DESC:FIELD
The present disclosure relates to means for fault detection in rolling element-type bearings.
DEFINITION
As used in the present disclosure, the following term is generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicate otherwise.
The expression ‘structural frequency’ used hereinafter in this specification refers to, but is not limited to, a frequency of forced vibration of a structure at which the structure undergoes vibration of very high amplitude. The structural frequency is also termed as ‘natural frequency’ or ‘resonance frequency’ of the structure.
The expression ‘base function’ used hereinafter in this specification refers to, but is not limited to, a mathematical model for an impulse modelled as a decaying sine wave of a particular frequency '?' as represented in the following equation.
b(t)=e^(-s?t)*sin?(?t)
The expression ‘kernel function’ used hereinafter in this specification refers to, but is not limited to, an adaptive kernel function/mother wavelet function ?(t) obtained by using the base function b(t) by addition of time-shifted versions of base functions b(t), where n and Tfault can be selected or adapted depending upon type of fault present in the vibration signal.
?(t)=b(t)+b(t-T_fault )+b(t-2T_fault )+?+b(t-nT_fault )
The expression ‘continuous wavelet transform’ used hereinafter in this specification refers to, but is not limited to, a wavelet transform which breaks up a continuous function f(t) into shifted and scaled versions of the mother wavelet function ?(t). It can be defined as the convolution of the input data sequence with a set of functions generated by the mother wavelet ?(t).Where ‘a’ represents scale and ‘b’ represents time shift of mother wavelet function ?(t) and ?*(t) is the complex conjugate of the mother wavelet function ?(t).
In CWT, if shape and time difference between two mother wavelet matches or correlates at a specific scale and location with the shape and time difference between two burst created when fault occurs in rolling element-type bearing, we get large transform value otherwise we get low transform value using which we can find out type of fault present in the signal.
cw(a,b)=1/va ?_(-8)^8¦?f(t).?^* ?((t-b)/a)dt
BACKGROUND
The background information herein below relates to the present disclosure but is not necessarily prior art.
In many complex rotating types of machinery major failure occurs due to localized faults in rolling element-type bearing. Failure of the bearing may cause an unplanned shut down time of machine in production industry leading to economic losses also in some cases catastrophic failures of machine can cause human injuries. To prevent such losses machine health monitoring and fault diagnosis of rolling element-type bearing has been the subject of intensive research. Vibration signal analysis is the state-of-the-art technology to detect the localized defects presence in parts of bearing since vibration signals contain important information about the fault development and can reveal the health of a bearing. A number of vibration analysis techniques are being used for diagnosis of rolling element-type bearings faults. These methods are generally classified in two main categories: time-domain analysis and frequency-domain.
Time domain analysis identifies the fault in a bearing by analysing the changes in statistical properties of vibration signal. Statistical properties like amplitude, root mean square (RMS), and kurtosis are used to detect faults likewise. Change in noise pattern of vibration signal is used to detect fault in a roller bearing. Similarly an inverted sawtooth waveform and changes in its envelope are used to detect the fault.
Frequency-domain analysis covert the time-domain vibration signal into frequency-domain and then identify the fault present in a rolling bearing by analysis of frequency components present in the vibration signal. Bearing envelope power spectrum-based analysis is used to detect the fault in a rolling element-type bearing. Similarly, a technique in which characteristics of the occurrence of damage to bearing is determined by envelope curve demodulation signal (ECD signal) is also known.
Advanced pattern detection and classification methods like, artificial neural networks (ANN) and fuzzy classification are also used in which features extracted from vibration signal by time- and frequency-domain methods are given to train the network which then used to identify and classify the fault present in rolling element-type bearing.
Time-domain analysis detects the fault in the rolling element-type bearing by analyzing variations in statistical properties of a vibration signal. In frequency-domain analysis, the time-domain vibration signal is converted into a frequency-domain vibration signal and then the faults, if any, present in the rolling element-type bearing are detected by analysing the frequency component of the vibration signal.
