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

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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Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

Advances in Mechanical Engineering ◽

10.1177/1687814018784201 ◽

2018 ◽

Vol 10 (6) ◽

pp. 168781401878420 ◽

Cited By ~ 5

Author(s):

Shu-Fa Yan ◽

Biao Ma ◽

Chang-Song Zheng

Keyword(s):

Life Prediction ◽

Health Management ◽

Principal Component ◽

Remaining Useful Life ◽

Cumulative Distribution ◽

Kernel Principal Component Analysis ◽

Degradation Index ◽

Life Estimation ◽

Power Shift ◽

Useful Life

Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process–based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.

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Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learning

Eksploatacja i Niezawodnosc - Maintenance and Reliability ◽

10.17531/ein.2021.4.11 ◽

2021 ◽

Vol 23 (4) ◽

pp. 684-694

Author(s):

Chenchen Wu ◽

Hongchun Sun ◽

Senmiao Lin ◽

Sheng Gao

Keyword(s):

Transfer Learning ◽

Prediction Accuracy ◽

Remaining Useful Life ◽

Rolling Bearings ◽

Degradation Data ◽

Single Scale ◽

Spatial Pyramid Pooling ◽

High Prediction ◽

Useful Life ◽

Failure Types

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of immense importance in ensuring the safe and smooth operation of machinery and equipment. Although the prediction accuracy has been improved by a predictive model based on deep learning, it is still limited in engineering because lots of models use single-scale features to predict and assume that the degradation data of each bearing has a consistent distribution. In this paper, A deep convolutional migration network based on spatial pyramid pooling (SPP-CNNTL) is proposed to obtain higher prediction accuracy with self-extraction of multi-feature from the original vibrating signal. And to consider the differences of the data distribution in different failure types, transfer learning (TL) added with maximum mean difference (MMD) measurement function is used in the RUL prediction part. Finally, the data of IEEE PHM 2012 Challenge is used for verification, and the results show that the method in this paper has high prediction accuracy.

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

International Journal of Automation and Computing ◽

10.1007/s11633-020-1276-6 ◽

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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Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model

Applied Sciences ◽

10.3390/app11167175 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7175

Author(s):

Islem Bejaoui ◽

Dario Bruneo ◽

Maria Gabriella Xibilia

Keyword(s):

Principal Component Analysis ◽

Induction Motor ◽

Life Prediction ◽

Principal Component ◽

Component Analysis ◽

Remaining Useful Life ◽

Degradation Model ◽

Broken Rotor Bar ◽

Industrial Sectors ◽

Useful Life

Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.

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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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An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Machines ◽

10.3390/machines6030035 ◽

2018 ◽

Vol 6 (3) ◽

pp. 35 ◽

Cited By ~ 1

Author(s):

Hung-Cuong Trinh ◽

Yung-Keun Kwon

Keyword(s):

Scoring Function ◽

Principal Component ◽

Remaining Useful Life ◽

Filter Method ◽

Feature Construction ◽

Training Set ◽

Robust Solution ◽

Benchmark Datasets ◽

Useful Life ◽

Better Than

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

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Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

PeerJ Computer Science ◽

10.7717/peerj-cs.795 ◽

2021 ◽

Vol 7 ◽

pp. e795

Author(s):

Pooja Vinayak Kamat ◽

Rekha Sugandhi ◽

Satish Kumar

Keyword(s):

Deep Learning ◽

Anomaly Detection ◽

Short Term Memory ◽

Rotating Machinery ◽

Remaining Useful Life ◽

Operating Conditions ◽

Learning Models ◽

Vibration Data ◽

Degradation Data ◽

Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

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