systems may result in increased costs of manufacturing and maintenance. For example, the installation of PM sensors in conventional systems might require additional engineering and material considerations, which can lead to higher initial manufacturing costs for automobiles. In addition, PM sensors are exposed to harsh operating conditions, including elevated temperatures, vibration, and chemical exposure. Ensuring the long-term reliability and durability of these sensors can contribute to higher maintenance costs. DP-based fault detection, as explained before, measure the pressure difference across the particulate filter, which can be affected by various factors such as driving conditions, fuel quality, and engine performance. Inaccurate readings or temporary spikes in pressure difference might trigger false alarms, indicating a fault when there isn't one.
Therefore, there is a need for a cost-effective and accurate system for fault detection in the particulate filter that may satisfactorily perform under varied operational conditions of the automobile.
SUMMARY

In an embodiment, a fault detection method for fault detection in a particulate filter is disclosed. In an embodiment, the method may include determining by a fault detection device, a plurality of instantaneous differential pressure (DP) values, based on pressure data of the exhaust gas that may be received by one or more pressure sensors over a time period. Further, the method may include determining a DP variance rate over the time period, based on the plurality of instantaneous DP values, and an average of the plurality of instantaneous DP values over the time period. The method may further include determining a reference DP variance rate by the fault detection device based on a plurality of instantaneous reference DP values, and an average reference DP value that may be determined over the time period. The plurality of instantaneous reference DP values may be determined based on a flow rate, and a temperature of the exhaust gas. The method may include detecting a fault in the particulate filter based on a comparison between the DP variance rate, the reference DP variance rate, and a predetermined threshold variance.
In an embodiment, a fault detection method for a particulate filter is disclosed. The method may include determining by a fault detection device based on a differential pressure (DP) variance rate comparison model, a DP variance rate, and a reference DP variance rate with a plurality of instantaneous DP values of exhaust gas across the particulate filter. The DP variance rate is based on the plurality of instantaneous DP values and an average DP value over the time period, and the reference DP variance rate is based on a plurality of instantaneous reference DP values and an average reference DP value over the time period. The method may further include determining, by the fault detection device, a first likelihood of a fault in the particulate filter based on the DP variance comparison model. The method may further include determining, by the fault detection device based on a particulate matter (PM) mass comparison model, a first PM mass and a second PM mass. In an embodiment, the first PM mass is based the plurality of instantaneous DP values, and a flow rate and a temperature of the exhaust gas, and the second PM mass is based on an air flow, a fuel quantity, a fuel injection pressure, an amount of exhaust gas recirculated. The method may further include determining, by the fault detection device, a second likelihood of a fault in the particulate filter based on the PM mass comparison model. The method may further include determining, by the fault detection device, an amount of soot accumulated in the particulate filter based on the second PM mass. The method may further include determining, by the fault detection device based on the amount of soot accumulated, a first weight corresponding to the first likelihood and a second weight corresponding to the second likelihood. The method may further include detecting, by the fault detection device, a fault in the particulate filter based on a weighted first likelihood and a weighted second likelihood. The weighted first likelihood is determined based on the first weight corresponding to the first likelihood and the weighted second likelihood is determined based on the second weight corresponding to the second likelihood.
In an embodiment, a fault detection device for a particulate filter is disclosed. The fault detection device may include a processor and a memory coupled to the processor. The memory stores a first set of processor executable instructions, which, on execution, causes the processor to determine, based on a differential pressure (DP) variance rate comparison model, a DP variance rate, and a reference DP variance rate with a plurality of instantaneous DP values of exhaust gas across the particulate filter. In an embodiment, the DP variance rate is based on the plurality of instantaneous DP values and an average DP value over the time period. Further, the reference DP variance rate is based on a plurality of instantaneous reference DP values and an average reference DP value over the time period. Upon execution of the first set of processor executable instructions, the processor may further determine a first likelihood of a fault in the particulate filter using the DP variance rate comparison model. In an embodiment, the memory may further store a second set of processor executable instructions, which, on execution, causes the processor to determine, based on a particulate matter (PM) mass comparison model a first PM mass and a second PM mass. In an embodiment, the first PM mass is based on an instantaneous DP value from the plurality of instantaneous DP values and the second PM mass based on an air flow, a fuel quantity, a fuel injection pressure, an amount of exhaust gas recirculated. In an embodiment, the processor may determine a second likelihood of the fault in the particulate filter using the PM mass comparison model. In an embodiment, the memory may also include a third set of processor executable instructions, which on execution, causes the processor to determine an amount of soot accumulated in the particulate filter based on the second PM mass. Based on the amount of soot accumulated, the processor may determine a first weight corresponding to the first likelihood and a second weight corresponding to the second likelihood. The processor may be configured to detect a fault in the particulate filter based on a weighted first likelihood and a weighted second likelihood. The weighted first likelihood is determined based on the first weight corresponding to the first likelihood and the weighted second likelihood is determined based on the second weight corresponding to the second likelihood.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
FIG. 1 illustrates a schematic block diagram of a particulate filter system, in accordance with an embodiment of the present disclosure.
FIG. 2 illustrates a functional block diagram of the fault detection device, in accordance with an embodiment of the present disclosure.
FIG. 3 illustrates a weight allocation criterion for the first likelihood of fault and the second likelihood of fault in the particulate filter, in accordance with an embodiment of the present disclosure.
FIG. 4 illustrates a flowchart of a fault detection method in a particulate filter, in accordance with an embodiment of the present disclosure.
FIG. 5 illustrates another flowchart of a fault detection method for a particulate filter, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION
Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims. Additional illustrative embodiments are listed.
References will now be made to exemplary embodiments of the disclosure, as illustrated in the accompanying drawings. Wherever possible, the same numerals have been used to refer to the same or like parts. The following paragraphs describe the present disclosure with reference to FIGs. 1-5.
As explained earlier, the installation of PM sensor in conventional systems may result in increased costs of manufacturing and maintenance because of the requirement of additional engineering and material considerations, which can lead to higher initial manufacturing costs for automobiles. Further, PM sensors are exposed to harsh operating conditions, including elevated temperatures, vibration, and chemical exposure which may also require iterative maintenance for long-term reliability and durability which can contribute to higher maintenance costs.