However, both, the time-domain analysis and the frequency-domain analysis are ineffective to process non-stationary vibration signals. Further, the time-domain analysis and frequency-domain analysis also fail to detect faults, if the vibration signal contains noise from the surrounding environment or if fault severity is very low, and do not provide for detecting multiple faults such as inner race fault and outer race fault if present simultaneously in a vibration signal.
Alternatively, a time-frequency analysis technique like wavelet transform is employed to detect faults in the rolling element-type bearings. The time-frequency analysis determines particular frequency components in the vibration signal at a particular time. The main drawback of wavelet transform method is that an analyzing mother wavelet cannot be changed once it is fixed due to which this method does not provide for detecting multiple faults such as inner race fault and outer race fault if present simultaneously in vibration signal. In addition, various training techniques are used for training and improving the performance of conventional fault detection systems. However, even these training techniques fail to effectively detect faults, if the dimensions of rolling element-type bearing or machine operating conditions are changed. Advanced pattern detection techniques cannot be generalized because they required huge amount of data to train the network and once the network is trained for particular bearing and machine operating parameters similar model is not usable if bearing or machine operating conditions are changed.
Therefore, there is felt a need for a system for detecting faults in rolling element-type bearings, that detects multiple faults and alleviates the aforementioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment satisfies, are as follows:
An object of the present disclosure is to provide a system for detecting faults in rolling element-type bearings.
Another object of the present disclosure is to provide a system for detecting faults, which facilitates detection of multiple types of faults in the rolling element-type bearings.
Still another object of the present disclosure is to provide a system for detecting faults, which facilitates detection of a fault with less severity.
Yet another object of the present disclosure is to provide a system for detecting faults, which is independent of change in dimensions of rolling element-type bearings and in machine operating conditions.
Yet another object of the present disclosure is to provide a system for detecting faults, which does not require large amount of data.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a system for detecting fault in a rolling element-type bearing. The system comprises a vibration sensor, an input unit and an analysis unit. The vibration sensor is configured to measure vibrations of the bearing being tested and to generate a vibration signal. The input unit is configured to receive inputs from the user. The inputs include parameters associated with the bearing, sampling frequency of the vibration sensor and rate of rotation of the rotating shaft of the bearing. The analysis unit is configured to determine if at least one fault is present in a component of the bearing from a list of components to be analysed for, using a continuous wavelet transform with an adaptive kernel. The parameters include pitch diameter, roller diameter and the number of rollers in the bearing. The list of components includes the inner race, the outer race, the rollers and the cage of the bearing.
In an embodiment, the analysis unit includes a calculation module, a preprocessing module, a base function generator, a kernel function generator, a wavelet transform module and a determination module. The calculation module is configured to at least calculate a fault frequency for the element of the bearing chosen for detecting fault, based on the parameters. The preprocessing module is configured to perform fast Fourier transform (FFT) of the vibration signal and extract structural frequencies from the vibration signal. In another embodiment, the structural frequencies are obtained by displaying the FFT on screen and manually selecting. The base function generator is configured to generate a base function using structural frequencies obtained from the preprocessing module. The kernel function generator is configured to generate an adaptive kernel function using the base function obtained from the base function generator and the fault frequency obtained from the calculation module. The wavelet transform module is configured to perform continuous wavelet transform of the vibration signal using the adaptive kernel function obtained from the kernel function generator. The determination module is configured to determine if a fault is present in a component from the list of components by comparing a time difference calculated between at least first two largest consecutive coefficients of the continuous wavelet transform by the calculation module with the period corresponding to the fault frequency.
In an embodiment, the system includes a signal conditioning unit for removing low-frequency noise in the vibration signal.
In another embodiment, the system includes a display unit configured to display at least the wavelet transform. In an embodiment, the display unit is configured to display whether or not a fault is present in a component from the list of components.
