scholarly journals Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 791
Author(s):  
Hassane Hotait ◽  
Xavier Chiementin ◽  
Lanto Rasolofondraibe
Keyword(s):  
Short Term Memory ◽  
Spatial Clustering ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Suggested Method ◽  
Health Indicator ◽  
Damage State ◽  
Rolling Bearings ◽  
Regressive Model ◽  
Input Model

This paper suggests a new method to predict the Remaining Useful Life (RUL) of rolling bearings based on Long Short Term Memory (LSTM), in order to obtain the degradation condition of the rolling bearings and realize the predictive maintenance. The approach is divided into three parts: the first part is the clustering to detect the damage state by the density-based spatial clustering of applications with noise. The second one is the health indicator construction which could give a better reflection of the bearing degradation tendency and is selected as the input for the prediction model. In the third part of the RUL prediction, the LSTM approach is employed to improve the accuracy of the prediction. The rationale of this work is to combine the two methods—the density-based spatial clustering of applications with noise and LSTM—to identify the abnormal state in rolling bearings, then estimate the RUL. The suggested method is confirmed by experimental data of bearing life cycle, and the RUL prediction results of the model LSTM are compared with the nonlinear au-regressive model with exogenous input model. In addition, the constructed health indicator is compared with the spectral kurtosis feature. The results demonstrated that the suggested method is more appropriate than the nonlinear au-regressive model with exogenous input model for the prediction of bearing RUL.

scholarly journals A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1864 ◽  
Author(s):  
Gangjin Huang ◽  
Hongkun Li ◽  
Jiayu Ou ◽  
Yuanliang Zhang ◽  
Mingliang Zhang
Keyword(s):  
Short Term Memory ◽  
Health Management ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Rolling Bearings ◽  
Variable Model ◽  
Management Technology ◽  
Useful Life ◽  
Degradation Evaluation

Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.

scholarly journals Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ying Zhang ◽  
Anchen Wang
Keyword(s):  
Life Prediction ◽  
Engineering Application ◽  
Rolling Bearing ◽  
Gaussian Mixture ◽  
Remaining Useful Life ◽  
Joint Probability Distribution ◽  
Rolling Bearings ◽  
Two Stage ◽  
Stochastic Filtering ◽  
Useful Life

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value.

Data Driven Prognostics for Rotating Machinery

2013 ◽  
pp. 120-133 ◽  
Author(s):  
Eric Bechhoefer
Keyword(s):  
Detection Threshold ◽  
Remaining Useful Life ◽  
Condition Based Maintenance ◽  
Data Driven ◽  
Health Indicator ◽  
Damage State ◽  
Condition Indicators ◽  
Gear Fault ◽  
Useful Life ◽  
Threshold Setting

A prognostic is an estimate of the remaining useful life of a monitored part. While diagnostics alone can support condition based maintenance practices, prognostics facilitates changes to logistics which can greatly reduce cost or increase readiness and availability. A successful prognostic requires four processes: 1) feature extraction of measured data to estimate damage; 2) a threshold for the feature, which, when exceeded, indicates that it is appropriate to perform maintenance; 3) given a future load profile, a model that can estimate the remaining useful life of the component based on the current damage state; and 4) an estimate of the confidence in the prognostic. This chapter outlines a process for data-driven prognostics by: describing appropriate condition indicators (CIs) for gear fault detection; threshold setting for those CIs through fusion into a component health indicator (HI); using a state space process to estimate the remaining useful life given the current component health; and a state estimate to quantify the confidence in the estimate of the remaining useful life.

scholarly journals Rotating Machinery Remaining Useful Life Prediction Scheme Using Deep-Learning-Based Health Indicator and a New RVM

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Gang Zhang ◽  
Weige Liang ◽  
Bo She ◽  
Fuqing Tian
Keyword(s):  
Deep Learning ◽  
Life Prediction ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
Relevance Vector Machine ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Time Frequency ◽  
Prediction Scheme ◽  
Useful Life