To this end, a fault detection system is disclosed. Now referring to FIG. 1, a schematic block diagram 100 of a particulate filter system 102 is illustrated in accordance with an embodiment of the present disclosure. The particulate filter system 102 may be communicably connected to a sensing unit 108 which may be further connected to a fault detection device 110. By way of an example, the fault detection device 110 may be implemented as a computing device which may be a control unit of the automobile. In an embodiment, the control unit may include an Electronic Control Unit (ECU) disposed in the automobile. In an embodiment, the fault detection device 110 includes a processor 120 and a memory 122. In an embodiment, the functions of the processor 120 may interchangeably be performed by a controller (not shown). In an embodiment, examples of processor 120 may include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, Nvidia®, FortiSOC™ system-on-a-chip processors or other future processors. The memory 122 may store instructions that, when executed by the processor 120, cause the processor 122 to detect one or more faults in a particulate filter 104. The memory 122 may be a non-volatile memory or a volatile memory. Examples of non-volatile memory may include but are not limited to a flash memory, a Read Only Memory (ROM), a Programmable ROM (PROM), Erasable PROM (EPROM), and Electrically EPROM (EEPROM) memory. Examples of volatile memory may include but are not limited to Dynamic Random Access Memory (DRAM), and Static Random-Access memory (SRAM).
In an embodiment, the sensing unit 108, may be communicably connected to the fault detection device 110 via vehicle communication bus, operating on wireless protocols, including, but not limited to A²B (Automotive Audio Bus), AFDX, ARINC 429, Byteflight, CAN (Controller Area Network) , D2B – (Domestic Digital Bus), FlexRay, IDB-1394, IEBus, I²C, ISO 9141-1/-2, J1708 and J1587, J1850, J1939 and ISO 11783 – an adaptation of CAN for commercial (J1939) and agricultural (ISO 11783) vehicles, Keyword Protocol 2000 (KWP2000), LIN (Local Interconnect Network), MOST (Media Oriented Systems Transport), IEC 61375, SMARTwireX, SPI, and/or VAN – (Vehicle Area Network), and the like. Alternatively, the sensors, actuators, and the other components may also be hard-wired to the sensing unit 108.
In an embodiment, the particulate filter system 102 may include a particulate filter 104. In an embodiment, the particulate filter 104 may be, but not limited to a Diesel Particulate Filter (DPF). In general, the particulate filter 104 may be provided in an exhaust gas passage, such as, but not limited to, an exhaust tailpipe of the automobile, to trap and collect particulate matter (interchangeably referred to as soot) from an exhaust gas passing therethrough.
Referring now to FIG. 1, exhaust gas from an engine of the automobile may enter an inlet of the particulate filter 104 through an upstream exhaust gas passage 112. Further, the exhaust gas may exit from an outlet of the particulate filter 104 towards an exhaust tail pipe through a downstream exhaust passage 114.
In an embodiment, the sensing unit 108 may include one or more sensors, such as an air-flow sensor to measure air flow rate flowing to the engine. Further, the one or more sensors may include a temperature sensor to measure temperature of gases or any component in the automobile. Further, the one or more sensors may include an EGR sensor to determine a critical flow orifice to flow rate corresponding to an amount of exhaust gas recirculated (EGR) in the engine of the automobile. Further, the one or more sensors may include other sensors to determine one or more operational parameters of the automobile and the like. Further, in an embodiment, the sensing unit 108 may include one or more pressure sensors (not shown) or a differential pressure sensor (interchangeably referred to as pressure sensors) that may be connected to the upstream exhaust gas passage 112 and to the downstream exhaust gas passage 114.
In an embodiment, the pressure sensors of the sensing unit 108 may be connected to the upstream exhaust gas passage 112 and the downstream exhaust gas passage 114 by way of a plurality of hoses. Referring to FIG. 1, the pressure sensors of the sensing unit 108 may be connected to the upstream exhaust gas passage 112 through an upstream hose 116. Similarly, the pressure sensors of the sensing unit 108 may be connected to the downstream exhaust gas passage 114 through a downstream hose 118. Accordingly, an instantaneous pressure in the upstream exhaust gas passage 112 may be determined by the one or more pressure sensors of the sensing unit 108 connected via the upstream hose 116. Similarly, an instantaneous pressure in the downstream exhaust gas passage 114 may be determined by the one or more pressure sensors of the sensing unit 108 connected via the downstream hose 118. Accordingly, a portion of the exhaust gas flowing through the upstream exhaust gas passage 112 may be transmitted to the pressure sensors of the sensing unit 108 through the upstream hose 116. Similarly, a portion of the exhaust gas flowing through the downstream exhaust gas passage 114 may be transmitted to the pressure sensors of the sensing unit 108 through the downstream hose 116.
Further, the sensing unit 108 may be configured to sense an instantaneous or a real-time pressure value or a differential pressure (DP) of the exhaust gas across the particulate filter 104. Accordingly, the pressure sensors of the sensing unit 108 may sense instantaneous pressure values of the exhaust gas in the upstream exhaust gas passage 112 and the downstream exhaust gas passage 114.
In an embodiment, the fault detection device 110 may be configured to determine an instantaneous (DP) value of the exhaust gas in the upstream exhaust gas passage 112 and the downstream exhaust gas passage 114 based on the DP of the exhaust gas in the upstream exhaust gas passage 112 and the downstream exhaust gas passage 114.
Due to continuous detection of pressure values by the sensing unit 108, the fault detection device 110 may determine a plurality of instantaneous differential pressure (DP) values of the exhaust gas across the particulate filter 104 based on the pressure data received from the sensing unit 108 over a predefined time period. Based on the instantaneous pressure values, the fault detection device 110 may determine the DP value of the exhaust gas across the particulate filter 104 at various time instants over the predefined time period.
With the ageing of the automobile and due to running of the automobile for long distances, the particulate filter 104 of the particulate filter system 102 may become faulty or clogged with soot. Examples of one or more faults in the particulate filter 104 may include, but not limited to, a physical deformity such as a through hole, a square blind hole, cracks, and the like. Due to such faults, there may be leakage of unfiltered exhaust gas across the particulate filter 104. It should be appreciated that such leakage of unfiltered exhaust gas may correspond to a fault in the particulate filter 104. The fault may be determined by the fault detection device 110 by monitoring the instantaneous DP value of the exhaust gas across the particulate filter 104.
In an embodiment, a higher magnitude of instantaneous DP value across the particulate filter 104 may be determined in case of no faults in the particulate filter 104. The higher magnitude of the instantaneous DP value may be attributed to a resistance exerted/provided by the particulate filter 104 to the exhaust gas flowing therethrough. As a result, a backpressure against the flow of the exhaust gas may be generated, causing a drop in the pressure of the exhaust gas across the particulate filter 104. Accordingly, due to leakage, the magnitude of instantaneous DP value across the particulate filter 104 sensed by the sensing unit 108 may be lesser than the magnitude of instantaneous DP value determined across a non-faulty particulate filter 104. Such detection may correspond to a fault in the particulate filter 104.
However, during motion of the automobile, a transient condition such as acceleration and a deceleration of the vehicle may result in fluctuations, or variations in the magnitude of instantaneous DP value which may indicate false positives such as indication of the fault even when the particulate filter 104 may not be faulty. Such variations may also indicate false negatives such as no fault may be detected even when the particulate filter 104 may be faulty. Therefore, to mitigate such fluctuations, a DP variance rate may be determined by the fault detection device 110 using a first methodology.
Accordingly, as per the first methodology, the fault detection device 110 may determine a first likelihood of a fault in the particulate filter 104 based on a differential pressure (DP) variance rate. The first methodology may be referred to hereinafter as a DP variance comparison model. In an embodiment, the fault detection device 110 may determine a DP variance rate over the predefined time period based on the plurality of instantaneous DP values and an average DP value determined for the predefined time period.
In an embodiment, the fault detection device 110 may be configured to determine one or more faults in the particulate filter 104 in accordance with the DP variance comparison model. In an embodiment, the fault detection device 110 may determine a reference DP variance rate for the predefined time period based on a plurality of instantaneous reference DP values. Further, the fault detection device 110 may determine an average reference DP value for the predefined time period based on an average of the plurality of instantaneous reference DP values. In an embodiment, the reference DP variance rate and the average reference DP value may be calculated instantly using an equation (provided below) and stored in the memory 122. In an embodiment, the plurality of instantaneous reference DP values may be determined over the predefined time period during experiments.