The present disclosure also envisages a method for diagnosing fault in a rolling element-type bearing. The method comprises:
sensing vibrations of the bearing being tested using a vibration sensor and to generate a vibration signal;
receiving inputs from the user through an input unit, the inputs include parameters associated with the bearing including pitch diameter, roller diameter and the number of rollers in the bearing, sampling frequency of the vibration sensor and rate of rotation of the rotating shaft of the bearing ;
performing fast Fourier transform (FFT) of the vibration signal;
extracting structural frequencies from said FFT;
generating a base function using the structural frequencies using a base function generator;
calculating a fault frequency for a component selected from the inner race, the outer race, the rollers and the cage of the bearing, based on the parameters, using a calculation module;
generating an adaptive kernel function using the base function and the fault frequency using a kernel function generator;
performing continuous wavelet transform of the vibration signal using the adaptive kernel function using a wavelet transform module;
calculating time difference between at least first two largest consecutive coefficients of the continuous wavelet transform using the calculation module; and
determining if a fault is present in a component by comparing the time difference with the period corresponding to the fault frequency.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWING
A system for detecting faults in rolling element-type bearings, of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a block diagram of the system for detecting faults in rolling element-type bearings, in accordance with an embodiment of the present disclosure;
Figure 2 illustrates a flow chart for a method for detecting faults in rolling element-type bearings, in accordance with an embodiment of the present disclosure;
Figure 3 illustrates a signal representing multiple faults in a rolling element-type bearing;
Figure 4 illustrates a fast Fourier transform of the signal of Figure 3;
Figure 5 illustrates a scalogram of a continuous wavelet transform plotted between the interval 0.5 to 0.7 of the signal of Figure 3 using Touter as the time shift;
Figure 6 illustrates a plot of a kernel function of a wavelet using Touter as the time shift;
Figure 7 a scalogram of a continuous wavelet transform of the signal of Figure 3 plotted using Tr3 as the time shift; and
Figure 8 illustrates a plot of a kernel function of a wavelet using Tr3 as the time shift.
LIST OF REFERENCE NUMERALS
100 System
102 Vibration Sensor
104 Signal Conditioning Unit
106 Input Unit
108 Repository
109 Calculation Module
110 Analysis Unit
112 Preprocessing Module
114 Base Function Generator
116 Kernel Function Generator
118 Continuous Wavelet Transform (CWT) Module
120 Determination Module
122 Display Unit
DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms “a”, “an” and “the” may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms “comprises”, “comprising”, “including” and “having” are open-ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
When an element is referred to as being “mounted on”, “engaged to”, “connected to” or ‘coupled to” another element, it may be directly on, engaged, connected or coupled to the other element. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed elements.
The terms first, second, third, etc., should not be construed to limit the scope of the present disclosure as the aforementioned terms may be only used to distinguish one element, component, region, layer or section from another component, region, layer or section. Terms such as first, second, third etc., when used herein do not imply a specific sequence or order unless clearly suggested by the present disclosure.
Terms such as “inner”, “outer”, “beneath”, “below”, “lower”, “above”, “upper” and the like, may be used in the present disclosure to describe relationships between different elements as depicted from the figures.
Rolling element-type bearings are critical components in most of the rotary machines. Problems in these machines may be mainly caused by localized faults in rolling element-type bearings. When localized defects occur in a rolling element-type bearing, an impact impulse response signal or burst signal is generated. These impact impulses are periodic and shape of that burst signal and time duration of occurrence is changed according to the basic morphology or the geometry of the bearing, rotation speed of the shaft and the type of fault Frequency spectrum of such an impact impulse response signal would consist of component fault frequency such as inner race or outer race fault frequency. Since these impulses are periodic we can detect the location of the defect using characteristic fault frequency formulae.
All the elements of a bearing have their characteristic fault frequency. Those fault frequencies can be calculated from the morphology of a bearing and the rotation speed of its shaft. By assuming the outer race stationary, fault frequencies for inner race fault (f_inner), outer race fault (f_outer), rolling element fault (f_roller), cage fault (f_cage) can be calculated by using equations (2)-(5) respectively.

