An Improved Method for Bearing Faults diagnosis

Transcription

1 An Improved Method for Bearing Faults diagnosis Adel.boudiaf, S.Taleb, D.Idiou,S.Ziani,R. Boulkroune Welding and NDT Research, Centre (CSC) BP64 CHERAGA-ALGERIA A.k.Moussaoui,Z Mentori Laboratory of Electrical Engineering of Guelma (LGEG), Université 8 Mai 945, Guelma, 4 -ALGERIA a_k_moussaoui@yahoo.fr Abstract Envelope analysis is especially suitable for fault diagnosis inducing periodic shocks or amplitude modulations such as gears and bearings and has been applied widely for mechanical fault detections over the last few decades. However, a critical limitation of this technique is that it requires a prior knowledge on filtering band. Due to this drawback, detecting machine defects at the incipient stage when defect-characteristic components are weak in amplitude and without a distinctive spectral pattern poses a challenge to the conventional enveloping spectral analysis technique.in order to overcome this limitation, this work gives a new signal processing approach for bearing faults diagnosis based on Hilbert Transform (HT) and Fast Fourier Transform (FFT). It is applied on real measurement signals collected from an experimental vibration system. The monitoring results indicate that the proposed method improves the bearing faults diagnosis relatively to other common techniques. Keywords Vibration Analysis; bearing Fault diagnosis; Hilbert Transform (HT); Envelope Analysis (EA); Fast Fourier Transform (FFT). I. INTRODUCTION The vibration monitoring is a fundamental axis of development and industrial research. Its purpose is to provide knowledge about the working condition of systems at each moment without stopping the production line. The monitoring allows avoiding the production losses related to breakdowns and reducing overall maintenance costs [-3]. In order to obtain useful information from vibration measurements and provide aid in early detection and diagnosis of faults there are many signal analysis techniques like Fourier Transform (FT), Short Time Fourier Transform (STFT) [4]- [5], Wigner-Ville Distribution (WVD) [6], Wavelet Transform (WT) [7-8], and Envelope Detection (ED) [8]. The most of these methods use spectral analysis based on FT, therefore, these methods present some limitations; it is the inability of FT to detect non-stationary signals whose frequencies vary over time. This incapability makes WT and EA an alternative solution for machinery fault diagnosis. WT can be continuous or discrete. It is an excellent tool for analyzing the nonstationary vibration signals. EA is an important signal processing technique. It is the method of extracting the modulating signal from an amplitude-modulated signal such as gears and bearings [7-8]. Rolling element bearings are a common component in machinery. Therefore, they have received great attention in the field of condition monitoring. Early fault detection in rolling element bearings can save millions of dollars in emergency maintenance cost. In this study, an improved method for bearing faults diagnosis is proposed based on Hilbert Transform (HT) and Fast Fourier Transform (FFT). It was applied on real measurement signals collected from an experimental vibration system containing bearing fault. The performances of the suggested method are assessed by its comparison with other techniques. The paper is organized as follows: Section presents system and bearing faults descriptions. Section 3 presents fault diagnosis techniques and monitoring results. Finally, Section 4 concludes our contributions. II. EXPERIMENTAL STUDY A. System Description The experiments presented in this paper used the vibration data obtained from the Case Western Reserve University Bearing Data Centre [9]. The data were collected from an accelerometer mounted on the motor housing at the drive end of an induction motor system coupled to a load; that can be varied within the operating range of the motor as shown in Figure. The data are sampled at a rate of khz and the duration of each vibration signal was seconds. The bearings used in this study are deep groove ball bearings manufactured by SKF. Faults were introduced to the test bearings using electro-discharge machining method. The defect diameters of the three faults were the same:.7,.4, and.8 inch. The motor speed during the experimental tests is 797,77,75,73 rpm. Each bearing was tested under the four different loads:,,, and 3 horse power (hp). Fig. The test-bed to simulate the fault of rolling element bearing B. Fault Bearing Characteristic Frequencies. Defective bearings generate vibration equal to the rotational speed of each element bearing frequencies. They relate notably to the rotation of the balls, the cage and the passage of the balls on the inner and outer rings. Frequencies associated with defective inner and outer races are as follows: 43