In accordance with the DP variance comparison model, the fault detection device 110 may determine a first likelihood of one or more faults in the particulate filter 104 based on a comparison between the DP variance rate and the reference DP variance rate.
Due to running of the automobile for extended periods, there may be an accumulation of particulate matter or soot in the particulate filter 104. Due to continuous accumulation of soot in the particulate filter 104, the differential pressure across the particulate filter 104 may gradually rise with time. Accordingly, due to soot accumulation, the measured plurality of instantaneous DP values after soot accumulation may be high as compared to the measured plurality of instantaneous DP values prior to soot accumulation. Accordingly, due to soot accumulation there may be an increase in the DP variance rate, thereby rendering the fault detection by the fault detection device 110 based on the DP variance comparison model less accurate. Therefore, to accurately detect a fault in cases of an increased soot load in the particulate filter 104, the fault detection device 110 may determine one or more faults in the particulate filter 104 based on a particulate matter mass comparison. Therefore, along with the first methodology of fault determination based on the DP variances, the fault detection device 110 may determine one or more faults in the particulate filter 104 based on a second methodology. The second methodology may be referred hereinafter as particulate matter mass comparison model that may include determination of one or more faults in the particulate filter 104 based on determination of particulate matter (PM) mass comparison. Accordingly, the fault detection device 110 may determine a first PM mass based on the plurality of instantaneous DP values, a flow rate, and a temperature of the exhaust gas in the particulate filter 104 at plurality of time instants. In an embodiment, the first PM mass may be determined using one or more PM mass lookup tables, which may include a plurality of values of the first PM mass based on the plurality of instantaneous DP values, a flow rate, and a temperature of the exhaust gas in the particulate filter 104. In an embodiment, the flow rate may depict a quantity of exhaust gas passing through the particulate filter 104 in the predefined time period. Further, the fault detection device 110 may determine a second PM mass based on an air flow, a fuel quantity, a fuel injection pressure, and an amount of exhaust gas recirculated (EGR) in an engine (not shown) of the automobile. In an embodiment, the air flow, the fuel quantity, the fuel injection pressure values, and the amount of EGR may be determined instantly during the operation of the automobile. In an embodiment, the second PM mass may be measured based on a second PM mass lookup table, which may include a predefined value of second PM mass corresponding to various value range of air flow, fuel quantity, fuel injection pressure, and the amount of EGR.
In an embodiment, the second PM mass lookup table may be stored in the memory 122 during the manufacturing of the automobile. In an embodiment, the predefined second PM mass values for various value range of the air flow, the fuel quantity, the fuel injection pressure values, and the amount of EGR may depict standard values during standard or optimal operating conditions. In an embodiment, the fault detection device 110 may determine a second likelihood of one or more faults in the particulate filter 104 based on the first PM mass and the second PM mass in accordance with the particulate matter mass comparison model.
Further, the fault detection device 110 may determine an amount of soot accumulated in the particulate filter 104. In an embodiment, the amount of soot accumulated in the particulate filter 104 may be based on the second PM mass determined in the predefined time period. Particularly, the amount of soot may be the second PM mass determined based on the second PM mass lookup table by the fault detection device 110. In a working scenario, the particulate filter 104 may undergo a regeneration in which the soot accumulated in the particulate filter 104 may be periodically ignited to prevent permanent clogging of the particulate filter 104. Accordingly, the amount of soot accumulation in the particulate filter 104 may increase until regeneration and post regeneration the amount of soot accumulation may become negligible. Accordingly, one or more faults in the particulate filter 104 may be determined based on an output from either the DP variance comparison model and/or the particulate matter mass comparison model based on the amount of soot accumulated in the particulate filter 104.
Accordingly, the fault detection device 110 may determine a weightage corresponding to the first likelihood and to the second likelihood of a fault in the particulate filter 104 based on the amount of soot accumulated in the particulate filter 104. In an embodiment, the determination of the weightage corresponding to the first likelihood and to the second likelihood of a fault may include determining a first weight corresponding to the DP variance comparison model and a second weight corresponding to the PM mass comparison model. In an embodiment, each of the first weight and the second weight may be in a range of 0 to 1. In an embodiment, at an instant of determination of a fault in the particulate filter 104 based on the amount of soot accumulated, a sum of the first weight and the second weight may be equal to one.
Accordingly, the fault detection device 110 may determine a fault in the particulate filter 104 based on a weighted first likelihood and a weighted second likelihood. In an embodiment, the weighted first likelihood and the weighted second likelihood may be determined based on the first weight and the second weight determined corresponding to the first likelihood and to the second likelihood of a fault in the particulate filter 104.
Referring now to FIG. 2, a functional block diagram 200 of the fault detection device 110 is illustrated, in accordance with an embodiment of the present disclosure. The fault detection device 110 may include a sensing module 202, a DP variance comparison module 204, a PM mass comparison module 206, a weight determination module 208, and an alert module 210.
The sensor module 202 may receive sensor data from the one or more pressure sensors of the sensing unit 108. Further, the sensor module 202 may determine a plurality of DP values for the predefined time period from the sensing unit 108. In an embodiment, the sensor module 202 may determine the DP values based on the plurality of instantaneous pressure values of the exhaust gas across the particulate filter 104 determined by the sensing unit 108. In an embodiment, the plurality of instantaneous DP values or the plurality of instantaneous pressure values may correspond to real-time DP values or real-time pressure values as determined by the sensing unit 108.
The DP variance rate module 204 may determine a fault in the particulate filter 104 based on the DP variance comparison model. In an embodiment, the DP variance rate module 204 may be implemented by the processor 120 executing a first set of instructions stored in the memory 122 of the fault detection device 110. In an embodiment, the DP variance rate module 204 may determine a DP variance rate based on the DP values determined by the sensor module 202 for the predefined time period. Accordingly, the DP variance rate module 204 may determine one or more faults based on a comparison between the DP variance rate and a reference DP variance rate. In an embodiment, the reference DP variance rate may be calculated instantaneously based on exhaust rate, exhaust temperature and pressure drop of the exhaust gas across the particulate filter 104. In an embodiment, the pressure drop of the exhaust gas at various instants may be recorded and saved in the memory 122. The DP variance rate module 204 may determine a first likelihood of one or more faults in the particulate filter 104 based on the DP variance comparison model.
In an embodiment, as explained earlier, an average reference DP value may be calculated based on an average of the plurality of instantaneous reference DP values for the predefined time period. Further, the reference DP values may correspond to DP values measured across the particulate filter 104 when the particulate filter 104 is healthy. Further, the reference DP values may be determined based on a plurality of exhaust gas parameters, and properties of the exhaust gas. In an embodiment, the plurality of exhaust gas parameters may include a flow rate and a temperature of exhaust gas recorded at an instant. In an embodiment, the properties of the exhaust gas may include dynamic viscosity and density of the exhaust gas. In an embodiment, the fault detection device 110 may be configured to determine a reference DP value corresponding based on an equation (1.1) given below, considering properties of the exhaust gas, which as explained earlier, may include viscosity and density. In an embodiment, the equation may be given as below:
(Xi)ref=(A1×µ)+(A2×?^2×?)+(A3×µ×?) … (1.1)
where,
(Xi)ref = Reference DP value,
µ= Dynamic viscosity of the exhaust gas,
?= flow rate of the exhaust gas,
? = Density of the exhaust gas, and
A1, A2, A3 = equation coefficients.

