Performance degradation assessment of train rolling bearings based on SVM and segmented vote method

2016 ◽  
Author(s):  
Yong Qin ◽  
Dandan Wang ◽  
Xuejun Zhao ◽  
Hui Ma ◽  
Limin Jia ◽  
...  
Keyword(s):  
Performance Degradation ◽  
Rolling Bearings ◽  
Vote Method

Multi-Mode Particle Filter for Bearing Remaining Life Prediction

2018 ◽  
Author(s):  
Peng Wang ◽  
Ruqiang Yan ◽  
Robert X. Gao
Keyword(s):  
Service Life ◽  
Particle Filter ◽  
Individual Performance ◽  
Performance Degradation ◽  
Remaining Useful Life ◽  
Normal Operation ◽  
Life Stages ◽  
Rolling Bearings ◽  
Useful Life ◽  
Multi Mode

As a critical element in rotating machines, remaining useful life (RUL) prediction of rolling bearings plays an essential role in realizing predictive and preventative machine maintenance in modern manufacturing. The physics of defect (e.g. spall) initiation and propagation describes bearing’s service life as generally divided into three stages: normal operation, defect initiation, and accelerated performance degradation. The transition among the stages are embedded in the variations of monitored data, e.g., vibration. This paper presents a multi-mode particle filter (MMPF) that is aimed to: 1) automatically detect the transition among the three life stages; and 2) accurately characterize bearing performance degradation by integrating physical models with stochastic modeling method. In MMPF, a set of linear and non-linear modes (also called degradation functions) are first defined according to the physical/empirical knowledge as well as statistical analysis of the measured data (e.g. vibration). These modes are subsequently refined during the particle filtering (PF)-based bearing performance tracking process. Each mode corresponds to an individual performance scenario. A finite-state Markov chain switches among these modes, reflecting the transition between the service life stages. Case studies performed on two run-to-failure experiments indicate that the developed technique is effective in tracking the evolution of bearing performance degradation and predicting the remaining useful life of rolling bearings.

Bearing Performance Degradation Signal Simulation Based on Health Confidence Value Injection and FPGA

2015 ◽  
Vol 764-765 ◽  
pp. 198-203
Author(s):  
Quan Li Liu ◽  
Chen Lu ◽  
Hong Mei Liu
Keyword(s):  
Wavelet Packet ◽  
Digital Simulation ◽  
Characteristic Parameter ◽  
Simulation Method ◽  
Rolling Bearing ◽  
Parameter Extraction ◽  
Performance Degradation ◽  
Assessment Model ◽  
Self Organizing Map ◽  
Rolling Bearings

A digital simulation method for the performance degradation signal of rolling bearings is developed based on the analysis of experimental data. A self-organizing map neural network is utilized to build the performance degradation assessment model of the rolling bearings based on characteristic parameter extraction. Wavelet packet decomposition is then implemented to extract the wavelet coefficients in the corresponding performance degradation sensitive band. Different health confidence values are injected into the extracted wavelet packet coefficients, and signals are reconstructed according to the simulation needs to obtain rolling bearing vibration data under different degradation degrees. Understanding the exact mathematical model of the measured object is unnecessary in this method; the method is simple and reliable and helps solve the problem of performance degradation data simulation. Finally, an FPGA-based performance degradation signal simulator is designed by combining the analogy procedure, employed to support the verification process of fault diagnosis and prediction capability.

scholarly journals A Fault Diagnosis Approach for Rolling Bearing Based on Wavelet Packet Decomposition and GMM-HMM

2019 ◽  
Vol 24 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Liangpei Huang ◽  
Hua Huang ◽  
Yonghua Liu
Keyword(s):  
Fault Diagnosis ◽  
Failure Modes ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Model Parameters ◽  
Wavelet Packet Decomposition ◽  
Rolling Bearings ◽  
Fault Pattern ◽  
Diagnosis Approach

Considering frequency domain energy distribution differences of bearing vibration signal in the different failure modes, a rolling bearing fault pattern recognition method is proposed based on orthogonal wavelet packet decomposition and Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). The orthogonal three-layer wavelet packet decomposition is used to obtain wavelet packet decomposition coefficients from low frequency to high frequency. Rolling bearing raw vibration signals are firstly decomposed into the wavelet signals of different frequency bands, then different frequency band signals are reconstructed respectively to extract energy features, which form feature vectors as the model input of GMM-HMM. A large number of samples are trained to get model parameters for different bearing faults, then several groups of test data are adopted to verify GMM-HMMs so different fault types of rolling bearings are recognized. By calculating the current state appearance probability of monitoring data in GMM-HMMs, different failure patterns are recognized and evaluated from the maximum probability. Similarly, we establish GMM-HMMs for different grade fault samples and evaluated the performance degradation state. Test results show that the proposed fault diagnosis approach can identify accurately the fault pattern of rolling bearings and evaluate performance degradation of bearings.

