Study Of The Method For Rolling Element Bearing Fault Diagnosis Based On Mathematical Morphology

Rotating machinery occupies an increasingly important position in modern industry and intelligent manufacturing.Real-time monitoring of the working state of rotating machinery not only can avoid the occurrence of disasters,but also bring the obvious economic benefits.Rolling element bearing is reputed as one of the major parts of rotating machinery equipment in industrial production,which is widely used in different fields.The running state of rolling element bearing is directly related to the working performance of mechanical equipment.Therefore,rolling element bearing is a good research object for exploration and excavation of the novel fault diagnosis method.In practical engineering,bearing vibration signals caused by local defects are usually characterized by some characteristics(e.g.nonlinearity,non-stationary,low signal to noise ratio and inconspicuous features),which indicates it is difficult to make effective diagnosis through directly the frequency spectrum analysis.In addition,some conventional diagnostic approaches,such as AR model,spectral kurtosis and time-frequency analysis,have their own limitations.Hence,exploring effective fault diagnosis method is an urgent and challenging task in engineering practice.Mathematical morphology(MM)is a non-linear and non-stationary signal analysis method,which can effectively match and capture the details of non-stationary signals by using a probe named the structuring element(SE).Besides,MM has a good application prospect in bearing damage detection.In this paper,rolling element bearing is taken as the research object.According to the existing MM method,MM-based fault detection approach is studied deeply and improved,which is aimed at improving the accuracy of bearing fault detection and avoiding the accident as much as possible.The innovations and main contributions of this paper are as follows:(1)Inspired by the difference value between the original signal and combination morphological filter,a new morphological operator termed as combination morphological filter-hat transform(CMFH)is formulated based on the theories and properties of the existing MM.The applicable occasions of different morphological operators are suggested by investigating the filtering characteristics of MM.On this basis,a method called particle swarm optimization-based CMFH is proposed for the purpose of overcoming the drawbacks of empirical selection of SE parameters in morphological operators.In this method,the optimal SE parameter of morphological operator is firstly determined by PSO algorithm,and then CMFH containing the optimal SE parameter is used to analyze fault data and extract bearing fault features.Simulation results show that the proposed method is effective in extracting impact fault feature.(2)According to the fusion among CMFH,multicale SE and weighted arithmetic,multiscale combination morphological filter-hat transform(MCFHM)is formulated for acquiring fault feature information at different scales.Then,on this basis,feature selection framework-based multiscale morphological analysis method(FS-MMA)is proposed for the purpose of solving the issue of losing local fault information of multiscale morphological analysis(MMA)with a single index.The algorithm first extracts multi-domain features of the raw signal,and then selects several sensitive features via entropy weight method,and finally grey correlation analysis is conducted to determine the optimal SE scale and then fault feature information extraction of bearing can be achieved.The validity of the proposed method is validated by analyzing the simulated and experimental bearing fault data.Results show that FS-MMA has better performance in bearing fault feature extraction and diagnosis accuracy compared with traditional MMA with a single index.(3)According to the product between CMFH and average-hat transform(AVGH),a novel morphological operator named morphology hat product operation(MHPO)is defined.On this basis,based on the excellent characteristics of feature enhancement and noise suppression of diagonal slice spectrum,an enhanced scale morphological-hat product filtering(ESMHPF)is then presented.In this method,multi-scale morphology hat product operation of the original signal is firstly performed.Next,TCDS and DSS of morphological filtering results for each SE scale are calculated,and fault feature ratio is applied to determine the optimal SE scale.Finally,optimal scale morphology diagonal slice spectrum is used for strengthening bearing fault characteristic information and improving the diagnosis performance of traditional multiscale morphological filtering(MMF).The effectiveness of the proposed method is verified by the simulated and measured bearing fault signal.Results indicate that ESMHPF not only can extract fault characteristic information,but also have the performance of feature enhancement.(4)Through the integration of morphological gradient operators,pattern spectrum(PS)and information entropy,the concept of pattern gradient spectrum(PGS)and pattern gradient spectrum entropy(PGSE)is firstly presented.On this basis,according to the coarse graining procedure of traditional multiscale entropy(MSE),generalized multiscale pattern gradient spectrum entropy(GMPGSE)is further proposed for evaluating randomness and dynamic behavior of the time series on different scales.Finally,in order to realize intelligent identification and automatic classification of bearing fault states,an intelligent detection algorithm for rolling bearing based on GMPGSE is developed by combining the GMPGSE,LS and ELM.The feasibility of the proposed method is verified by the analysis of instance data.Results show that GMPGSE has higher diagnostic accuracy and computational efficiency,and can identify different bearing fault states more effectively,compared with MSE.(5)The feasibility of the above-mentioned method is validated by performing a bearing fault simulation experiment.Firstly,the scheme of bearing fault simulation experiment is introduced in detail.Then,bearing vibration data collected and actual engineering data are respectively analyzed by three methods(PSO-CMFH,FS-MMA and ESMHPF)to prove their efficacy.Performance among feature extraction algorithms is compared and discussed by quantitative and qualitative analysis.Finally,GMPGSE is applied to intelligent fault diagnosis of rolling element bearing.Experimental results show that GMPGSE can identify effectively different bearing damage types,and its classification accuracy is higher than that of MSE.