In an embodiment, the equation coefficients A1, A2, and A3 may be determined by conducting a plurality of lab-scale experiments. Further, the equation coefficients A1, A2, and A3 may be predefined and saved in the memory 122 of the fault detection device 110.
As it should be appreciated that density and dynamic viscosity of the exhaust gas may be dependent on the temperature of the exhaust gas. For example, decrease in temperature of the exhaust gas may also decrease the dynamic viscosity, and may increase the density of the exhaust gas. Similarly, increase in temperature of the exhaust gas may increase the dynamic viscosity and may decrease the density of the exhaust gas. Such variation in the dynamic viscosity and density may be accounted for using correction factors corresponding to the real-time temperature of the exhaust gas. In an embodiment, the correction factors corresponding to different exhaust gas temperatures may be part of a lookup table stored in the memory 122.
The DP variance rate module 204 may determine a reference DP variance rate based on a plurality of reference DP values. Further, an average of the plurality of the reference DP values may be determined. As explained before, the plurality of reference DP values may be determined based on experimentation. The average of the plurality of the reference DP values may be an average of the plurality of the reference DP values of “n” reference samples considered during the experimentation, determined over the predefined time period “t”. In an embodiment, DP variance rate module 204 may determine the reference DP variance rate based on equation (1.2) given below:
(Xref)var=[(?_(i=0)^n¦?{(Xi)ref-(Xavg)ref}?^2 )/n]/t … (1.2)
where
n = total number of reference samples
(Xref)var = reference DP variance rate,
(Xi)ref = plurality of instantaneous reference DP values over the predefined time “t”, and
(Xavg)ref = an average of the reference plurality of the instantaneous DP values
In an embodiment, the DP variance rate module 204 may determine a DP variance rate based on the plurality of instantaneous DP values, and the average of the plurality of the DP values. In an embodiment, the DP variance rate may be determined based on equation (1.3) given below:
Xvar=[(?_(i=0)^n¦?{(Xi)-(Xavg)}?^2 )/n]/t … (1.3)
where
n = total number of reference samples,
Xvar = DP variance rate,
Xi = instantaneous DP value over the predefined time “t", and
(Xavg) = an average of the plurality of the instantaneous DP values.
The DP variance rate comparison module 204 may compare the DP variance rate and the reference DP variance rate. In case the comparison may be greater than a predefined threshold a first likelihood of a fault in the particulate filter 104 may be determined.
Accordingly, the DP variance rate module 204 may determine a first likelihood of a fault in the particulate filter 104 based on a comparison of an instantaneous DP variance with its corresponding reference DP variance. For example, the first likelihood of the fault in the particulate filter 104 may be detected in case a difference between a reference DP variance rate in a time period and the instantaneous DP variance rate at that time period is above a predefined threshold level.
Table 1 provided below illustrates an exemplary determination of a first likelihood of the fault. It may be observed that the comparison of the reference DP variance rate and the DP variance rate may include a calculation of a difference between the reference DP variance rate and the DP variance rate. However, in another embodiment, the comparison may include calculating a ratio of the reference DP variance rate and the DP variance rate. Accordingly, in case the comparison exceeds a predefined threshold of “0.35”, the outcome based on the first likelihood of the fault may be determined as “Fault”.
Reference DP Variance rate (Xref)var DP variance rate (X)var Comparison:
((Xref)var – (X)var) Threshold Outcome (Fault/No Fault)
1.36 0.80 0.56 0.35 Fault
1.25 1.05 0.25 No Fault
2.25 1.25 1 Fault
2.8 2.3 0.5 Fault
Table 1
The PM mass comparison module 206 of FIG. 2 may determine a second likelihood of one or more faults in the particulate filter 104 based on the PM mass comparison model. In an embodiment, the PM mass comparison module 206 may be implemented by the processor 120 executing a second set of instructions stored in the memory 122 of the fault detection device 110. The PM mass comparison module 206 may detect the second likelihood of one or more faults in cases of high soot load in the particulate filter 104. The PM mass comparison module 206 may determine the second likelihood of one or more faults in the particulate filter 104, based on determination of a first PM mass and a second PM mass. In an embodiment, the first PM mass may be determined based on the plurality of instantaneous DP values, temperature value, and flow rate of the exhaust gas. For example, the values of the first PM mass may be determined based on a first PM mass lookup table stored in the memory 122 of the fault detection device. In an embodiment, the first PM mass may correspond to real time values of the PM mass present in the exhaust gas passing through the particulate filter 104. Also, it must be noted that the first PM mass may be updated iteratively over a predefined time period based on a plurality of instantaneous DP values, or until the regeneration of particulate matter in the particulate filter 104 is initiated.
In an embodiment, the first PM mass and the second PM mass may be determined based on one or more lookup tables stored in the memory 122. In an embodiment, Table 2 below depicts a first PM mass lookup table for determination of first PM mass based on plurality of instantaneous DP values, temperature, and flow rate of the exhaust gas. It must be noted that the following first PM mass lookup table to determine the first PM mass is for illustration purposes only and the determination of the first PM mass may not be limited to a single first PM mass lookup table. In an embodiment, the first PM mass may be determined using multiple first PM mass lookup tables.
Temperature (K) Flow rate (kg/s) Instantaneous DP values (kPa) First PM Mass (grams)
489.01 51.24 102.62 3.45
568.89 57.62 120.23 4.69
622.32 59.12 159.3 6.32
769.36 64.12 178.98 7.16
Table 2
In an embodiment, the PM mass comparison module 206 may determine a second PM mass based on one or more engine parameters including, but not limited to, a flow rate of the exhaust gas from the engine, a fuel injection pressure, and the amount of EGR, etc. In an exemplary embodiment, the PM mass comparison module 206 may determine the second PM mass using a second lookup table, or a second PM mass lookup table listing a plurality of second PM mass values for corresponding values of flow rate of the exhaust gas from the engine, a fuel injection pressure, and the amount of EGR. The second PM mass values may be calculated based on engine parameters during normal operation of the particulate filter 104. i.e., in the absence of faults in the particulate filter 104. Particularly, the second PM mass may signify an amount of PM mass expected to be deposited on the particulate filter 104 during an optimum running of the engine. In an embodiment, the second PM mass may be updated iteratively over the predefined time period based on an instantaneous soot mass flow, or until the regeneration of particulate matter in the particulate filter 104 is initiated.
Table 4 below depicts an exemplary second PM mass lookup table based on which the second PM mass value may be determined. It must be noted that the following second PM mass lookup table to determine the second PM mass is for illustration purposes only and the determination of the second PM mass may not be limited to a single second PM mass lookup table. In an embodiment, the second PM mass may be determined using multiple second PM mass lookup tables.
Fuel Injection Pressure (bar) Exhaust Gas Flow rate (kg/s) EGR (kg/s) Second PM Mass (grams)
18.50 44.21 22.12 12.90
21.45 49.56 24.56 15.43
22.23 51.84 30.21 17.22
19.16 46.99 20.19 14.78
Table 3
In an embodiment, the second PM mass estimated based on the lookup table as above may be updated based on one or more DPF regeneration parameters, such as exhaust flow rate, exhaust temperature, concentration of pollutants in the exhaust gases, which may control a DPF regeneration operation. This is because a regeneration operation may result in the reduction of the second PM mass as estimated above. For example, if the exhaust flow rate and the exhaust temperature are high, an active regeneration may be performed, which results in the burning-off of the soot, causing reduction in the second PM mass.
The PM mass comparison module 206 may determine the second likelihood of the fault in the particulate filter 104 by comparing the first PM mass and the second PM mass. In an embodiment, the PM mass comparison module 206 may determine an outcome corresponding to the second likelihood of fault based on a PM mass fault lookup table as shown in Table 4 below. Based on Table 4 the PM mass comparison module 206 may determine an outcome of the second likelihood as “Fault” in case comparison between the first PM mass and the second PM mass may exceed a threshold PM mass. Accordingly, the outcome in such a scenario may indicate a fault in the particulate filter 104 which may be caused due to presence of greater PM mass than the second PM mass expected to be deposited on the particulate filter 104. For example, referring to Table 4, it may be observed that in case the first PM mass i.e., 2.3 mg is lower than the second PM Mass i.e., 10 mg. The comparison between the first PM mass and the second PM mass exceeds the threshold PM mass i.e., 6 mg, accordingly, a fault in the particulate filter 104 may be present accordingly to second likelihood.