scholarly journals Evaluation of Rolling Bearing Performance Degradation Using Wavelet Packet Energy Entropy and RBF Neural Network

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1064 ◽  
Author(s):  
Jianmin Zhou ◽  
Faling Wang ◽  
Chenchen Zhang ◽  
Long Zhang ◽  
Peng Li
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Operating Conditions ◽  
Threshold Method ◽  
Rolling Bearings ◽  
Practical Applications ◽  
Energy Entropy

Rolling bearings are the most important parts in rotating machinery, and one of the most vulnerable parts to failure. The rolling bearing is a cyclic symmetrical structure that is stable under normal operating conditions. However, when the rolling bearing fails, its symmetry is destroyed, resulting in unstable performance and causing major accidents. If the performance of rolling bearings can be monitored and evaluated in real time, maintenance strategies can be implemented promptly. In this paper, by using wavelet packet energy entropy (WPEE), the early fault-free features of bearing and the failure samples of similar bearings are decomposed firstly, and the energy value is extracted as the original feature, simultaneously. Secondly, a radial basis function (RBF) neural network model is established by using early fault-free features and similar bearing failure characteristics. The bearing full-life data characteristics of the extracted features are added into the RBF model in an iterative manner to obtain performance degradation Indicator. Boxplot was introduced as an adaptive threshold method to determine the failure threshold. Finally, the results are verified by empirical mode decomposition and Hilbert envelope demodulation. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method. The experimental results show that the method can diagnose early fault points in time and evaluate the degree of bearing degradation, which is of great significance for industrial practical applications.

Prediction of performance deterioration of rolling bearing based on JADE and PSO-SVM

2020 ◽  
pp. 095440622095120
Author(s):  
Tao Zan ◽  
Zhihao Liu ◽  
Hui Wang ◽  
Min Wang ◽  
Xiangsheng Gao ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Small Sample ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Rolling Bearings ◽  
Time Frequency ◽  
The Time Domain

In order to improve the prediction accuracy of performance degradation trends of rolling bearings, a method based on the joint approximative diagonalization of eigen-matrices (JADE) and particle swarm optimization support vector machine (PSO-SVM) was proposed. Firstly, the features of the time-domain, frequency-domain, and time-frequency-domain eigenvalues of the vibration signal corresponding to the entire life cycle of the rolling bearing are extracted, and the performance degradation parameters are initially selected by using the monotonicity parameter. Then, a fusion feature that can effectively represent the performance degradation is obtained by using the JADE method. Finally, the prediction model based on PSO-SVM is constructed to predict the performance degradation trend. By comparing with the prediction results obtained by other classical methods, it can be proved that this method can accurately predict the performance degradation trend and the remaining useful life (RUL) of rolling bearings under small sample sizes, and has considerable application potentials.

scholarly journals Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yaping Wang ◽  
Chaonan Yang ◽  
Di Xu ◽  
Jianghua Ge ◽  
Wei Cui
Keyword(s):  
Prediction Accuracy ◽  
Scoring Function ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Health State ◽  
Rolling Bearings ◽  
Output State ◽  
Parameter Configuration ◽  
Long Time

It is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. First, to reduce the uncertainty of the manual intervention, performance degradation characteristic indexes of rolling bearings are evaluated and screened by the correlation, the monotonicity, and the robustness. Second, the original characteristic indicator curve is divided into the Health Indicator (HI) curve and the residual curve by means of fixed-window averaging to quantitatively and intuitively reflect the deterioration degree of the rolling bearing performance. Finally, the Attention mechanism is combined with the LSTM model, and a scoring function is established to enhance the prediction accuracy. The scoring function is used to adjust the intermediate output state weight of the LSTM model and improve the prediction accuracy. The appropriate network structure and the parameter configuration are determined, and the prediction model of rolling bearing degradation performance is established. Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. The model has the strong robustness and the generalization ability, which has the important engineering practical value for the prediction of the equipment health state.

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