Description:TECHNICAL FIELD
Embodiments disclosed herein generally relate to monitor exhaust after treatment devices for internal combustion engine and more particularly, to detect failure in Diesel Particulate Filter (DPF).
BACKGROUND
Diesel engines produce a variety of particles during combustion of fuel/air mixture due to incomplete combustion. The composition of the particles varies widely dependent upon engine type, age, and emission specification that the engine was designed to meet. Diesel Particulate Matter (PM) resulting from the incomplete combustion of diesel fuel produces soot, whose particles include tiny nanoparticles which can be smaller than one micrometer (one micron). Soot and other particles from diesel engines worsen the particulate matter pollution in the air and can be harmful to environment.
A diesel particulate filter (DPF) refers to a device designed to remove diesel particulate matter (PM) or soot from the exhaust gas of the diesel engine. The DPF can collect soot/ PM present in the exhaust, and the soot/PM has to be emptied regularly to maintain the performance of the engine. The DPF can determine the amount of soot/PM to be accumulated based on a differential pressure across the DPF, wherein the amount of soot/PMs being emitted may be obtained from the operating state of the internal combustion engine. Currently, the abnormalities/malfunctioning of the DPF can be determined based on large mismatch occurring between the DPF and the PM emissions.
If the internal combustion engine is in a state in which the amount of PM emissions is small, any change in the differential pressure across the DPF is small, and an accurate PM accumulation amount could not be obtained.
In conventional methods, exhaust after treatment system can be equipped with a PM sensor which can provide a soot mass value and can detect the DPF failure if the soot mass value exceeds the emission limit.
FIG. 1A illustrates an block diagram, wherein the exhaust after treatment layout with PM sensor is attached. As illustrated in FIG. 1A, the DPF can be configured with the PM sensor, an exhaust gas treatment (EGS) PM sensor, a diesel oxidation catalyst (DOC) and an engine control unit (ECU). The EGSPM sensor can monitor the DPF function to track the current emission limits and on-board diagnostics regulations. The ECU controls DPF monitoring using PM sensor depending on the engine operating zones, soot mass and monitoring releasing conditions.
FIG. 1B is a block diagram, illustrating the flow of the DPF with a normal sample and an end cut sample. As illustrated in FIG. 1B, there is an increase of flow area between the normal sample and the end cut sample. As illustrated, there is a geometrical increase in the geometric flow area between the normal sample and the end cut sample. Thus, resulting in increased PM value for the end cut sample.
FIG. 1C is a graphical representation, illustrating change in flow area with respect to delta pressure with end cut sample and normal sample. As illustrated in FIG. 1C, drop in delta pressure with 25mbar with approximately 26% increase in the flow area.
OBJECTS
The principal object of embodiments herein is to disclose methods and systems for monitoring and detecting failure(s) in the diesel particulate filter (DPF), while the engine emission exceeds over on-board diagnostics (OBD) limits, wherein a faulty DPF is detected using a delta pressure sensor.
These and other objects of embodiments herein will be better appreciated and understood when considered in conjunction with following description and accompanying drawings. It should be understood, however, that the following descriptions, while indicating embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF DRAWINGS
The embodiments are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:
FIG. 1A illustrates a block diagram, wherein the exhaust after treatment layout with PM sensor attached, according to prior arts;
FIG. 1B is a block diagram, illustrating flow of a DPF with a normal sample and an end cut sample, according to the prior arts;
FIG. 1C is a graphical representation, illustrating flow area of the delta pressure with end cut sample and normal sample, according to the prior arts;
FIGs. 2A, 2B and 2C are block diagrams, illustrating the thermodynamic behavior of the DPF, according to an embodiment as disclosed herein;
FIG. 3 depicts a detection methodology, wherein approaches for performing detection of robustness and improved monitor frequency of the engine, according to the embodiments as disclosed herein;
FIG. 4A and 4B are diagrams, illustrating the logic of DPF monitoring using delta pressure sensor, according to the embodiment as disclosed herein;
FIG. 5 is a diagram, illustrating a logic flow for detection of failure using integration logic, according to the embodiment as disclosed herein;
FIGs. 6A and 6B are diagrams, illustrating a validation of DPF monitoring by integration logic on a fleet vehicle drive cycle, according to the embodiment as disclosed herein;
FIG. 7 is an example diagram, illustrating the validation and overall summary on fleet vehicle data by monitoring DPF, according to the embodiment as disclosed herein;
FIG. 8E is an example diagram, illustrating a drive cycle wise comparison, according to the embodiment as disclosed herein;
FIGs. 9A and 9B, are example diagrams, illustrating an integrated Dp for entire drive cycle in NEDC and HW, according to the embodiment as disclosed herein;
FIG. 10 is an example diagram, illustrating an effect of exhaust temperature, according to the embodiment as disclosed herein;
FIGs. 11A, 11B, 11C, 11D and 11E are example diagrams, illustrating an integrated Dp approach for individual cycles, according to the embodiment as disclosed herein;
FIG. 12 is an example diagram, illustrating a logic flow using an empirical formula, according to the embodiment as disclosed herein; Empirical formula = [(Corrected Dp^3)/ (exhaust flow rate)], where corrected Dp is modelled delta pressure with exhaust gas temperature and soot mass corrections.