First PM mass value (grams) Second PM mass value(grams) Comparison:
(Second PM Mass – First PM Mass) grams Threshold PM mass (grams) Outcome (Fault/No Fault)
2.3 10 7.7 6 Fault
3.33 12 8.67 Fault
6.69 8 1.31 No Fault
6.21 7 0.79 No Fault
Table 4
With continued reference to FIG. 2, the weight determination module 208, based on executing by the processor 120 a third set of instructions stored in the memory 122 may determine a first weight corresponding to the first likelihood of one or more faults in the particulate filter 104 determined by the DP variance comparison module 204. Further, the weight determination module 208 may determine a second weight corresponding to the second likelihood of one or more faults in the particulate filter 104 determined by the PM mass comparison module 206. In an embodiment, the fault detection device 110 may continuously determine the first likelihood of the fault and the second likelihood of the fault in the particulate filter 104. Since, the amount of PM accumulating in the particulate filter 104 varies from minimum level to maximum level until the thermal regeneration of the particulate filter 104. The weight determination module 208 may determine the first weight and the second weight based on the amount of PM accumulated on the particulate filter 104.
The determination of the first weight and the second weight by the weight determination module 208 is described in detail in reference to FIG. 3. FIG. 3 illustrates a weight allocation criterion 300 for the first likelihood of fault and the second likelihood of fault in the particulate filter 104, in accordance with an embodiment of the present disclosure. The weight allocation criterion 300 depicts on an x-axis an amount of soot accumulation (in percentage) in the particulate filter until regeneration. In an example, the first likelihood of fault 302 as determined by based on the DP variance comparison module 204 may have a first weight equal to “1” until the amount of PM accumulated is in a range of 0-20%. Further, the second likelihood of fault 304 as determined by based on the PM mass comparison module 206 may have a second weight equal to “0” until the amount of PM accumulated is in a range of 0-20%. Further, in an example, the second likelihood of fault 304 as determined by based on the PM mass comparison module 206 may have a second weight equal to “1” when the amount of PM accumulated is in a range of 60-100%. Consequently, the first likelihood of fault 302 as determined by based on the DP variance comparison module 204 may have a first weight equal to “0” when the amount of PM accumulated is in a range of 60-100%. Further, in case the amount of PM accumulated is in a range of 20-60%, depicted by a window 306, the first weight and the second weight may be determined such that a sum of the first weight and the second weight at a particular amount of PM accumulated may be equal to “1”. Accordingly, the values of each of the first weight and the second weight may be in a range of 0-1 based on the amount of PM accumulated.
In an explanatory embodiment, if 50% of the soot may be accumulated in the particulate filter 104, the first weight and the second weight may be equal to 0.5. Accordingly, the first likelihood of fault and the second likelihood of fault may be given to equal weightage. In an exemplary embodiment, if 45% of the soot may be accumulated in the particulate filter 104, the first weight may be greater than the second weight. For example, the first weight may be equal to a weightage of 0.6, and the second weight may be equal to a weightage of 0.4. Accordingly, the first likelihood of fault may be given more weightage than the second likelihood of fault. In contrast, when 55% of the soot may be accumulated in the particulate filter 104, the second weight may be given more weightage than the first weight. For example, the first weight may be equal to 0.4, while the second weight may be equal to 0.6. Accordingly, the second likelihood of fault may be given more weightage than the first likelihood of fault.
Accordingly, the fault detection device 104 may detect a fault in the particulate filter 104 based on determination of a weighted first likelihood and a weighted second likelihood. The weighted first likelihood may be determined based on the first weight corresponding to the first likelihood. Further, the weighted second likelihood may be determined based on the second weight corresponding to the second likelihood.
Referring to FIG. 2, the alert module 210 may configure the controller (not shown in figure) to generate a visual and/or audio alert in case one or more faults detected based on the weighted first likelihood and the weighted second likelihood. In an embodiment, a warning notification may be displayed on an infotainment device of the input/output device (not shown) of the automobile.
With reference to FIG. 4, illustrating a flowchart 400 of a fault detection method for fault detection in a particulate filter 104. In an embodiment, the method may include a first step 402 in which a plurality of instantaneous differential pressure (DP) values, based on a pressure data of the exhaust gas that may be received by the sensing unit 108 over a time period may be determined. Further, in the next step 404, a DP variance rate over the time period, based on the plurality of instantaneous DP values, and an average of the plurality of instantaneous DP values over the time period may be determined by the fault detection device 110. The DP variance rate may be based on the plurality of instantaneous DP values and an average DP value over the time period, and the reference DP variance rate may be based on a plurality of instantaneous reference DP values and an average reference DP value over the time period. In the next step 406, a reference DP variance rate by the fault detection device 110 based on a plurality of instantaneous reference DP values, and an average reference DP value may be determined by the fault detection device 110 over the time period. The plurality of instantaneous reference DP values may be determined based on a flow rate, and a temperature of the exhaust gas. In the next step 408, a fault in the particulate filter 104 may be detected by the fault detection device 110 based on a comparison between the DP variance rate, and the reference DP variance rate. This is already explained in detail with conjunction to FIGs. 1-3.
Now refer to FIG. 5, illustrating a flowchart 500 of a fault detection method for a particulate filter 104 for a particulate filter is disclosed. In a first step 502 a DP variance rate and a reference DP variance rate with a plurality of instantaneous DP values of exhaust gas across the particulate filter 104 may be determined by the fault detection device 110. The reference DP variance rate may be determined by the control unit based on the DP variance comparison model. The DP variance rate may be based on the plurality of instantaneous DP values and an average DP value over the time period, and the reference DP variance rate may be based on a plurality of instantaneous reference DP values and an average reference DP value over the time period. In the next step 504, a first likelihood of a fault in the particulate filter based on the DP variance comparison model may be determined by the fault detection device 110. In the next step 506, a first PM mass and a second PM mass may be determined by the fault detection device 110 based on a particulate matter (PM) mass comparison model. In an embodiment, the first PM mass is based the plurality of instantaneous DP values, and a flow rate and a temperature of the exhaust gas, and the second PM mass is based on an air flow, a fuel quantity, a fuel injection pressure, an amount of exhaust gas recirculated. Accordingly, a second likelihood of a fault in the particulate filter may be determined by the fault detection device 110 based on the PM mass comparison model. In the next step 508, an amount of soot accumulated in the particulate filter 104 may be determined based on the second PM mass. In the next step 510, a first weight corresponding to the first likelihood and a second weight corresponding to the second likelihood may be determined by the fault detection device 110 based on the amount of soot accumulated. In the next step 512, a fault in the particulate filter based on a weighted first likelihood and a weighted second likelihood may be determined by the fault detection device 110. The weighted first likelihood may be determined based on the first weight corresponding to the first likelihood and the weighted second likelihood is determined based on the second weight corresponding to the second likelihood. This is already explained in detail with conjunction to FIGs. 1-3.
Therefore, the device and method for detection of fault in the particulate filter 104 as illustrated by the aforementioned embodiments may be advantageous over the conventional devices, as removal of the PM sensor may prove to be cost-effective. Also, robust models such as DP variance comparison model, and the PM mass comparison model may also enable detection of physical damages of the particulate filter 104.
With respect to the use of any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for the sake of clarity.
It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" and/or "an" should typically be interpreted to mean "at least one" or "one or more"); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B."
While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

