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 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.

scholarly journals Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yongbin Liu ◽  
Bing He ◽  
Fang Liu ◽  
Siliang Lu ◽  
Yilei Zhao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Phase Space ◽  
Extreme Learning Machine ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Phase Point ◽  
Rolling Bearings ◽  
Point Distribution ◽  
Useful Life ◽  
Learning Machine

Rolling bearings play a pivotal role in rotating machinery. The degradation assessment and remaining useful life (RUL) prediction of bearings are critical to condition-based maintenance. However, sensitive feature extraction still remains a formidable challenge. In this paper, a novel feature extraction method is introduced to obtain the sensitive features through phase space reconstitution (PSR) and joint with approximate diagonalization of Eigen-matrices (JADE). Firstly, the original features are extracted from bearing vibration signals in time and frequency domain. Secondly, the PSR is applied to embed the original features into high dimensional phase space. The between-class and within-class scatter (SS) are calculated to evaluate the feature sensitivity through the phase point distribution of different degradation stages and then different weights are assigned to the corresponding features based on the calculatedSS. Thirdly, the JADE is employed to fuse the weighted features to obtain the advanced features which can better reflect the bearing degradation process. Finally, the advanced features are input into the extreme learning machine (ELM) to train the RUL prediction model. A set of experimental case studies are carried out to verify the effectiveness of the proposed method. The results show that the extracted advanced features can better reflect the degradation process compared to traditional features and could effectively predict the RUL of bearing.

Standard Uncertainty of Rolling Bearing Vibration as Time Series

2011 ◽  
Vol 382 ◽  
pp. 133-136
Author(s):  
Xin Tao Xia ◽  
Lei Lei Gao ◽  
Xiao Chao Sun
Keyword(s):  
Time Series ◽  
Measurement Theory ◽  
Failure Process ◽  
Rolling Bearing ◽  
Standard Uncertainty ◽  
Bearing Failure ◽  
Vibration Acceleration ◽  
Rolling Bearings ◽  
Vibrational Acceleration ◽  
Bearing Vibration

The standard uncertainty in the measurement theory is applied to evaluate the change of the rolling bearing vibration acceleration generated by the failure on the surface of the ring raceway. The time series are obtained via the experimental investigation on the vibrational acceleration of the rolling bearings with different failure diameters. And the result shows that the standard uncertainty of the vibrational acceleration increases nonlinearly with the failure diameter, revealing a new characteristic of the variation of the rolling bearing failure process. It follows that for a rolling bearing in running, the failure process can be described by the standard uncertainty of its vibration acceleration, laying a foundation for failure warning of a rolling bearing.

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 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.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

2020 ◽  
pp. 1-9
Author(s):  
Yiming Guo ◽  
Hui Zhang ◽  
Zhijie Xia ◽  
Chang Dong ◽  
Zhisheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Convolution Neural Network ◽  
Dual Channel ◽  
Time Domain Data ◽  
Channel Input ◽  
Deep Convolution Neural Network ◽  
Useful Life

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.

scholarly journals Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

2021 ◽  
Vol 11 (11) ◽  
pp. 4773
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Health State ◽  
Rolling Bearings ◽  
Data Set ◽  
Time Frequency ◽  
Useful Life ◽  
Degradation Indicator

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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