FIG. 13 is an example diagram, illustrating, overall detection of failure occurred on the engine, according to the embodiment as disclosed herein;
FIGs. 14A, 14B, 14C and 14D are example diagram, illustrating detection in different cycles, according to the embodiments as disclosed herein; and
FIG. 15 is a flow diagram illustrating a method for detecting faulty diesel particulate filter using DPF delta pressure sensor, according to the embodiments as disclosed herein.
DETAILED DESCRIPTION
The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The embodiments herein disclose methods and systems for monitoring and detecting failure(s) in diesel particulate filter (DPF), while the engine emission exceeds over on-board diagnostics (OBD) limits, wherein a faulty DPF is detected using a delta pressure sensor. Referring now to the drawings, and more particularly to FIGS. 2 through 15, where similar reference characters denote corresponding features consistently throughout the figures, there are shown embodiments.
FIGs. 2A, 2B and 2C are diagrams, illustrating the thermodynamic behavior of a DPF brick, according to embodiments as disclosed herein. Delta pressure may refer to the pressure difference across DPF at different engine operating conditions. As illustrated in FIG. 2A, the thermodynamic behavior of the DPF brick has been displayed without any particulates/soot, wherein the empty DPF can be used to model differential pressure against exhaust flowrate. As illustrated, the behavior can be tracked using a map with differential pressure and exhaust flow rate with constant temperature and later applying temperature-based corrections.
FIG. 2B illustrates a thermodynamic behavior of DPF brick when the DPF brick is loaded. As illustrated, the behavior can be tracked using a map with differential pressure and exhaust flow rate. Later modelled delta pressure is corrected with temperature and Soot based factor.
FIG. 2C illustrates the effect of exhaust gas temperature on differential pressure across DPF at various iso-exhaust flow rates and constant soot mass. It was observed that, the exhaust gas temperature and flow rate have direct effect of differential pressure.
The thermodynamic behavior of the DPF can be obtained based on the general gas flow equation. The differential pressure across DPF is a function of exhaust gas flowrate, exhaust gas temperature, and flow path geometry. The relation between exhaust flowrate, exhaust gas temperature and delta pressure are derived in the form of best fit curve. Hence, in an embodiment herein, based on the above dependent thermodynamic parameters, a best fit curve can be used for modelling.
FIG. 3 depicts a method for detecting faults in the DPF (104), according to embodiments as disclosed herein. As depicted in FIG. 3, the detection methodology can be performed by two approaches running parallelly. In an embodiment herein, the detection can be performed at the end of drive cycle. The first approach can be based on the empirical formula dP3/m. The second approach can be based on comparison of actual and modelled integrated dP signal under suitable monitoring conditions. The DPF can be confirmed as damaged only if both the approaches determine that the DPF is damaged. This ensures robust detection (which can prevent erroneous detections), along with providing improved monitoring frequency.
FIG. 4A and 4B are diagrams, illustrating the delta pressure sensor monitoring the DPF, according to an embodiment as disclosed herein. As illustrated, differential pressure can be measured using a delta pressure sensor across the DPF, which can be provided to a monitoring unit. The monitoring unit can monitor parameters, such as, but not limited to, speed of the engine (using an engine speed unit), fueling (measured using a fuel check unit) exhaust flow rate, and so on.
As illustrated in FIG. 4A, a delta pressure sensor 102 can be configured to DPF 104, which can be connected to a monitoring unit 106. The monitoring unit 106 can be configured to take input from various monitor parameters such as speed of the engine, fueling and exhaust flow rate. The monitoring unit 106 can be configured with an integrator 108 and a normalizer 110 which can be used to determine the quality of the DPF.