f_r=Rpm/60 (1)
1/T_inner =f_inner=(nf_r)/2 {1+d/D cos?? } (2)
1/T_outer =f_outer=(nf_r)/2 {1-d/D cos?? } (3)
1/T_roller =f_roller=(Df_r)/d {1-(d/D cos?? )^2 } (4) 1/T_cage = f_cage=f_r/2 {1-d/D cos?? } (5)
where D is pitch diameter of bearing, n is number of rollers, d is the roller diameter, ? is the contact angle and f_r is the rotating frequency of input shaft.
Periodic impulses represent fault signatures in machinery. Mathematical model for an impulse modelled as a decaying sine wave is represented in the following Equation.
b(t)=e^(-s?t)*sin?(?t) (7)

Where s (s > 0) is decay (attenuation) constant which controls attenuation speed of the wavelet base function b(t) and can be selected appropriately according to the actual vibration system of rolling element-type bearing. Frequency ? is a structural damped natural frequency of a vibrating system which controls the oscillating period of a wavelet base function.
?(t)=b(t)+b(t-T_fault )+b(t-2T_fault )+?+b(t-nT_fault) (8)

By using the base function b(t) shown in Equation (7) an adaptive kernel function/mother wavelet function ?(t) is formed using Equation (8) by addition of time-shifted versions of base functions b(t), where n and Tfault can be selected or adapted depending upon type of fault present in the vibration signal, so the time shift Tfault can be Touter, Tinner or Troller as given in Equations (2) to (5).
cw(a,b)=1/va ?_(-8)^8¦?f(t) ?.??^* ? ((t-b)/a)dt (9)
In the above equation, a represents scale and b represents a time shift of the adaptive kernel function ?(t) and ?*(t) is the complex conjugate of the adaptive kernel function ?(t).
A preferred embodiment of a system 100 for detecting faults in rolling element-type bearings of the present disclosure, which implements the aforementioned theory, is now being described in detail with reference to the block diagram of Figure 1.
The system 100 is used for detecting faults in a rolling element-type bearing (hereinafter referred as “bearing”). The system 100 includes a vibration sensor 102, an analysis module 110 and an input unit 106. The system 100 also includes a signal conditioning unit 104. The input unit 106 is configured to accept parameters associated with the bearing being tested, from a human operator. In an embodiment, the parameters associated with the bearing, include, but are not limited to, sampling frequency, revolution per minute (RPM) of the rotating shaft of the bearing, pitch diameter of the bearing (D), roller diameter (d) of the bearing, and number of rollers (z) in the bearing. In another embodiment, the input unit 106 is a keyboard, and/or editable windows for providing inputs by the human operator using a hand-held input device such as a mouse or a stylus. A repository 108 is configured to receive and store the inputs of the user which include the parameters associated with the bearing being tested, received via the input unit 106. The repository 108 also stores a list of components of the bearing, including the inner race, the outer race, the rollers and the cage of the bearing.
In an embodiment, the vibration sensor 102 is configured to continuously measure vibrations of the bearing being tested, and is further configured to generate a vibration signal. A predetermined length of the vibration signal (either in terms of number of data points or in terms of time duration) is captured and saved in the repository 108. The sampling frequency and the frequency resolution desired are considered while capturing the vibration data. The vibration sensor 102 is configured to transmit the vibration signal to the signal conditioning unit 104. The vibration sensor 102 is an accelerometer. The accelerometer is mounted using suitable adhesive means or other mounting means on a surface of the bearing. Usually the accelerometer is mounted on an external surface, i.e. on the housing of the bearing to be tested. In an embodiment, the accelerometer is a three-axis accelerometer. In another embodiment, the accelerometer is a single-axis accelerometer. The accelerometer is calibrated before installation and the calibration value is stored in the repository 108. The signal conditioning unit 104 is configured to receive the vibration signal, and is further configured to convert the analog signal into a digital signal. In an embodiment, the signal conditioning unit 104 comprises an analog to digital convertor, which converts the received analog vibration signal into a digital signal based on the sampling frequency input by the user. The signal conditioning unit 104 is further configured to remove low frequency noise present in the vibration signal. In an embodiment, the signal conditioning unit 104 comprises a high pass filter. The high pass filter is selected from a group consisting of infinite impulse response (IIR) filters including Chebyshev filters, finite impulse response (FIR) filters,