Remaining useful life (RUL) prediction plays a significant role in developing the condition-based maintenance and improving the reliability and safety of machines. This paper proposes a remaining useful life prediction scheme combining deep-learning-based health indicator and a new relevance vector machine. First, both one-dimensional time-series information and two-dimensional time-frequency maps are input into a hybrid deep-learning structure network consisting of convolutional neural network (CNN) and long short-term memory network (LSTM) to construct health indicator (HI). Then, the prediction results and confidence interval are calculated by a new RVM enhanced by a polynomial regression model. The proposed method is verified by the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed method in improving the prediction accuracy and analyzing the prediction uncertainty.

Stages prediction of the remaining useful life of rolling bearing based on regularized extreme learning machine

2021 ◽  
pp. 095440622110095
Author(s):  
Chenchen Wu ◽  
Hongchun Sun ◽  
Zihan Zhang
Keyword(s):  
Time Series ◽  
Pearson Correlation ◽  
Selection Criterion ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Useful Life ◽  
Bearing Vibration ◽  
Learning Machine

The prediction of the remaining useful life (RUL) of rolling bearings is an important means to ensure the rotating machinery's safe operation. At present, most of the proposed methods use direct prediction based on bearing vibration signals, which not only have low prediction accuracy but also time-consuming. This paper proposes a staged prediction method, and the regularized learning machine (RELM) based on the proposed sensitive degradation feature is applied to predict RUL of the bearing with high accuracy and speed. Firstly, the relative root mean square value (RRMS) is used to divide the degradation stages of rolling bearings. Secondly, the RRMS indicator is used for multi-step time series prediction in the normal phase of the bearing. Thirdly, in the bearing's degradation stage, the Pearson Correlation Coefficient (PCC) Combined Entropy Weight Method (EWM) feature selection criterion is proposed to predict the RUL of the rolling bearing. Finally, the sensitive degradation feature of the bearing vibration signals is input into RELM to predict the RUL. The bearing data sets of PHM Challenging 2012 are used to verify the effectiveness of the proposed method. Three comparative experiments have been verified to prove the accuracy and rapidity of the proposed method in time series forecasting.

scholarly journals Bearing Remaining Useful Life Prediction Based on a Scaled Health Indicator and a LSTM Model with Attention Mechanism

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 238
Author(s):  
Songhao Gao ◽  
Xin Xiong ◽  
Yanfei Zhou ◽  
Jiashuo Zhang
Keyword(s):  
Health Indicators ◽  
Rolling Bearing ◽  
Attention Mechanism ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Rotor Systems ◽  
Damage Degree ◽  
Traditional Health ◽  
Actual Damage ◽  
Phase Space Warping

Rotor systems are of considerable importance in most modern industrial machinery, and the evaluation of the working conditions and longevity of their core component—the rolling bearing—has gained considerable research interest. In this study, a scale-normalized bearing health indicator based on the improved phase space warping (PSW) and hidden Markov model regression was established. This indicator was then used as the input for the encoder–decoder LSTM neural network with an attention mechanism to predict the rolling bearing RUL. Experiments show that compared with traditional health indicators such as kurtosis and root mean square (RMS), this scale-normalized bearing health indicator directly indicates the actual damage degree of the bearing, thereby enabling the LSTM model to predict RUL of the bearing more accurately.

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 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.

scholarly journals A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

2021 ◽  
Author(s):  
Zhao-Hua Liu ◽  
Xu-Dong Meng ◽  
Hua-Liang Wei ◽  
Liang Chen ◽  
Bi-Liang Lu ◽  
...  
Keyword(s):  
Residual Life ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Elastic Net ◽  
Maintenance Cost ◽  
Real World Data ◽  
Rolling Bearings ◽  
Safety And Reliability ◽  
Useful Life

AbstractRotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life (RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network (LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure. In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.

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