The impulsive signals produced by bearing faults are usually modulated in amplitude. Multiscale morphology is suited to demodulate the signal because of its powerful demodulation ability. However, when the structuring element scales are increased gradually, the multiscale morphology method using closing and/or opening allows the low-amplitude impulses to be eliminated. Therefore, iterative asymmetric multiscale morphology is explored in this paper to handle the problem. Firstly, a modified difference filter is developed based on closing and opening to conduct iterative morphology operation, and then a type of asymmetric-multiscale is designed to set the structuring element scales of the modified difference filter filter for demodulating the fault signal with amplitude modulation well. Meanwhile, iterative morphology is conducted to enhance the impulsive features, and kurtosis acts as the iteration stop condition. The effectiveness of the proposed method is evaluated by both simulation experiment and the vibration signals of rolling element bearings with an inner race, an outer, and a rolling element faults. In comparisons with the conventional multiscale morphology, the results demonstrate that the iterative asymmetric multiscale morphology method has better diagnosis for the bearing faults.
McFaddenPSmithJ. Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol Int1984; 17: 3–10.
2.
McFaddenPSmithJ. Model for the vibration produced by a single point defect in a rolling element bearing. J Sound Vib1984; 96: 69–82.
3.
LiuTChenJDongGet al.The fault detection and diagnosis in rolling element bearings using frequency band entropy. Proc IMechE, Part C: J Mechanical Engineering Science2013; 227: 87–99.
4.
AntoniJ. The spectral kurtosis: A useful tool for characterising non-stationary signals. Mech Syst Signal Process2006; 20: 282–307.
5.
YanRGaoRX. Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis. Tribol Int2009; 42: 293–302.
6.
AntoniJ. Cyclostationarity by examples. Mech Syst Signal Process2009; 23: 987–1036.
7.
AntoniJBonnardotFRaadAet al.Cyclostationary modelling of rotating machine vibration signals. Mech Syst Signal Process2004; 18: 1285–1314.
8.
Randall RB, Antoni J and Chobsaard S. A comparison of cyclostationary and envelope analysis in the diagnostics of rolling element bearings. In: Proceedings of 2000 IEEE international conference on acoustics, speech, and signal processing, ICASSP’00, 2000, pp.3882–3885.
9.
RandallRBAntoniJChobsaardS. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mech Syst Signal Process2001; 15: 945–962.
ZhouYChenJDongGMet al.Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis. Mech Syst Signal Process2012; 26: 229–243.
12.
LeiYHeZZiY. EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Syst Appl2011; 38: 7334–7341.
13.
LeiYHeZZiY. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech Syst Signal Process2009; 23: 1327–1338.
14.
VanMKangHJ. Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition. Proc IMechE, Part C: J Mechanical Engineering Science2016; 230: 291–302.
15.
YuYChengJS. A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J Sound Vib2006; 294: 269–277.
16.
SamantaBAl-BalushiKRAl-AraimiSA. Bearing fault detection using artificial neural networks and genetic algorithm. EURASIP J Appl Signal Process2004, pp. 366–377.
17.
ZhuKLiH. A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine. Proc IMechE, Part C: J Mechanical Engineering Science2015.
18.
ZhangXZhouJ. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mech Syst Signal Process2013; 41: 127–140.
19.
WidodoAYangBS. Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process2007; 21: 2560–2574.
20.
HussainALeeSJChoiMSet al.An expert system for acoustic diagnosis of power circuit breakers and on-load tap changers. Expert Syst Appl2015; 42: 9426–9433.
21.
RandallRBAntoniJ. Rolling element bearing diagnostics - A tutorial. Mech Syst Signal Process2011; 25: 485–520.
22.
SerraJ. Image analysis and mathematical morphology, London: Academic press, 1982.
NikolaouNGAntoniadisIA. Application of morphological operators as envelope extractors for implusive-type periodic signals. Mech Syst Signal Process2003; 17: 1147–1162.
25.
RajASMuraliN. Early classification of bearing faults using morphological operators and fuzzy inference. IEEE Trans Ind Electron2013; 60: 567–574.
26.
DongYLiaoMZhangXet al.Faults diagnosis of rolling element bearings based on modified morphological method. Mech Syst Signal Process2011; 25: 1276–1286.
27.
HaralickRMSternbergSRZhuangX. Image analysis using mathematical morphology. IEEE Trans Pattern Anal Mach Intell1987; 9: 532–550.
28.
MaragosP. Pattern spectrum and multiscale shape representation. IEEE Trans Pattern Anal Mach Intell1989; 11: 701–716.
29.
LiBZhangPLWangZJet al.A weighted multi-scale morphological gradient filter for rolling element bearing fault detection. ISA Trans2011; 50: 599–608.
ChenQChenZSunWet al.A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis. J Vib Control2013; 21: 765–789.
32.
HuZWangCZhuJet al.Bearing fault diagnosis based on an improved morphological filter. Measurement2016; 80: 163–178.
33.
ShenCHeQKongFet al.A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis. Proc IMechE, Part C: J Mechanical Engineering Science2013; 227: 1362–1370.
34.
ZhangLXuJYangJet al.Multiscale morphology analysis and its application to fault diagnosis. Mech Syst Signal Process2008; 22: 597–610.
35.
RandallRBAntoniJChobsaardS. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults. Mech Syst Signal Process2001; 15: 945–962.
SmithWARandallRB. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mech Syst Signal Process2015; 64: 100–131.