The analysis unit 110 is configured to cooperate with the signal conditioning unit 104 and acquire data therefrom. In an embodiment, the analysis unit 110 is implemented as one or more microprocessors, microcomputers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. The analysis unit 110 includes a calculation module 109, a preprocessing unit 112, a base function unit 114, a kernel function unit 116, a continuous wavelet transform (CWT) unit 118 and a determination unit 120. The calculation module 109 is configured to compute shaft rotating frequency (fr), inner race fault frequency (finner), outer race fault frequency (fouter), roller fault frequency (froller) and cage fault frequency (fcage), using Equations (1)-(5), based on the received parameters associated with the bearing being tested. The calculation module 109 calculates T_fault, which is reciprocal value of the corresponding frequency (i.e. one of finner, fouter, froller or fcage ) for the component for which the analysis is currently carried out, from the list of components of the bearing. The preprocessing module 112 is configured to perform fast Fourier transform (FFT) of the vibration signal and extract structural frequencies from the vibration signal. The base function generator 114 is configured to generate a base function b(t) using structural frequencies obtained from the preprocessing module 112, as given by Equation 7. The kernel function generator 116 is configured to generate an adaptive kernel function ?(t) using the base function b(t) obtained from the base function generator 114, the Tfault obtained from the calculation module 110 and a number ‘n’ input by the user, as given by Equation 8. The Tfault is Tinner, Touter, Troller or Tcage based on the chosen element for detecting fault and ‘n’ is greater than or equal to 1. In this way, the system uses an adaptive kernel which makes the method independent of rolling element-type bearing dimensions as well as operating conditions. The wavelet transform module 118 is configured to perform continuous wavelet transform cw(a, b) of the vibration signal using the adaptive kernel function ?(t) obtained from the kernel function generator 116, as given by Equation 9. The determination module 120 is configured to determine if a fault is present in the chosen element by comparing a time difference between at least first two largest consecutive coefficients of the continuous wavelet transform calculated by the calculation module 112 with the period corresponding to the fault frequency, Tfault.
Fault at an outer race is detected by the determination module 120, if the time difference between at least first two largest consecutive coefficients of the continuous wavelet transform generated by the CWT module 118, Touter, matches with selected Tfault, i.e., Tfault = Touter. Fault at an inner race is detected by the determination module 120, if the time difference between at least first two largest consecutive coefficients of the continuous wavelet transform generated by the CWT module 118, Tinner, matches with selected Tfault, i.e. Tfault = Tinner. Different values of Tfault are used to detect other types of fault present in vibration signals, by choosing different elements for which faults are to be detected. In another embodiment, the continuous wavelet transform cw(a, b) is displayed in the form of a scalogram, via a display unit 122. The display unit 122 also displays which of the components of the bearing being tested has a fault.
Figure 2 illustrates a flow chart 200 for a method for detecting faults in bearings, comprising of following steps:
Step 202: sensing vibrations of the bearing being tested using a vibration sensor 102 and generate a vibration signal;
Step 204: receiving inputs from the user through an input unit 106, the inputs include parameters associated with the bearing including pitch diameter, roller diameter and the number of rollers in the bearing, sampling frequency of the vibration sensor 102 and rate of rotation, i.e., revolution per minute (RPM) of the rotating shaft of the bearing;
Step 206: performing fast Fourier transform (FFT) of the impact signal using the preprocessing module 112;
Step 208: extracting structural frequencies from the FFT;
Step 210: generating a base function b(t) using the structural frequencies using the base function generator 114;
Step 212: calculating a fault frequency for a component selected from the inner race, the outer race, the rollers and the cage of the bearing, based on the parameters, using the calculation module 109;
Step 214: generating an adaptive kernel ?(t) function using the base function and the fault frequency, using the kernel function generator 116;
Step 216: performing continuous wavelet transform cw(a, b) of the vibration signal using the adaptive kernel function, using the wavelet transform module 118;
Step 218: calculating time difference between at least first two largest consecutive coefficients of the continuous wavelet transform using the calculation module 109;
Step 220: determining if a fault is present in a component by comparing the time difference with the period corresponding to the fault frequency using the determination module 120.
