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.

scholarly journals A Reliable Health Indicator for Fault Prognosis of Bearings

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3740 ◽  
Author(s):  
Bach Duong ◽  
Sheraz Khan ◽  
Dongkoo Shon ◽  
Kichang Im ◽  
Jeongho Park ◽  
...  
Keyword(s):  
Wavelet Packet ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Poor Correlation ◽  
Discrete Wavelet ◽  
Health Indicator ◽  
Vibration Acceleration ◽  
Discrete Wavelet Packet Transform ◽  
Acceleration Signal ◽  
Gradient Based

Estimation of the remaining useful life (RUL) of bearings is important to avoid abrupt shutdowns in rotary machines. An important task in RUL estimation is the construction of a suitable health indicator (HI) to infer the bearing condition. Conventional health indicators rely on features of the vibration acceleration signal and are predominantly calculated without considering its non-stationary nature. This often results in an HI with a trend that is difficult to model, as well as random fluctuations and poor correlation with bearing degradation. Therefore, this paper presents a method for constructing a bearing’s HI by considering the non-stationarity of the vibration acceleration signals. The proposed method employs the discrete wavelet packet transform (DWPT) to decompose the raw signal into different sub-bands. The HI is extracted from each sub-band signal, smoothened using locally weighted regression, and evaluated using a gradient-based method. The HIs showing the best trends among all the sub-bands are iteratively accumulated to construct an HI with the best trend over the entire life of the bearing. The proposed method is tested on two benchmark bearing datasets. The results show that the proposed method yields an HI that correlates well with bearing degradation and is relatively easy to model.

scholarly journals Adapting Approximate Entropy as a Health Indicator of Rotating Machinery for Estimation of Remaining Useful Life

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Cody Walker
Keyword(s):  
Health Monitoring ◽  
Health Indicators ◽  
Approximate Entropy ◽  
Remaining Useful Life ◽  
Frequency Content ◽  
Health Indicator ◽  
Vibration Signals ◽  
Early Results ◽  
Useful Life ◽  
Significant Attention

Despite significant attention to online health monitoring and prognostics of bearings, many common health indicators are not sensitive to early stages of degradation. This research investigates the use of approximate entropy (ApEn), previously developed for fault diagnostics, as a health indicator for prognostics. ApEn quantifies the regularity of a signal; as bearings degrade, the frequency content of vibration signals changes and affects the ApEn as the vibration becomes more chaotic. Early results suggest ApEn supports earlier degradation detection and more predictable progression from fault to failure. This research focuses on optimizing parameters of the ApEn calculation to provide guidance across a variety of bearing types, sizes, and geometries in both steady-state and transient operation.

Deep Learning-based Health Indicator for Better Bearing RUL Prediction

2021 ◽  
Vol 263 (6) ◽  
pp. 493-498
Author(s):  
Taewan Kim ◽  
Seungchul Lee
Keyword(s):  
Domain Knowledge ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Health State ◽  
Health Indicator ◽  
Prognostic Performance ◽  
High Level ◽  
Processing Techniques ◽  
Signal Processing Techniques

The prognostic performance of data-driven approaches closely depends on the features extracted from the measurement. For a high level of prognostic performance, features must be carefully designed to represent the machine's health state well and are generally obtained by signal processing techniques. These features are themselves used as health indicators (HI) or used to construct HIs. However, many conventional HIs are heavily relying on the type of machine components and expert domain knowledge. To solve these drawbacks, we propose a fully data-driven method, that is, the adversarial autoencoder-based health indicator (AAE-HI) for remaining useful life (RUL) prediction. Accelerated degradation tests of bearings collected from PRONOSTIA were used to validate the proposed AAE-HI method. It is shown that our proposed AAE-HI can autonomously find monotonicity and trendability of features, which will capture the degradation progression from the measurement. Therefore, the performance of AAE-HI in RUL prediction is promising compared with other conventional HIs.

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 Health indicator construction and remaining useful life prediction for space Stirling cryocooler

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989673
Author(s):  
Lei Song ◽  
Haoran Liang ◽  
Wei Teng ◽  
Lili Guo
Keyword(s):  
Life Prediction ◽  
Thermal Environment ◽  
Health Indicators ◽  
Degradation Process ◽  
Space Mission ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Indicator Method ◽  
Aerospace Applications ◽  
Useful Life

Stirling cryocoolers are widely used to refrigerate significant facilities in military and aerospace applications. However, under the influences of high-frequency piston motion and thermal environment deterioration, the refrigerating performance of Stirling cryocoolers will worsen inevitably, thus affecting the successful accomplishment of space mission. In this article, a methodology on assessing the performance of space Stirling cryocoolers is proposed, which involves the analysis of the failure mechanism, health indicator construction and remaining useful life prediction of the cryocooler. The potential factors affecting the refrigerating performance are discussed first. In view of these, three health indicators representing the degradation process of cryocoolers are constructed and then a multi-indicator method based on particle filter is proposed for remaining useful life prediction. Finally, the proposed method is validated by a Stirling cryocooler from one retired aircraft, and the results show that the constructed health indicators and remaining useful life prediciton approaches are effective for performance assessment of Stirling cryocooler.

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.

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