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.

Fault Prediction Method of the Marine Gas Turbine Based on Neural Network-Markov

2014 ◽  
Vol 538 ◽  
pp. 171-174 ◽  
Author(s):  
Jian Guo Cui ◽  
Long Zhang ◽  
Gui Hua Wang ◽  
Bo Cui ◽  
Li Ying Jiang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Gas Turbine ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Health State ◽  
Object A ◽  
The Neural Network ◽  
Specific Object

Since the fault of marine gas turbine is difficult to predict accurately, making the rolling bearing as the specific object, a fault prediction model of the marine gas turbine based on Neural Network and Markov method is built through the data analysis, preprocessing and feature extraction for the rolling bearing history test data. First, it uses the neural network method to realize the health state recognition of the marine gas turbine. Then, the fault of the marine gas turbine is predicted by taking advantage of the fault prediction which is based on the Markov model. The results show that the efficiency of fault prediction for the marine gas turbine can be realized better through the fault prediction model constructed in view of the Neural Network and Markov. And it also has a significant practical value in project item.

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 Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hongsheng Yan ◽  
Hongfu Zuo ◽  
Jianzhong Sun ◽  
Di Zhou ◽  
Han Wang
Keyword(s):  
Standard Deviation ◽  
Data Fusion ◽  
Complex Structure ◽  
Prediction Method ◽  
Mapping Function ◽  
Performance Degradation ◽  
Remaining Useful Life ◽  
Aviation Industry ◽  
Health State ◽  
Two Stage

Aeroengine is one of the most concerned objects of the relevant aviation industry and researchers, and it is a hard work to assess and predict performance degradation due to the complex structure and the changeable operating condition of the engine. In order to realize the performance degradation assessment and remaining useful life (RUL) prediction of aeroengine, this paper proposes a two-stage assessment and prediction method based on data fusion. First, the standard deviation merged by multiple selected features is used as the health indicator to characterize the engine performance. Second, a sliding window detection method called average local window slope is proposed to determine the current health state of observations by a specified rule. Finally, the RUL prediction is performed on the observation in the two stages, respectively. On the one hand, a similarity-based RUL prediction method is used to engines in the health stage, and on the other hand, for engines in the degradation stage, a RUL prediction method based on a mapping function of the standard deviation and the current using cycle is established. The proposed method has been applied and verified on the NASA’s C-MAPSS simulation data. Results of degradation assessment and prediction show that the proposed method is trustworthy and feasible from the engineering perspective, and it has better performance in the comprehensive indicator compared with other methods.

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 Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Huaitao Shi ◽  
Yajun Shang ◽  
Xiaochen Zhang ◽  
Yinghan Tang
Keyword(s):  
Transfer Learning ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Target Domain ◽  
Rolling Bearings ◽  
Noise Interference ◽  
Fault Features ◽  
Early Failures

In actual working conditions, the initial faults of rolling bearings are difficult to effectively predict due to the lack of evolution knowledge, weak fault information, and strong noise interference. In this paper, a rolling bearing initial fault prediction model that is based on transfer learning and the DCAE-TCN is presented. Firstly, a deep autoencoder (DAE as the first two hidden layers and CAE as the last hidden layer) is used to extract fault features from the rolling bearing vibration signal data. Then, the balanced distributed adaptation (BDA) is used to minimise the distribution difference and class spacing between extracted fault features, and a common feature set is constructed. The temporal features of the original vibration signal in the target domain are extracted using the advantages of the TCN. The experiments are conducted on the publicly available XJTU-SY dataset. The experimental results show that the proposed method can effectively learn the transferable features and compensate the differences between the source and target domains and has a promising application with higher accuracy and robustness for the prediction of early failures of rolling bearings.

scholarly journals Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Fengtao Wang ◽  
Xiaofei Liu ◽  
Chenxi Liu ◽  
Hongkun Li ◽  
Qingkai Han
Keyword(s):  
Interpolation Method ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Main Trend ◽  
Data Series ◽  
Model Parameters ◽  
Remaining Life ◽  
Rolling Bearings ◽  
Useful Life

A trend prediction method based on the Pchip-EEMD-GM(1,1) to predict the remaining useful life (RUL) of rolling bearings was proposed in this paper. Firstly, the dimension of the extracted features was reduced by the KPCA dimensionality reduction method, and the WPHM model parameters were estimated via the kernel principal components. Secondly, the hazard rate was calculated at each time, and the Pchip interpolation method was used to obtain the uniformly spaced interpolation data series. Then the main trend of signal was obtained through the EEMD method to fit the GM(1,1) prediction model. Finally, the GM (1,1) method was used to predict the remaining life of the rolling bearing. The full life test of rolling bearing was provided to demonstrate that the method predicting the hazard data directly has the higher accuracy compared with predicting the covariates, and the results verified the feasibility and effectiveness of the proposed method for predicting the remaining life.

Efficiency of application of the phase-chronometric method and neurodiagnostics for monitoring the degradation of rolling bearings during operation

2020 ◽  
pp. 43-50
Author(s):  
A.S. Komshin ◽  
K.G. Potapov ◽  
V.I. Pronyakin ◽  
A.B. Syritskii
Keyword(s):  
Life Cycle ◽  
Wavelet Analysis ◽  
Metrological Support ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Measurement Information ◽  
Rolling Bearings ◽  
Vibration Diagnostics ◽  
Bearing Life ◽  
Alternative Approach

The paper presents an alternative approach to metrological support and assessment of the technical condition of rolling bearings in operation. The analysis of existing approaches, including methods of vibration diagnostics, envelope analysis, wavelet analysis, etc. Considers the possibility of applying a phase-chronometric method for support on the basis of neurodiagnostics bearing life cycle on the basis of the unified format of measurement information. The possibility of diagnosing a rolling bearing when analyzing measurement information from the shaft and separator was evaluated.

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