In an illustrative example, a signal representing multiple faults (i.e., faults in inner and outer race) shown in Figure 3 is collected from a rolling element-type bearing at shaft rotating frequency fr =1/Tr = 13.33Hz (step 202). The sampling frequency is 50000Hz. Length of the sample data is 50000 data points, i.e., 1s.
Unlike in case of the outer race, when a fault signal in the inner race of the bearing is captured, the inner race is also rotating with the shaft. Due to this reason and due to unequal loading on the shaft, some of the rollers do not come in contact with the inner race when the shaft is rotating. Hence, some intermediate fault or burst signals are found to be missing. When inner race fault reaches at the bottom of the bearing block (or orients along the effective loading axis of the bearing) and at the same time if a roller (or a ball) is present there, the roller hits the inner race fault perfectly generating a burst signal which indicates the presence of an inner race fault at the shaft rotating frequency Fr in the vibration signal. So, instead of using Tinner as the time difference of occurrence of inner race fault, Tr = 1/Fr is used. The FFT shown in Figure 4 of a multi-fault signal shown in Figure 3 is performed by the preprocessing module 112 (step 206). The FFT of Figure 4 does not show presence of a fault frequency of either the inner or the outer race due to complexity of the signal and the noise present in the signal (step 208). At the given shaft rotating frequency and for the given bearing dimensions input using the input unit 106 (step 204), the outer race fault frequency fouter is equal to 65.71321039Hz and the inner race fault frequency finner is equal to 94.28678961Hz calculated from Equations (2) and (3) respectively by the calculation module 109 (step 212). Therefore, the time differences of occurrence of outer race fault and inner race fault are Touter = 0.01521764s and Tinner = 0.01060594s respectively. The base function is generated using the structural frequencies and s = 0.08 by the base function generator 114 (step 210). The kernel function of a wavelet using Touter as the time shift shown in Figure 6 and kernel function of a wavelet using Tr3 as the time shift shown in Figure 8 are developed by the kernel function generator 116 by using Equation (8) in which value n=3, Tfault = Touter and Tfault = Tr (obtained in step 212) are used to detect faults in the outer and the inner race respectively (step 214). A CWT scalogram computed using Touter as the time shift ‘b’ in the mother wavelet is shown in Figure 5 for time 0.5 to 0.7s and scale ‘a’ = 6 to 8 (step 216). The scalogram of Figure 5 clearly shows CWT coefficients from scale 6 to 8 separated by a time difference Touter (step 218) indicating presence of fault in the outer race (step 220). Similarly when Tr is used as a time shift ‘b’ in the mother wavelet to compute the CWT shown in Figure 7, the scalogram shows higher value wavelet coefficients predominantly from scale 3 to 9 indicating presence of a fault in the inner race of the bearing.
The system 100 does not require creation of a signal database for the purpose of analysis. Only capturing the data just before the fault detection is carried out is sufficient. Moreover, the system does not require any training by using a large signal database or by continuous acquisition of data for a long period of time.
The foregoing description of the embodiments has been provided for purposes of illustration and not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but, are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a system for detecting faults in rolling element-type bearings and a method thereof, which:
facilitates detection of single as well as multiple types of faults in bearings;
facilitates detection of a fault even if the fault is less severe;
requires a single vibration sensor for analysis;
is independent of dimensions of the rolling element-type bearing and machine operating conditions; and
does not require large amount of data for training the system.
The foregoing disclosure has been described with reference to the accompanying embodiments which do not limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments 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 foregoing description of the specific embodiments 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 preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application.