2 . Inner-race defects are characterized by Ball Pass Frequency Inner-race (BPFI): n d BPFI = f + cos α () r D. Outer-race defects are characterized by Ball Pass Frequency Outer-race (BPFO): n d () BPFO = f cos α r D where, f r is the rotational frequency, d the ball diameter, D the pitch diameter, n the number of balls and α the contact angle. In order to evaluate the suggested method, the measured data are collected at speeds of 797 rpm (3Hz) for -load ( hp) and 73 rpm (9Hz) for 3-load (3 hp) in three cases; normal state, inner races fault and outer races fault. The sampling frequency is Hz and the number of samples for each signal is 496 points. Figures (), (3) and (4) represent respectively the vibration signals of normal state, inner races fault and outer races fault. Table. Calculated Frequencies of bearing faults Location of the data The Drive-End bearing (DE) collection Fault Diameter.7 inches Faults Types Inner race Outer race Motor Load (HP) 3 3 Motor Speed (rpm) Frequencies Speed of Motor f r (Hz) Frequencies of bearing faults in Hz calculated by equations () and () Motor Speed 73 (rpm) Fig.3. Vibration signals of inner races fault Motor Speed 73 (rpm) Motor Speed 73 (rpm) Fig.. Vibration signals of normal state Fig.4. Vibration signals of outer races fault. III. FAULT DIAGNOSIS METHODS The vibration signals acquired from machines for diagnostics purposes can be classified either deterministic i.e periodic or non-periodic and random i.e stationary or nonstationary. In order to obtain useful information from vibration data and provide aid in early detection, diagnosis and correction of faults, we present in this section some signal processing methods appropriate for bearing faults diagnosis A. Temporal analysis. One of the simpler detection approaches is to analyze the measured vibration signal in the time domain. This method based on the analysis of the vibration data as a function of time 44

3 using several parameters or indicators such as peak value, peak to peak value, root mean square RMS, kurtosis, crest factor, impulse factor, shape factor and clearance factor [-3]. Below is the equation for the crest factor: Crest Factor,CRF= Peak value (3) RMS Value The equation of impulse factor is defined as: Impulse Factor,IMF = Peak value (4) N x N i i = The equation for kurtosis is given by: N 4 x i x Kurtosis = N i = (RMSvalue) 4 The temporal analysis of vibration signals of normal state, inner races fault and outer races fault are collected at speeds of 797 rpm (3Hz) for -load ( hp) using the following indicators: impulse factor, kurtosis, crest factor and shape factor, is shown in the Figs (5) and (6). (5) frequency components do not change over time [-3]. The spectrum X (f) of a given signal x (t) is defined by [-3]. + i πft X ( f ) = x( t) e dt (6) Where: ƒ is the signal frequency. The spectrums of signal with bearing fault at 797 rpm and 73 rpm for inner races fault and outer races fault are shown in figs (7) and (8). Obviously, it is difficult to identify the bearing fault, because the spectral analysis presents some limitations in the analysis of non-stationary signals. This inability makes the envelope analysis (EA) alternative for machinery fault diagnosis Hz 4 6 Motor Speed 73 (rpm) 36Hz..5 Fig.5. Indicators values of vibration signals of normal state and inner races fault at 797 rpm (3Hz) for -load ( hp). 4 6 Fig.7. Spectrums of signals with inner races fault Hz 4 6 Fig.6. Indicators values of vibration signals of normal state and outer races fault at 797 rpm (3Hz) for -load ( hp). These indicators are simple to implement. Also, the computed indicator allows the tracking of any abnormal change in condition machine. But, the temporal analysis will not provide any information on which component is faulty. This method represents only a strategy of security. B. Frequency analysis Frequency analysis or spectral analysis is the most commonly used method for analyzing stationary signals whose.. Motor Speed 73 (rpm) 34Hz 4 6 Fig.8. Spectrums of signals with outer races fault. 45

