Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

Yang, Wu; Yao, Qingfeng; Ye, Kejiang; Xu, Cheng‐Zhong

doi:10.1007/s10766-019-00650-1

Cited by 22 publications

(8 citation statements)

References 27 publications

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“…In order to illustrate the advantage of the proposed method, the obtained results are compared with five other recent relevant methods on the FEMTO dataset. In particular, the sparse domain adaption network (SDAN) in [41], the method based on time empirical mode decomposition (EMD) and temporal convolutional networks (TCN) in [42], the gated dual attention unit (GDAU) in [43], the multiscale convolutional neural network (MSCNN) in [44], and the deep separable convolutional network (DSCN) in [45] are adopted and labeled as SDAN, EMD-TCN, GDAU, MSCNN and DSCN, respectively. The comparison results are indicated in Fig.…”

Section: State-of-the-art Comparisonmentioning

confidence: 99%

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Nie

Zhang

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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As a critical content of condition-based maintenance (CBM) for mechanical systems, remaining useful life (RUL) prediction of rolling bearing attracts extensive attention to this day. Through mining the bearing degradation rule from operating data, the deep learning method is often used to perform RUL prediction. However, due to the complexity of operating data, it is usually difficult to establish a satisfactory deep learning model for accurate RUL prediction. Thus, a novel convolutional neural network (CNN) prediction method based on similarity feature fusion is proposed. In this paper, the similarity features are extracted based on the correlation between statistical features and time series. After sensitive feature screening, eligible features are applied to develop a health indicator (HI), which can be used to define the bearing failure stages and reduces the complexity of the CNN model. Subsequently, a one-dimensional CNN is established to predict the RUL of bearing, and the HI is utilized to train the prediction model. The proposed approach is verified by FEMTO bearing datasets and IMS bearing datasets. And the experimental results reveal the superiority and effectiveness of the feature fusion-based CNN method in constructing HI and accurate RUL prediction.

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Section: State-of-the-art Comparisonmentioning

confidence: 99%

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Nie

Zhang

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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“…Some studies have successfully used TCN for time series prediction tasks. Literature proposed a deep learning method for RUL estimation based on time-empirical pattern decomposition and TCN [19]. The frame can effectively reveal the non-stationary characteristics of the bearing degradation signal.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction method based on gated dilation causal convolution

Sun

Zhang

et al. 2023

Preprint

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Time series modeling is key to predicting the remaining useful life (RUL) of operating equipment. However, the design of traditional convolutional neural networks cannot flexibly adapt to various time window sizes, and when dealing with long sequences, it needs to have a corresponding deep structure, which is prone to the problem of gradient disappearing. These defects make traditional convolutional neural networks have high prediction errors in mechanical RUL prediction, so this paper proposed a new gated dilation causal convolutional (GDCC) network with multi-feature squeeze excitation unit (MSEU) and sinusoidal position coding, and used it for RUL prediction of rolling bearings. Specifically, MSEU adaptively corrects feature responses from both local and global perspectives; Second, sinusoidal position encoding adds a global order relationship to the hidden layer features; Then, build and stack GDCC for further learning of the front layer features; Finally, the RUL of the rolling bearings is predicted by the Relu activation function. The proposed method was evaluated using vibration data from the accelerated degradation test of rolling bearings and the performance of this method was compared with several advanced predictive methods. Experimental results show that the proposed method is effective in improving the prediction accuracy of bearings RUL, and is better than some existing data-driven prediction methods.

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“…TCN has been used for RUL estimation as an alternative to RNN by [38]- [40] on the turbofan-engine degradation NASA C-MAPPS data-set [41]. Degradation estimation of bearings using TCN is evaluated and benchmarked on the PRONOSTIA bearing data-set [42] presented by [43], [44]. A method based on TCN for State of Health (SOH) and remaining number of charging cycles estimation of lithiumion batteries is presented by [45], evaluated on the NASA lithium-ion battery data-set [46].…”

Section: Introductionmentioning

confidence: 99%

Prognostics for Electromagnetic Relays Using Deep Learning

et al. 2022

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Electromagnetic Relays (Electromagnetic Relay (EMR)s) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate the State of Health or the Remaining Useful Life emphasises the limited analysis and understanding of expressive EMR degradation indicators, as well as accessibility and use of EMR life cycle data sets. Prioritising these open challenges, a deep learning pipeline is presented in a prognostic context termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a remaining useful switching actuations forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact-Voltage, Contact-Current). Monte-Carlo Dropout is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting mean absolute percentage error of ±12 % over the course of the entire EMR life. INDEX TERMSElectromagnetic relay, prognostics, prognostics and health management, predictive maintenance, remaining useful life, artificial intelligence, deep learning, temporal convolutional networks, Monte-Carlo dropout. ABBREVIATIONS AT Arcing time. BT Bounce time. CAE Convolutional auto encoder. CC Coil current. CI Contact current. CNN Convolutional neural network. CR Contact resistance. CT Closing time. CV Contact voltage. DCR Dynamic contact resistance. DI Degradation indicator. EI Exponential indexing. The associate editor coordinating the review of this manuscript and approving it for publication was Sajid Ali . EMR Electromagnetic relay. EMRUA Electromagnetic relay useful actuation pipeline. EOL End of life. FC Fully connected layer. GI Growing-sequence indexing. LI Linear indexing. LSTM Long-short-term-memory network. MAE Mean absolute error. MAPE Mean absolute percentage error. MCD Monte-carlo dropout. MVTD Multivariate time series data. NN Neural network. OT Over-travel time.

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Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

Cited by 22 publications

References 27 publications

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Remaining useful life prediction for rolling bearings based on similarity feature fusion and convolutional neural network

Remaining useful life prediction method based on gated dilation causal convolution

Prognostics for Electromagnetic Relays Using Deep Learning

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