Wear Site Electrostastic Monitoring And Life Prediction For Rolling Bearing

Rolling bearing is one of the key components in the rotating machinery, once the failure of therolling bearing occured will affect the performance of the whole machine, or cause unplanned outage,which will make for economic losses or even the heavy casualties. As the greate discreteness of thelife of the rolling bearing, traditional maintenance often causes “inadequate maintenance” or “Excessmaintenance”. Therefore it is great significance to study the condition monitoring and life predictionmethods of the rolling bearing, which are the crucial technicals of the fault prevention and securityservice of the devices. In view of the traditional monitoring techniques for the inadequacy of ability ofearly failure found, the wear site electrostatic sensor and the corresponding life prediction frameworkis designed and proposed in this paper. The research results have important reference values andguiding significances for the improvement of condition monitoring and life prediction ability ofmechanical equipment.The main contents of this paper are as follows:(1) The wear site charging mechanism is revaled in this paper, and we design the electrostaticsensor based on the electrostatic induction principle. Considering the rolling bearing workingpeculiarity as a mixture of rolling and sliding, sliding bearing steel point contacts for early detectionof scuffing experiments, rolling friction fault injection experiments and rolling bearing lifeexperiments are carried out, which are used to verify the electrostatic sensor sensitivity for earlyfailure and different degree of faults. The results show that the electrostatic sensor could detectabnormal signals before complete failure and provide more effective information of the rolling beaingdegradation.(2) As electrostatic induction signals are mixed with strong different types of noises, it is difficultto adopt single method to suppress noises effectively. Three power frequency interference removemethod, digital notch filter, independent component analysis and spectrum interpolation areintroduced and compared by simulation, and then three method, wavelet denoising, EMD denosingand difference spectrum of singular values, are introduced to remove the background noises and therandom pulses. On this basis, a noval joint denoising method based on the spectrum interpolation anddifference spectrum of singular values is proposed to denoising of the electrostatic signals. Thesimulation and experiments show that the proposed method is effective and necessary.(3) For the traditional time and frequency domain index is insufficient to reflect the performance degradation of the rolling bearing, new indicators are explored for electrostatic monitoring. The natureChange of the electrostatic signals of rolling bearing life cycle is the random component changingduring the degradation process. Complexity just right can reflect this change so several complexitymeasures are proposed in this paper. The results show that complexity measures can reflect initialdegradation more sensitive than general indices.(4) In view of single feature is insensitive or inconsistency on the performance degradation of thebearing, a multi-parameter fusion method based on spectrum regression-gaussian mixture models(SR-GMM) is proposed in this paper. Several dimension reduction algorithms are compared and theSR mothod will be better to discover the data structure and has faster computing speed, and than thedata set of normal state is used to built the baseline GMM, a new index, bayesian inference distance(BID), is used to characterize the global distance of the test data to the GMM model, which is usedas a quantitative indicator to reflect the bearing performance degradation degree. This indicator usesbayesian inference combined priori and posteriori probability, which could earlier response to datachanges. Comparing to the other index, bayesian inference probability (BIP) and negative loglikelihood probability (NLLP), BID is advantageous. And the GMM-BID method can finddegradation occurred much earlier than the support vector domain description (SVDD) method. Tofurther illustrate the advantages of electrostatic monitoring, it is compared with the vibrationmonitoring, it is found that the electrostatic monitoring is more sensitive to the early fault and canprepared enough time for the decision-making.(5) Stochastic filtering life prediction model is brought into the field of electrostaticmonitoring.In view of data input of stochastic filtering model need to be one-dimensional, when theinput is high-dimensional, it need to calculating the joint probability distribution, which willinfluence the speed of calculation, a multi-feature fusion life prediction framework based onstochastic filtering is proposed, which the BID results are used as the input data, and establish therelationship between BID and residual life by stochastic filtering. The accuracy of the predictionresults is verified by the experiments. Meanwhile, the proposed method is compared with the statelife assessment method based on support vector machine (SVM), life prediction based on time seriesand hidden semi-Markov model (HSMM), the results show the superiority of the proposed method.