, Claims:
CLAIMS
I/We claim:
1. A fault detection method (400) for a particulate filter, comprising:
determining (402), by a fault detection device (110), a plurality of instantaneous differential pressure (DP) values of exhaust gas across the particulate filter (104) based on pressure data received from a sensor unit (108) over a time period;
determining (404), by the fault detection device (110), a DP variance rate over the time period based on the plurality of instantaneous DP values and an average DP value over the time period;
determining (406), by the fault detection device (110), a reference DP variance rate over the time period based on a plurality of instantaneous reference DP values and an average reference DP value over the time period; and
detecting (408), by the fault detection device (110), a fault in the particulate filter (104) based on a comparison between the DP variance rate and the reference DP variance rate.

2. The fault detection method (400) as claimed in claim 1, wherein each instantaneous reference DP value is determined based on a flow rate and a temperature of the exhaust gas at an instant of time.

3. A fault detection device (110) for a particulate filter, comprising:
a processor (120) and a memory (122) coupled to the processor (120), wherein the memory (122) stores:
a first set of instructions, which, on execution, causes the processor (120) to:
determine a plurality of instantaneous differential pressure (DP) values of exhaust gas across the particulate filter (104) based on pressure data received from the sensor unit (108) over a time period;
determine a DP variance rate over the time period based on the plurality of instantaneous DP values and an average DP value over the time period;
determine a reference DP variance rate over the time period based on a plurality of instantaneous reference DP values and an average reference DP value over the time period; and
determine a fault in the particulate filter (104) based on a comparison between the DP variance rate and the reference DP variance rate.