The numerical values mentioned for the various physical parameters, dimensions or quantities are only approximations and it is envisaged that the values higher/lower than the numerical values assigned to the parameters, dimensions or quantities fall within the scope of the disclosure, unless there is a statement in the specification specific to the contrary.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation. ,CLAIMS:WE CLAIM:
1. A system (100) for diagnosing fault in a rolling element-type bearing, said system (100) comprising:
• a vibration sensor (102) configured to be mounted on the housing of the bearing, to sense vibrations of said bearing being tested and to generate a vibration signal;
• an input unit (106) configured to receive inputs from the user, said inputs include parameters associated with said bearing including pitch diameter, roller diameter and the number of rollers in said bearing, sampling frequency of said vibration sensor (102), and rate of rotation of the rotating shaft of said bearing ; and
• an analysis unit (110) configured to determine if at least one fault is present in a component of said bearing from a list of components to be analysed for, using a continuous wavelet transform with an adaptive kernel, said list of components to be analysed for including the inner race, the outer race, the rollers and the cage of said bearing.
2. The system (100) as claimed in claim 1, wherein said analysis unit (110) includes a calculation module (109), a preprocessing module (112), a base function generator (114), a kernel function generator (116), a wavelet transform module (118) and a determination module (120).
3. The system (100) as claimed in claim 2, wherein said calculation module (109) is configured to at least calculate a fault frequency for a component of said bearing, based on said parameters.
4. The system (100) as claimed in claim 3, wherein said preprocessing module (112) is configured to perform Fast Fourier transform (FFT) of the vibration signal and extract structural frequencies from the vibration signal.
5. The system (100) as claimed in claim 4, wherein said base function generator (114) is configured to receive said structural frequencies from said preprocessing module (112) and to generate a base function using structural frequencies obtained from said preprocessing module (112).
6. The system (100) as claimed in claim 5, wherein said kernel function generator (116) is configured to receive said base function from said base function generator (114) and to generate an adaptive kernel function using the base function obtained from said base function generator (114) and said fault frequency obtained from said calculation module (109).
7. The system (100) as claimed in claim 6, wherein said wavelet transform module (118) is configured to receive said kernel function from said kernel function generator (116) and to perform continuous wavelet transform of the vibration signal using said adaptive kernel function obtained from said kernel function generator (116).
8. The system (100) as claimed in claim 7, wherein said determination module (120) is configured receive said wavelet transform from said wavelet transform module (118) and to determine if a fault is present in a component from said list of components by comparing a time difference calculated between at least first two largest consecutive coefficients of said continuous wavelet transform by said calculation module (112) with the period corresponding to said fault frequency.
9. The system (100) as claimed in claim 1, wherein said system (100) includes a signal conditioning unit (104) for removing low-frequency noise in said vibration signal.
10. The system (100) as claimed in claim 1, wherein said system (100) includes a display unit (108) configured to display at least said wavelet transform.
11. The system (100) as claimed in claim 10, wherein said display unit (108) is configured to display whether or not a fault is present in a component from said list of components.
12. The system (100) as claimed in claim 1, wherein said vibration sensor (102) is configured to sense vibration along X, Y and Z axes.
13. A method for diagnosing fault in a rolling element-type bearing, said method comprising:
• sensing vibrations of said bearing being tested using a vibration sensor (102) and generating a vibration signal;
• receiving inputs from the user through an input unit (106), said inputs include parameters associated with said bearing including pitch diameter, roller diameter and the number of rollers in said bearing, sampling frequency of said vibration sensor (102) and rate of rotation of the rotating shaft of said bearing ;
• performing fast Fourier transform (FFT) of the vibration signal using a preprocessing module (112);
• extracting structural frequencies from said FFT;
• generating a base function using said structural frequencies using a base function generator (114);
• calculating a fault frequency for a component selected from the inner race, the outer race, the rollers and the cage of said bearing , based on said parameters, using a calculation module (109);
• generating an adaptive kernel function using said base function and said fault frequency using a kernel function generator (116);
• performing continuous wavelet transform of said vibration signal using said adaptive kernel function using a wavelet transform module (118);
• calculating time difference between at least first two largest consecutive coefficients of said continuous wavelet transform using said calculation module (109); and
• determining if a fault is present in a component by comparing said time difference with the period corresponding to said fault frequency using a determination module (120).