As illustrated in FIG. 4B, based on the monitored data, the monitoring unit can determine whether one or more of the monitoring release conditions have been met. These conditions include steady state engine running condition at higher exhaust flow rates, normal engine operating mode (DPF regeneration inactive), soot mass in the desired range, vehicle speed above minimum speed threshold (more conditions can be added based on requirement). The monitoring unit can determine the modelled Dp as a function of exhaust flow rate. In an embodiment herein, the monitoring unit can determine the temperature and soot mass corrections, which can be applied to the modelled Dp. The monitoring unit can integrate the modelled Dp and the actual Dp. The monitoring unit can calculate a normalized function based on the empirical formula (dp^3/exhaust flow rate) for the modelled Dp and the actual Dp. The modelled Dp and the actual Dp can identify the ratio of the actual and modelled Dp to determine whether the modelled Dp is less than a pre-defined threshold. The monitoring unit on identifying that the ratio is greater or less than the threshold, indicates that the DPF is in good condition.
The monitoring unit can calculate a normalized function based on the empirical formula (dp^3/exhaust flow rate) for the modelled and the actual Dp. The monitoring unit can compare if the normalized actual Dp is less than the normalized modelled Dp. The monitoring unit on determining that the normalized actual Dp is greater than the normalized modelled Dp, indicates that the DPF is in good condition. If the normalized actual Dp is not greater than the normalized modelled Dp, the monitoring unit can be combined with the output of the integrator using AND operator. The processing unit on determining that the output of both system integrator and normalizer can be analyzed using the AND operator, can determine that the DPF is in damaged condition.
In another embodiment, depicting the approach of monitoring the DPF using an integration approach. The modeled Dp for empty filter can be at 0.3869 * exhaust mass flow rate – 8.9297 (approximated here as linear function for a given DPF sample). The modeled Dp for loaded filter can be at 1.0592 * exhaust mass flow rate + 20.3 (approximated here as linear function for a given DPF sample). The modeled Dp due to soot of 20 grams can be the difference between the modeled loaded filter Dp and the modeled empty filter Dp. In an embodiment, the differential pressures across empty and fully loaded DPF with linear approximation.
In an embodiment herein, to define the steady state of engine operation, the exhaust flow rate was monitored. It was observed that, better demarcation is possible between damaged and good DPF at higher exhaust flowrates. Hence, if exhaust flowrate is greater than a pre-defined threshold for a pre-defined time period, then the modeled Dp and actual Dp are integrated. The integrated Dp value of this time period can be stored and the average at the end of drive cycle can be considered.
In an example herein, if the exhaust flow rate is greater than 40 kg/h continuously for 50s, then the modeled Dp and the actual Dp are integrated for the points where exhaust flow greater than 40 kg/hr for 100 s. The integrated Dp value for 100s can be stored and the average can be considered at the end of drive cycle. Exhaust flowrate thresholds and time spans for monitoring were selected based on the experimental observations for the particular vehicle.
At the end of the drive cycle, the final ratio of averaged actual integrated dp and averaged modeled integrated dp is calculated. Final_Ratio = "Act_dp_avg" /"Mod_dp_avg"
Based on the first observation, it is noted that, the Final_ratio less than 0.55 for end cut and greater than 0.55 for intact DPF which implies demarcation. Also in the second observation, at higher exhaust flowrates, even better demarcation was noted. However, threshold for minimum exhaust flowrate for a particular vehicle to be decided considering the tradeoff between both the robust detection and monitoring frequency.
FIG. 5 is an example diagram, illustrating the process of detecting failure in the DPF using integration logic, according to embodiment as disclosed herein. As illustrated in the FIG. 5, on determining that the exhaust flowrate is greater than 40 kg/hr for continuously 50 seconds, can be integrated with the modeled and actual Dp for 100 seconds, else it can be considered at next time stamp. Further, it can obtain average of integrated modeled and actual Dp. Thus, detection ratio can be determined by average of integrated actual Dp/average of integrated modeled Dp. Also, if the detected ratio lower than threshold it can be determined that the DPF is damaged.
FIGs. 6A and 6B are example diagrams, illustrating a validation of DPF monitoring by integration logic on a fleet vehicle drive cycle, according to embodiment as disclosed herein. As illustrated in FIG. 6A, the validation of DPF monitoring by integration logic on fleet vehicle drives cycle can be tracked using a map with exhaust flow rate and detection ratio, wherein the exhaust flow rate is greater than 40 kg/h.
