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

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

Mathematical Problems in Engineering ◽

10.1155/2020/2153235 ◽

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.

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A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery

Shock and Vibration ◽

10.1155/2021/6806319 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Hailong Lin ◽

Zihao Lei ◽

Guangrui Wen ◽

Xiaojun Tian ◽

Xin Huang ◽

...

Keyword(s):

Turning Point ◽

Principal Component ◽

Remaining Useful Life ◽

Degradation Index ◽

Rolling Bearings ◽

Construction Methods ◽

Degradation Data ◽

Novel Approach ◽

Safety And Reliability ◽

Useful Life

Rolling bearings are key components of rotating machinery, and predicting the remaining useful life (RUL) is of great significance in practical industrial scenarios and is being increasingly studied. A precise and reliable remaining useful life prediction result provides valuable information for decision-makers, which is essential to ensure the safety and reliability of mechanical systems. Generally, the RUL label is considered to be an ideal life curve, which is the benchmark for RUL prediction. However, the existing label construction methods make more use of expert experience and seldom mine knowledge from data and combine experience to assist in constructing a health index (HI). In this paper, a novel and simple approach of label construction is proposed for predicting the RUL accurately. More specifically, the degradation index of the multiscale frequency domain is first extracted. Furthermore, the fuzzy C-means (FCM) algorithm is innovatively used to divide the degradation data into several stages to obtain the turning point of degradation. Then, a nonlinear degradation index, the RUL label with the turning point, was constructed based on principal component analysis (PCA). Finally, the recurrent neural network (RNN) is used for prediction and verification. In order to verify the effectiveness of the proposed approach, two different bearing lifecycle datasets are gathered and analyzed. The analysis result confirms that the proposed method is able to achieve a better performance, which outperforms some existing methods.

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

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211009556 ◽

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.

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

Sensors ◽

10.3390/s20071864 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1864 ◽

Cited By ~ 3

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.

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

Shock and Vibration ◽

10.1155/2018/3013684 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

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.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201965 ◽

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.

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

Applied Sciences ◽

10.3390/app11114773 ◽

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.

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A Clustering-Based Framework for Performance Degradation Prediction of Slewing Bearing Using Multiple Physical Signals

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4042843 ◽

2019 ◽

Vol 5 (2) ◽

Cited By ~ 2

Author(s):

Peng Ding ◽

Hua Wang ◽

Yongfen Dai

Keyword(s):

Degradation Process ◽

Remaining Useful Life ◽

Data Driven ◽

Self Organizing Map ◽

Rolling Bearings ◽

Heavy Load ◽

Joint Application ◽

Learning Modes ◽

Useful Life ◽

Slewing Bearings

Diagnosing the failure or predicting the performance state of low-speed and heavy-load slewing bearings is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures focus on the performance prediction of small rolling bearings, while studies on slewing bearings' health evaluation are very rare. Among these rare studies, supervised or unsupervised data-driven models are often used alone, few researchers devote to remaining useful life (RUL) prediction using the joint application of two learning modes which could fully take diversity and complexity of slewing bearings' degradation and damage into consideration. Therefore, this paper proposes a clustering-based framework with aids of supervised models and multiple physical signals. Correlation analysis and principle component analysis (PCA)-based multiple sensitive features in time-domain are used to establish the performance recession indicators (PRIs) of torque, temperature, and vibration. Subsequently, these three indicators are divided into several parts representing different degradation periods via optimized self-organizing map (OSOM). Finally, corresponding data-driven life models of these degradation periods are generated. Experimental results indicate that multiple physical signals can effectively describe the degradation process. The proposed clustering-based framework is provided with a more accurate prediction of slewing bearings' RUL and well reflects the performance recession periods.

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