Unsupervised Health Indicator Construction by a Novel Degradation-Trend-Constrained Variational Autoencoder and Its Applications

Qin, Yi; Zhou, Jianghong; Chen, Dingliang

doi:10.1109/tmech.2021.3098737

Cited by 69 publications

(19 citation statements)

References 30 publications

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“…In many recent works, there exists a problem that HI fluctuates greatly, and it is impossible to determine an appropriate confidence interval, leading to low prediction accuracy [19]; At present, a 95% or 99% confidence interval is usually used, but it is too subjective to apply to all HI, resulting in poor generalization ability [6]. To solve these problems, Chen et al [20] proposed a quadratic functionbased deep convolutional auto-encoder (QFDCAE) constructed HI, which can automatically extract bearing HI from the original vibration signal without expert knowledge; Zhou et al proposed a [21] distribution contact ratio metric health indicator (DCRHI) that can well represent the degradation process and obtain a unified failure threshold, combined with the improved gated current unit (GRU), it can more accurately predict the remaining service life of the bearing; Qin et al [22] also proposed an HI construction method based on degradation trend constrained variable autoencoder (DTC-VAE), which can adaptively generate HIs with obvious degradation trend. These articles have made great contributions to the HI construction part of RUL prediction research, while in this paper, a different and more effective solution is proposed for HI construction and HI processing.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm

et al. 2023

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Due to the importance of bearings in modern machinery, the prediction of the remaining useful life (RUL) of rolling bearings has been widely studied. When predicting the RUL of rolling bearings in engineering practice, the RUL is usually predicted based on historical data, and as the historical data increases, the prediction results should be more accurate. However, the existing methods usually have the shortcomings of low prediction accuracy, large cumulative error and failure to dynamically give prediction results with the increase of historical data, which are not suitable for engineering practice.To address the above problems, a novel RUL prediction method is proposed. The proposed method consists of 3 parts: First, the multi-scale entropy-based feature -namely "average multi-scale morphological gradient power spectral information entropy (AMMGPSIE)"from the rolling bearings as the Health indicator (HI) is extracted to ensure all the fault-related information is well-included; Then, the HI is processed with the enhanced Hodrick Prescott trend-filtering with boundary lines (HPTF-BL) to ensure good performance and small fluctuation on the HI; Finally, the deterioration curve is predicted using an LSTM neural network and the improved Particle Filter algorithm that we proposed. The proposed method is validated using the experimental bearing degradation dataset and the casing data of a centrifugal pump bearing from an actual industrial site. Comparing the results with other recent RUL prediction methods, the proposed method achieved state-ofthe-art feasibility and effectiveness, conform to the needs of practical application of the project.

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Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm

et al. 2023

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“…In recent years, the anomaly detection algorithm based on deep learning has been an efective method and academic focus gradually [10][11][12][13][14][15][16][17][18][19][20][21][22]. Auto-Encoder (AE) is widely used for anomaly detection due to its excellent deep representation.…”

Section: Introductionmentioning

confidence: 99%

“…Te method innovatively constructs the HI of a distribution contact ratio metric health indicator (DCRHI) to represent the degradation process well and obtain a uniform failure threshold. Qin et al proposed a novel degradation-trend-constrained VAE (DTC-VAE) to construct the HI vector with the distinct degradation trend [22]. Compared with other typical unsupervised HI construction methods, this method can more easily determine the uniform failure threshold.…”

Section: Introductionmentioning

confidence: 99%

Multiworking Conditions Anomaly Detection of Mechanical System Based on Conditional Variational Auto-Encoder

Lei

Dong

et al. 2023

Shock and Vibration

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Existing anomaly detection models of mechanical systems often face challenges for the equipment under multiple working conditions: the learning model under a single working condition is challenging to adapt to new working conditions, and centralized learning of multicondition samples leads to too low detection accuracy. A multiworking condition variational auto-encoder (MW-CVAE) is proposed to solve the problem. Based on the variational auto-encoder model, the working conditions of the equipment are regarded as the input. The anomaly evaluation threshold of each independent working condition is established by the centralized learning of normal multiworking condition samples. At the same time, it is found that the representation of each working condition sample in the space of the hidden layer forms a distribution close to the prior probability, providing a theoretical basis for the separation and evaluation of working conditions. By comparing and verifying the CWRU, JNU, and PU datasets, the new method significantly improves anomaly detection (the F1-score value is increased by 18-19%) and can be widely used in anomaly detection mechanical systems with various discrete working conditions.

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“…Remaining useful life (RUL) prediction is a key technology in prognostics and health management (PHM) (Zeng et al , 2021; Y. et al , 2021; Ren et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of rolling bearings based on convolutional recurrent attention network

Zhang

Shao

et al. 2022

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Purpose The current studies on remaining useful life (RUL) prediction mainly rely on convolutional neural networks (CNNs) and long short-term memories (LSTMs) and do not take full advantage of the attention mechanism, resulting in lack of prediction accuracy. To further improve the performance of the above models, this study aims to propose a novel end-to-end RUL prediction framework, called convolutional recurrent attention network (CRAN) to achieve high accuracy. Design/methodology/approach The proposed CRAN is a CNN-LSTM-based model that effectively combines the powerful feature extraction ability of CNN and sequential processing capability of LSTM. The channel attention mechanism, spatial attention mechanism and LSTM attention mechanism are incorporated in CRAN, assigning different attention coefficients to CNN and LSTM. First, features of the bearing vibration data are extracted from both time and frequency domain. Next, the training and testing set are constructed. Then, the CRAN is trained offline using the training set. Finally, online RUL estimation is performed by applying data from the testing set to the trained CRAN. Findings CNN-LSTM-based models have higher RUL prediction accuracy than CNN-based and LSTM-based models. Using a combination of max pooling and average pooling can reduce the loss of feature information, and in addition, the structure of the serial attention mechanism is superior to the parallel attention structure. Comparing the proposed CRAN with six different state-of-the-art methods, for the predicted results of two testing bearings, the proposed CRAN has an average reduction in the root mean square error of 57.07/80.25%, an average reduction in the mean absolute error of 62.27/85.87% and an average improvement in score of 12.65/6.57%. Originality/value This article provides a novel end-to-end rolling bearing RUL prediction framework, which can provide a reference for the formulation of bearing maintenance programs in the industry.

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Unsupervised Health Indicator Construction by a Novel Degradation-Trend-Constrained Variational Autoencoder and Its Applications

Cited by 69 publications

References 30 publications

Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm

Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm

Multiworking Conditions Anomaly Detection of Mechanical System Based on Conditional Variational Auto-Encoder

Remaining useful life prediction of rolling bearings based on convolutional recurrent attention network

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