FIG. 6B illustrates that validation of DPF monitoring by integration logic on fleet vehicle drives cycle can be tracked using a map with exhaust flow rate and detection ratio, wherein the exhaust flow rate is greater than 90 kg/h. Therefore, monitoring at a higher exhaust flow rate shows larger demarcation between good and damaged sample compared to lower exhaust flow rates. But the number of points in the cluster will be low which means possibility of late detection.
FIG. 7 is an example diagram, illustrating the validation and overall summary on fleet vehicle data by monitoring DPF, according to embodiment as disclosed herein. As illustrated in FIG. 7, DPF monitoring in fleet vehicles can be tracked using final ratio and valid track cycles. In another embodiment, based on the observation, that the total number of recordings were 524, out of which the total number of valid drive cycles for DPF detection approach were 232. Damaged DPF can be detected based on integration approach and by average Dp approach, however in few exceptional cases intact DPF detected was damaged mainly for the drive cycles during which DPF regeneration was active.
FIG. 8E is an example diagram, illustrating a drive cycle wise comparison, according to embodiment as disclosed herein. FIG. 8E illustrates the drive wise cycle in terms of RDE cycle with GVW condition using detection ratio and drive cycles, in which intact RDE GVW and end cut RDE GVW can be obtained. Wide separation between good and damaged DPF sample was observed with integration approach. Similar results were verified with variety of drive-patterns (city, highway) and drive-cycles (NEDC, WLTP).
FIGs. 9A and 9B, are example diagrams, illustrating an integrated Dp for entire drive cycle in NEDC, according to embodiment as disclosed herein. FIG. 9A illustrates an integrated Dp for entire drive cycle in terms of NEDC with GVW condition using integrated Dp of exhaust flowrate greater than 90 kg/hr and time. Mod Dp intact GVW NEDC, Act Dp intact GVW NEDC, mod Dp end cut GVW NEDC and act Dp end cut GVW NEDC can be obtained in a graph as illustrated.
FIG. 9B illustrates an integrated Dp for entire drive cycle in terms of NEDC using integrated Dp of exhaust flowrate greater than 90 kg/hr and time. Mod Dp intact 1810 NEDC and Act Dp intact 1810 NEDC can be obtained for 1810 kg inertia condition with NEDC in a graph as illustrated. In another embodiment, similar to NEDC, integration approach was validated for variety of drive patterns (city, highway) and variety of drive cycles (WLTP, RDE).
Therefore, modelled and actual Dp for intact DPF sample are following similar trend and are having close values. This, validates the equation of modelled Dp based on exhaust flow rate, and soot mass content in the brick. For end cut sample, the separation for modelled and actual Dp is higher. Hence, the ratio of actual of modelled integrated Dp can be used to monitor DPF damage.
FIG. 10 is an example diagram, illustrating an effect of exhaust temperature, according to embodiment as disclosed herein. As illustrated in FIG. 10, effect of exhaust temperature in DPF in which corrected temp effect with monitoring enabled for exhaust flowrate greater than 40 kg/hr has been mentioned. As illustrated, inclusion of temperature parameter is not useful to increase demarcation since it is not the brick temperature but DPF upstream temperature. However, it can be still considered (with the use of calibration label for activation) in the current logic. With the availability of accurate temperature value inside brick, temperature factor can be considered.
FIGs. 11A, 11B, 11C, 11D and 11E are example diagrams, illustrating an integrated Dp approach for individual cycles, according to embodiment as disclosed herein. FIG. 11A is an example diagram, illustrating the graph of the parameter WLTP with monitoring enabled for exhaust flowrate greater than 45 kg/hr wherein the following parameters are indicated in the graph of Exhaust flowrate greater than 45 kg/hr represented in the x-axis and detection ratio represented in the y-axis to obtain end cut WLTP EGR, end cut WLTP w/o EGR, Intact WLTP.
FIG. 11B is an example diagram, illustrating the graph of the parameter HW_Exh greater than 45_Msotmeasbas wherein the following parameters are indicated in the graph of exhaust flowrate greater than 45 kg/hr represented in the x-axis and detection ratio represented in the y-axis to obtain end cut HW, intact HW.