4. A fault detection method (500) for a particulate filter, comprising:
determining (502), by a fault detection device (110) based on a differential pressure (DP) variance rate comparison model, a DP variance rate, and a reference DP variance rate with a plurality of instantaneous DP values of exhaust gas across the particulate filter (104), wherein:
the DP variance rate is based on the plurality of instantaneous DP values and an average DP value over the time period; and
the reference DP variance rate is based on a plurality of instantaneous reference DP values and an average reference DP value over the time period;
determining (504), by the fault detection device (110), a first likelihood of a fault in the particulate filter (104) based on the DP variance comparison model (204);
determining (506), by the fault detection device (110) based on a particulate matter (PM) mass comparison model, a first PM mass and a second PM mass, wherein:
the first PM mass is based on the plurality of instantaneous DP values, and a flow rate and a temperature of the exhaust gas; and
the second PM mass is based on an air flow, a fuel quantity, a fuel injection pressure, and an amount of exhaust gas recirculated;
determining (508), by the fault detection device (110), a second likelihood of a fault in the particulate filter (104) based on the PM mass comparison model (206);
determining, by the fault detection device (110), an amount of soot accumulated in the particulate filter (104) based on the second PM mass;
determining (510), by the fault detection device (110) based on the amount of soot accumulated, a first weight corresponding to the first likelihood and a second weight corresponding to the second likelihood; and
detecting (512), by the fault detection device (110), a fault in the particulate filter (104) based on a weighted first likelihood and a weighted second likelihood, wherein the weighted first likelihood is determined based on the first weight corresponding to the first likelihood and the weighted second likelihood is determined based on the second weight corresponding to the second likelihood.