4 C. Envelope Analysis (EA) The envelope Analysis or amplitude demodulation is an important signal processing technique enabling the extraction of the modulating signal from an amplitude modulated signal. The Hilbert Transform is used to extract the envelope. The Hilbert Transform in time domain, for a given signal x(t), is given by [4-8]. x ( t) + ( τ ) x = dτ π t τ It is defined as the convolution of the signal x(t) with function /πt, which is the impulse response function of the Hilbert Transformer. The phase shifted and original signals are summed up to obtain an analytic signal x+(t) defined as follows [4-8]. x ( ) ( ) $ + t = x t + jx( t) (8) The envelope of the signal is defined as [4-8]. ( t ) x ( t) + x( t) ν = + (9) The process of the Analysis detection is involved from three main steps; The First step is signal filtering with a band-pass filter, the next step is the envelope extraction of band-pass filtered signal using the Hilbert transform, the final step is the extraction of frequency spectrum of the envelope signal using the Fast Fourier Transform (FFT) [7-8] as shown in Fig.9. (7) The envelope of signals with bearing faults, collected at 797 rpm and 73 rpm for inner races fault and outer races fault, are shown in Fig., Fig. respectively Motor Speed 73 (rpm) Fig.. Envelopes of signals with inner races fault Motor Speed 73 (rpm).5 Fig.9. Procedure for Envelope Analysis (EA). The center frequency of band pass filter should be selected to coincide with the center frequency of the resonance to be studied, the resonance frequencies and its bandwidth of signals with inner race fault, collected at speeds of 797 rpm (3Hz) for -load ( hp) and 73 (9Hz) for -load ( hp), are 358 Hz in the bandwidth [38-48] and 36Hz in the bandwidth [3-4] respectively. the resonance frequencies and its bandwidth of signals with outer race fault, collected at speeds of 797 rpm (3Hz) for - load ( hp) and 73 (9Hz) for -load ( hp), are 344 Hz in the bandwidth [34-384] and 34Hz in the bandwidth [3-38] respectively Fig.. Envelopes of signals with outer races fault. The envelope spectrum of signals with bearing faults, collected at 797 rpm and 73 rpm for inner races fault and outer races fault, are shown in Fig., Fig.3 respectively. From Fig. where a motor speed of 797 rpm and a motor load of HP are considered, the impact repetition frequency at 6 Hz and its second harmonic at 34 Hz can be clearly recognized. The frequency 6 Hz is very close to calculated Frequencies of Inner Race Fault (IRF) at 6. Hz as listed in table. Hence, the fault is identified as Inner Race Fault (IRF). From the Fig, where a motor speed of 73 rpm and a motor load of 3 HP are considered, the impact repetition 46

5 frequency at 56Hz and its second harmonic at 3Hz can be clearly recognized. The frequency 56 Hz is very close to calculated Frequencies of Inner Race Fault (IRF) at 56.3 Hz as listed in table. Hence, the fault is identified as Inner Race Fault (IRF). From Fig.3 where a motor speed of 797 rpm and a motor load of HP are considered, the impact repetition frequency at 8 Hz and its second harmonic at 6 Hz can be clearly recognized. The frequency 8 Hz is very close to calculated Frequencies of Outer Race Fault (ORF) at 7.37 Hz as listed in table. Hence, the fault is identified as Outer Race Fault. From Fig.3 where a motor speed of 73 rpm and a motor load of 3 HP are considered, the impact repetition frequency at 3 Hz and its second harmonic at 6 Hz can be clearly recognized. The frequency 8 Hz is very close to calculated Frequencies of Outer Race Fault (ORF) at 3.37 Hz as listed in table. Hence, the fault is identified as Outer Race Fault. The identification of the bearing faults is possible by using envelope analysis. However, the envelope analysis has a major drawback consisting of the requirement of a preliminary research of the resonance frequencies. In order to overcome this limitation, a new signal processing approach for bearing fault diagnosis which will be studied in the next section Hz.5 6Hz 34Hz Hz. Motor Speed 73 (rpm) 3Hz 6Hz Fig.3. Spectrums of outer races fault using Envelope Analysis. D. Suggested improved method In order to obtain more detailed information contained in the measured data, an improved method for bearing fault diagnosis is suggested. It is based on Hilbert transform (HT) and Fast Fourier Transform (FFT) and it follows a hierarchical paradigm as illustrated in Fig. 4. The first step is the envelope extraction of vibration signal x(t) using the Hilbert transform (HT), where T=Hilbert transform (x(t)), The next step is to calculate the grandeur C=FFT(T). Subsequently we calculate the grandeur A = log (abs (FFT (C))), and the final step is the extraction of frequency spectrum of the signal (A) using Fast Fourier Transform (FFT) Motor Speed 73 (rpm)..5 9Hz 56Hz 3Hz Fig.. Spectrums of inner races fault using Envelope Analysis Hz. 8Hz 6Hz Fig.4. Framework of the suggested bearing fault diagnosis method. In the present study, the identification of the bearing faults is possible by using the suggested improved method. It can be seen from Fig. 5 and 6 that the peaks at the rotation frequencies of the shaft (9 Hz and 3 Hz), also at the characteristic frequencies of the inner race fault and outer race fault and their multiples are present in the frequency spectrum Hz 6Hz 34Hz