FIG. 11C is an example diagram, illustrating the graph of the parameter RDE Exhaust greater than 45_Msotmeasbas wherein the following parameters are indicated in the graph of exhaust flowrate greater than 45 kg/hr represented in the x-axis and detection ratio represented in the y-axis to obtain end cut, intact.
FIG. 11D is an example diagram, illustrating the graph of the parameter City Exhaust greater than 45_Msotmeasbas wherein the following parameters are indicated in the graph of Exhaust flowrate greater than 45 represented in the X-axis and Detection Ratio represented in the Y-axis to obtain end cut and Intact.
FIG. 11E is an example diagram, illustrating the graph of the parameter NEDC_Exhaust greater than 45_Msotmeasbas wherein the following parameters are indicated in the graph of Exhaust floware greater than 45 kg/hr represented in the x-axis and Detection Ratio represented in the y-axis to obtain Endcut_NEDC_w/o_EGR, Endcut_NEDC_EGR, Intact_NEDC.
FIG. 12 is an example diagram, illustrating a logic flow using an empirical formula, according to embodiment as disclosed herein. As illustrated in FIG. 12, the logical flow using empirical formula (Corrected dP^3)/(exhaust flow rate) derived based on the experimental approximations and thermodynamic behavior can be obtained, in which it is determined that if the exhaust flow is greater than 40 kg/h, also it is determined that if the flow is above 40kg/h for greater than 50 secs and less than 1000 secs. Also, on determining the false condition can proceed to the next time scale. Then can calculate average factor and average mass flow rate for the specified time period.
FIG. 13 is an example diagram, illustrating, failure detection based on normalized Dp approach occurred on the engine, according to embodiment as disclosed herein. As illustrated in FIG. 13, the graph of parameters factor as x-axis and exhaust mass flow (kg/h) as y-axis to determine the overall failures in the engine. As illustrated in FIG. 13, the highlighted portion close to the x-axis defines the failure detection of the engine, which can be plotted for a RDE cycle.
RDE cycle can be plotted for failure detection of engine using factor as x-axis and average flow rate (kg/h) as y-axis. A curve is obtained in which the plots below the curve indicate the failure of engine, while the plots above the curve are analyzed to be in normal condition.
FIGs. 14A, 14B, 14C and 14D are example diagram, illustrating detection in different cycles, according to embodiments as disclosed herein. FIG. 14A illustrates the detection of failure in different cycles in NEDC plotted in the map of x-axis average flow rate (kg/h) and y-axis factor. Similarly, FIG. 14B, FIG. 14C and FIG. 14D are plotted at different cycles such as RDE, Highway and WLTP respectively.
Hence, calculations for modelled values and threshold curves can be performed offline, which can be calibrated in a look up table. Further the table can be used based on exhaust flow rate, the values for look-up tables can be dependent on engine and vehicle applications. The calculations can be explained in brief such as:
Mod dp empty = Modelled Dp for empty filter = f (exhaust flow rate)
Mod Dp loaded = Modelled Dp for loaded filter = f (exhaust flow rate)
ddp = Mod dp loaded – Mod dp empty
moddp soot = Modelled dp for current soot load
Corrected dp = Corrected dp after soot load-based and temperature-based correction
Dp param act = calculated from corrected Dp and exhaust flow rate
Dp param thresh = threshold curve based on exhaust flow rate
FIG. 15 is a flow diagram illustrating a method for detecting faulty diesel particulate filter using DPF delta pressure sensor, according to embodiments as disclosed herein.
FIG. 15 is a flow diagram illustrating a method 1700 for detecting faulty diesel particulate filter using DPF delta pressure sensor. At step 1702, the method includes analyzing, by a delta pressure sensor, at least one differential pressure corresponding to the DPF.
At step 1704, the method includes determining, by a monitoring unit, at least one parameter received satisfies a release condition, wherein the at least one parameter includes at least one differential pressure, an engine speed, a fueling and an exhaust flow rate;
At step 1706, the method includes receiving, by a monitoring unit (106), at least one modelled diesel particulate as a function of the exhaust flow rate with a temperature and soot mass correction, wherein an integrator integrates at least one modelled differential pressure and an actual differential pressure sensed by delta pressure sensor.
At step 1708, the method includes determining, by the monitoring unit (106), at least one normalized function for modelled and actual diesel particulate and detecting, by the monitoring unit (106), a failure in the DPF by checking if the normalized actual Dp is less than a normalized modelled Dp,.
