RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

Yao, Xuejian; Zhu, Junjun; Jiang, Quansheng; Yao, Qin; Shen, Yehu; Zhu, Qixin

doi:10.1088/1361-6501/ad123c

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…The ability to extract a greater amount of resources from signals was only possible with processes that involved transforming the signal into images and applying filters and mechanisms that managed to reduce noise signals. In the method used by [70], noise is eliminated through the use of a variation of autoencoders (CDAE), and Bi-LSTM is used to extract characteristics from the signals. The results were considered satisfactory for predicting the remaining useful life.…”

Section: Approach Based On Detection Of Anomalies and Failuresmentioning

confidence: 99%

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Santiago,

Barbosa,

Mergulhão

et al. 2024

Energies

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Wind energy has achieved a leading position among renewable energies. The global installed capacity in 2022 was 906 GW of power, with a growth of 8.4% compared to the same period in the previous year. The forecast is that the barrier of 1,000,000 MW of installed wind capacity in the world will be exceeded in July 2023, according to data from the World Association of Wind Energy. In order to support the expected growth in the wind sector, maintenance strategies for wind turbines must provide the reliability and availability necessary to achieve these goals. The usual maintenance procedures may present difficulties in keeping up with the expansion of this energy source. The objective of this work was to carry out a systematic review of the literature focused on research on the predictive and prescriptive maintenance of wind turbines based on the implementation of data-oriented models with the use of artificial intelligence tools. Deep machine learning models involving the detection, diagnosis, and prognosis of failures in this equipment were addressed.

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Section: Approach Based On Detection Of Anomalies and Failuresmentioning

confidence: 99%

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Santiago,

Barbosa,

Mergulhão

et al. 2024

Energies

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“…The LSTM network is a special kind of RNN, mainly used to solve the gradient vanishing and gradient explosion problems while training long sequences; thus, LSTM can work better in long sequences. LSTM has been widely used for RUL prediction because it is suitable for time series prediction [37][38][39]. The primary cell structure of the LSTM network is shown in Figure 12, which effectively controls the consequences caused by accumulation by introducing cell states, forgetting gates, input gates, and output gates.…”

Section: Lstm Structure Detailsmentioning

confidence: 99%

RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

Fu,

Gao,

Zhang

2024

Mathematics

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Piezoelectric vibration sensors (PVSs) are widely used in high-temperature environments, such as vibration measurements in aero-engines, because of their high accuracy, small size, and high temperature resistance. Accurate prediction of its RUL (Remaining Useful Life) is essential for applying and maintaining PVSs. Based on PVSs’ characteristics and main failure modes, this work combines the Digital-Twin (DT) and Long Short-Term Memory (LSTM) networks to predict the RUL of PVSs. In this framework, DT can provide rich data collection, analysis, and simulation capabilities, which have advantages in RUL prediction, and LSTM network has good results in predicting time sequence data. The proposed method exploits the advantages of those techniques in feature data collection, sample optimization, and RUL multiclassification. To verify the prediction of this method, a DT platform is established to conduct PVS degradation tests, which generates sample datasets, then the LSTM network is trained and validated. It has been proved that prediction accuracy is more than 99.7%, and training time is within 94 s. Based on this network, the RUL of PVSs is predicted using different test samples. The results show that the method performed well in prediction accuracy, sample data utilization, and compatibility.

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“…Signal noise reduction is an important prerequisite for predicting the remaining useful life prediction of rolling bearings. Yao et al [15] proposed a denoising network model based on convolutional denoising autoencoder. The noise component was removed from the original data by stacking convolutional autoencoders, and the RUL of rolling bearings was predicted by the bidirectional long short-term memory network model.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

Cao,

Zhang,

Zhao

et al. 2024

Applied Sciences

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For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.

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RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

Cited by 15 publications

References 42 publications

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

RUL Prediction for Piezoelectric Vibration Sensors Based on Digital-Twin and LSTM Network

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

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