5. The fault detection method (500) as claimed in claim 4, wherein when the amount of soot accumulated is less than a first predefined accumulated soot threshold, the first weight is greater than the second weight.

6. The fault detection method (500) as claimed in claim 4, when the amount of soot accumulated is more than a second predefined accumulated soot threshold, the second weight is greater than the first weight.

7. The fault detection method (500) as claimed in claim 4, wherein detecting the fault based on the DP variance rate comparison model comprises:
performing a comparison between a DP variance rate, a reference DP variance rate, and a predetermined threshold variance, and wherein each instantaneous reference DP value is determined based on a flow rate and a temperature of the exhaust gas.

8. The fault detection method (500) as claimed in claim 4, wherein detecting the fault based on the PM mass comparison model comprises:
performing comparison between the first PM mass and the second PM mass, and wherein the first PM mass is updated based on a plurality of instantaneous DP values, and the second PM mass is updated based on an instantaneous soot mass flow.

9. The fault detection method (500) as claimed in claim 4, wherein the reference DP variance rate DP is determined based on a plurality of reference DP values based on a dynamic viscosity of the exhaust gas, a density of the exhaust gas, and one or more predefined parameters corresponding to the exhaust gas over the time period, wherein the one or more predefined parameters comprises temperature of exhaust gas and a flow rate of exhaust gas.

10. The fault detection method (500) as claimed in claim 4, the second PM mass is updated based on one or more DPF regeneration parameters, wherein the one or more DPF regeneration parameters comprises a flow rate of the exhaust gas, a temperature of the exhaust gas, and/or a concentration level of pollutants in the exhaust gas.

11.A fault detection device (110) for a particulate filter, comprising:
a processor (120) and a memory (122) coupled to the processor (120), wherein the memory (122) stores:
a first set of instructions, which, on execution, causes the processor (120) to:
determine, based on a differential pressure (DP) variance rate comparison model, a DP variance rate, and a reference DP variance rate with a plurality of instantaneous DP values of exhaust gas across the particulate filter (104), wherein:
the DP variance rate is based on the plurality of instantaneous DP values and an average DP value over the time period;
the reference DP variance rate is based on a plurality of instantaneous reference DP values and an average reference DP value over the time period; and
determine a first likelihood of a fault in the particulate filter (104) using the DP variance rate comparison model (206);
a second set of instructions, which, on execution, causes the processor (120) to:
determine, based on a particulate matter (PM) mass comparison model a first PM mass and a second PM mass, wherein:
the first PM mass is based on an instantaneous DP value from the plurality of instantaneous DP values; and
the second PM mass based on an air flow, a fuel quantity, a fuel injection pressure, an amount of exhaust gas recirculated; and
determine a second likelihood of the fault in the particulate filter (104) using the PM mass comparison model;
a third set of instructions, which on execution, causes the processor (120) to:
determine an amount of soot accumulated in the particulate filter (104) based on the second PM mass;
based on the amount of soot accumulated, a first weight corresponding to the first likelihood and a second weight corresponding to the second likelihood; and
detect a fault in the particulate filter (104) based on a weighted first likelihood and a weighted second likelihood, wherein the weighted first likelihood is determined based on the first weight corresponding to the first likelihood and the weighted second likelihood is determined based on the second weight corresponding to the second likelihood.

12. The fault detection device (110) as claimed in claim 11, wherein when the amount of soot accumulated is less than a first predefined accumulated soot threshold, the first weight is greater than the second weight.

13. The fault detection device (110) as claimed in claim 11, wherein when the amount of soot accumulated is more than a second predefined accumulated soot threshold, the second weight is greater than the first weight.

14. The fault detection device (110) as claimed in claim 11, wherein the DP variance rate comparison model is configured to:
compare between the DP variance rate, the reference DP variance rate, and a predetermined threshold variance, and wherein each instantaneous reference DP value is determined based on a flow rate and a temperature of the exhaust gas.
.
15. The fault detection device (110) as claimed in claim 11, wherein the PM mass comparison model is configured to:
compare between the first PM mass and the second PM mass, and wherein the first PM mass is updated based on a plurality of instantaneous DP values, and the second PM mass is updated based on an instantaneous soot mass flow.

16. The fault detection device (110) as claimed in claim 11, wherein the reference DP variance rate is determined based on a plurality of reference DP values based on a dynamic viscosity of the exhaust gas, a density of the exhaust gas, and
one or more predefined parameters corresponding to the particulate filter (104), wherein the one or more parameters comprises temperature of exhaust gas and a flow rate of the exhaust gas.