6 Hz Motor Speed 73 (rpm) 56Hz 3Hz Fig.5. Spectrums of inner races fault using an improved method. suitable for fault diagnosis inducing periodic shocks or amplitude modulations such as gears and bearings. However, the envelope analysis has a major drawback consisting of the requirement of a preliminary research of the resonance frequencies. 4. The identification and the monitoring of the bearing faults using the suggested method are very easy. Also, the suggested method does not require the preliminary research of the resonance frequencies..5 3Hz 8Hz 6Hz Hz Motor Speed 73 (rpm) 3Hz 6Hz Fig.6. Spectrums of outer races fault using an improved method. IV. CONCLUSION In this paper, a novel signal processing technique based on a combination of Hilbert Transform (HT) and Fast Fourier Transform (FFT) was presented in order to improve machinery fault diagnosis. It was applied on real measurement signals collected from a vibration system containing bearing faults. The principal aim of the suggested method is to identify the bearing faults. After this study, the following points are concluded.. The temporal analysis allows the tracking of any abnormal change in condition machine. But, the temporal analysis will not provide any information on which component is faulty. This method represents only a strategy of security.. The identification of the bearing faults by using spectral analysis is difficult because, it is not suitable for non-stationary signal analysis. 3. The identification of the bearing faults is possible by using envelope analysis because it is especially REFERENCES [] N. Tandon, A. Parey, Condition Monitoring of Rotary Machines, Springer Series in Advanced Manufacturing, Vol.5, pp. 9-36,6. [] A. Heng, S. Zhang, A. Tan, J. Mathew, Rotating machinery prognostics: State of the art, challenges and opportunities, Journal of Mechanical Systems and Signal Processing, Vol.3, no. 3, pp , 997. [3] A.W. Lees, N. Tandon, A. Choudhury, A Review of Vibration and Acoustic Measurement Methods for the Detection of Defects in Rolling Element Bearings, Tribology International, Vol.3, pp ,999. [4] K. Shibata, A. Takahashi, T. Shirai, Fault diagnosis of rotating machinery through visualisation of sound signal, Journal of Mechanical Systems and Signal Processing, Vol. 4, pp. 9 4,. [5] S. Seker, E. Ayaz, A study on condition monitoring for induction motors under the accelerated aging processes, IEEE Power Engineering, Vol., no. 7, pp ,. [6] N. Baydar, A. Ball, A comparative study of acoustic and vibration signals in detection of gear failures using wigner ville distribution, Mechanical Systems and Signal Processing, Vol.5, no. 6, pp. 9 7,. [7] H. Bendjama, S. Bouhouche, M.S. Boucherit, Application of Wavelet Transform for Fault Diagnosis in Rotating Machinery, International Journal of Machine Learning and Computing,Vol., no., pp. 8 87,. [8] H. Bendjama, S. Bouhouche, A.k. Moussaoui, Wavelet Transform for Bearing Faults Diagnosis, proceedings of the 3, Conference of Advances in Control Engineering,, no.4, pp , 3. [9] K.A. Loparo, Bearings vibration data set, Case Western Reserve University, ( [] V.Saxena, N Chowdhury,S. Devendiran, Assessment of Gearbox Fault Detection Using Vibration Signal Analysis and Acoustic Emission Technique, Journal of Mechanical and Civil Engineering, Vol.7, no. 4, pp. 5 6, 3. [] A. Aherwar, M. Saifullah Khalid, Vibration analysis techniques for gearbox Diagnostic: a review, International Journal of Advanced Engineering Technology, Vol.3, no., pp. 4,. [] T.Karacay, N.Akturk, Experimental diagnostics of ball bearings using statistical and spectral methods, Tribology International,Vol.4, pp , 9. [3] A.Bhende, G.Awari,S.Untawale, Assessment of Bearing Fault Detection Using Vibration Signal Analysis, Journal of Technical and Non-Technical, Vol., no.5, pp. 49-6,. [4] A. Djebala,N. Ouelaa,C. Benchaabane, D.F. Laefer, Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects, Journal of Meccanica, Vol.47, no.7, pp. 6 6,. [5] M. Feldman, Hilbert transform in vibration analysis, Mechanical Systems and Signal Processing, Vol.5, pp ,. [6] R. B.Randall, J. Antoni, Rolling element bearing diagnostics, Mechanical Systems and Signal Processing, Vol.5, pp ,. [7] J.Slavic, A. Brkovic, M. Boltezar, Typical Bearing-Fault Rating Using Force Measurements-Application to Real Data, Journal of Vibration and Control,Vol.7, no.4, pp ,. [8] M.Pan, W.Tsao, Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings, International Journal of Mechanical Sciences, Vol.69, pp. 4 4, 3. 48