The various actions, acts, blocks, steps, or the like in the method and the flow diagram 1700 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, modified, skipped, or the like without departing from the scope of the invention.
The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in FIG. 1 include blocks, which can be at least one of a hardware device, or a combination of hardware device and software module.
The embodiments disclosed herein describe methods and systems for detecting faulty diesel particulate filter using DPF delta pressure sensor in an emission engine. Therefore, it is understood that the scope of the protection is extended to such a program and in addition to a computer readable means having a message therein, such computer readable storage means contain program code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device.
The method is implemented in at least one embodiment through or together with a software program written in e.g. Very high-speed integrated circuit Hardware Description Language (VHDL) another programming language or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof, e.g. one processor and two FPGAs.
The device may also include means which could be e.g. hardware means like e.g. an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means are at least one hardware means and/or at least one software means. The method embodiments described herein could be implemented in pure hardware or partly in hardware and partly in software. The device may also include only software means. Alternatively, the invention may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modifications within the spirit and scope of the embodiments as described herein.
, Claims:1. A method for detecting a failure in a Diesel Particulate Filter (DPF) (104), the method comprising:
analyzing, by a delta pressure sensor (102), at least one differential pressure corresponding to the DPF (104);
determining, by a monitoring unit (106), at least one parameter received satisfies a release condition, wherein the parameter includes at least one differential pressure, an engine speed, a fueling and an exhaust flow rate;
receiving, by the monitoring unit (106), at least one modelled diesel particulate as a function of the exhaust flow rate with a temperature and soot mass correction, wherein an integrator (108) integrates at least one modelled diesel particulate and an actual diesel particulate;
determining, by the monitoring unit (106), at least one normalized function for modelled and actual diesel particulates; and
detecting, by the monitoring unit (106), a failure in the DPF (104) by checking if the normalized actual diesel particulate is less than a normalized modelled diesel particulate.

2. The method as claimed in claim 1, wherein the monitoring unit (106) on determining that a ratio of actual diesel particulate and modelled diesel particulate is less than a threshold value, combines the result of compared normalized actual diesel particulate.

3. The method as claimed in claim 1, wherein an AND operator combine the result on the integrator (108) and the normalizer (110) to detect the damage (malfunction) at the DPF (104).
4. The method as claimed in claim 1, wherein the monitoring unit (106) on determining that the ratio of actual diesel particulate and modelled diesel particulate is not less than the threshold value, indicates that the at least one DPF (104) is in good condition (working condition).
5. The method as claimed in claim 1, wherein the monitoring unit (106) on determining that the normalized actual diesel particulate is not less than normalized modelled diesel particulate, indicates that the at least one DPF is in good condition (working condition).
6. A system (100) for detecting a failure in a Diesel Particulate Filter (DPF) (104), the system (100) comprising:
a delta pressure sensor (102);
a monitoring unit (106);
wherein the delta pressure sensor (102) is configured to analyze at least one differential pressure corresponding to the DPF (104);
the monitoring unit (106) is configured to determine the at least one parameter received satisfies a release condition, wherein the at least one parameter includes at least one differential pressure, an engine speed, a fueling and an exhaust flow rate;
the monitoring unit (106) is configured to receive at least one modelled diesel particulate as a function of the exhaust flow rate with a temperature and soot mass correction, wherein an integrator integrates at least one modelled diesel particulate and an actual diesel particulate; and
the monitoring unit (106) is configured to determine at least one normalized function for modelled and actual diesel particulate and
the monitoring unit (106) detects a failure in the DPF (104) by checking if the normalized actual diesel particulate is less than normalized modelled diesel particulate.

7 The system (100) as claimed in claim 6, wherein the monitoring unit (106) on determining that a ratio of actual diesel particulate and modelled diesel particulate is less than a threshold value, combines the result of compared normalized actual diesel particulate.

8. The system (100) as claimed in claim 6, wherein an AND operator combine the result on the integrator (108) and the normalizer (110) to detect the damage (malfunction) at the DPF (104).
9. The system (100) as claimed in claim 6, wherein the monitoring unit (106) on determining that the ratio of actual diesel particulate and modelled diesel particulate is not less than the threshold value, indicates that the at least one DPF is in good condition.
10. The system (100) as claimed in claim 6, wherein the monitoring unit (106) on determining that the normalized actual diesel particulate is not less than normalized modelled diesel particulate, indicates that the at least one DPF (104) is in good condition (working condition).