Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method

Hu, Tao; Guo, Yiming; Gu, Liudong; Zhou, Yifan; Zhang, Zhisheng; Zhou, Zhiting

doi:10.1016/j.ress.2021.108265

Cited by 53 publications

(9 citation statements)

References 26 publications

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“…Rolling bearings [187][188][189][190][191][192][193][194], aircraft engines [195,196] addressing long-distance prediction problems. As a result, transformer has found applications in machinery prognostic tasks.…”

Section: Cutting-edge Methods In DLmentioning

confidence: 99%

“…Zhang et al [192] also utilized MMD to implement cross-domain alignment of features and proposed a dynamic model-assisted approach to handle scenarios with insufficient running-tofailure data. Considering the inherent properties of each domain, Hu et al [193] designed a deep feature disentanglement TL network to extract domain invariant features while reducing the negative impact of domain private representations on RUL prediction. Miao and Yu [194] adopted a more robust multi-kernel MMD to deal with cross-domain feature distribution transfer under different working conditions and fault modes.…”

Section: Tl Methods Considering Domain Shiftmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Zhou,

Yang,

Qian

et al. 2024

Meas. Sci. Technol.

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Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets. A crucial component of PdM is the remaining useful life (RUL) prediction for machines, which has garnered increasing attention. With the rapid advancements in industrial Internet of Things (IoT) and artificial intelligence (AI) technologies, RUL prediction methods, particularly those based on pattern recognition (PR) technology, have made significant progress. However, a comprehensive review that systematically analyzes and summarizes these state-of-the-art PR-based prognostic methods is currently lacking. To address this gap, this paper presents a comprehensive review of PR-based RUL prediction methods. Firstly, it summarizes commonly used evaluation indicators based on accuracy metrics, prediction confidence metrics, and prediction stability metrics. Secondly, it provides a comprehensive analysis of typical machine learning methods and deep learning networks employed in RUL prediction. Furthermore, it delves into cutting-edge techniques, including advanced network models and frontier learning theories in RUL prediction. Finally, the paper concludes by discussing the current main challenges and prospects in the field. The intended audience of this article includes practitioners and researchers involved in machinery PdM, aiming to provide them with essential foundational knowledge and a technical overview of the subject matter.

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Section: Cutting-edge Methods In DLmentioning

confidence: 99%

Section: Tl Methods Considering Domain Shiftmentioning

confidence: 99%

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Zhou,

Yang,

Qian

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The latter two have received the most attention. Taking the popular statistical learning techniques as the example, many methods has been applied in degradation monitoring, including supervised learning [ 28 ], unsupervised learning [ 29 ], transfer learning [ 30 ], statistical model [ 31 ], integrated learning [ 32 ], etc. For existing data problems, a large number of studies have also been carried out to reduce their impacts in the tasks of classification [ 33 , 34 ] and regression [ 5 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

An Adaptive Sampling Framework for Life Cycle Degradation Monitoring

Yin

Liu

Zhang

et al. 2023

Sensors

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Data redundancy and data loss are relevant issues in condition monitoring. Sampling strategies for segment intervals can address these at the source, but do not receive the attention they deserve. Currently, the sampling methods in relevant research lack sufficient adaptability to the condition. In this paper, an adaptive sampling framework of segment intervals is proposed, based on the summary and improvement of existing problems. The framework is implemented to monitor mechanical degradation, and experiments are implemented on simulation data and real datasets. Subsequently, the distributions of the samples collected by different sampling strategies are visually presented through a color map, and five metrics are designed to assess the sampling results. The intuitive and numerical results show the superiority of the proposed method in comparison to existing methods, and the results are closely related to data status and degradation indicators. The smaller the data fluctuation and the more stable the degradation trend, the better the result. Furthermore, the results of the objective physical indicators are obviously better than those of the feature indicators. By addressing existing problems, the proposed framework opens up a new idea of predictive sampling, which significantly improves the degradation monitoring.

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“…Que et al (2021) suggested gated recurrent used based RUL prediction. Hu et al (2022) proposed transfer learning method for RUL prediction. Shi et al (2021) proposed ensemble learning for prediction of bearing RUL.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimum prediction model of remaining useful life for rolling element bearing based on integrating optimize health indicator (OHI) and machine learning algorithm

Nistane

2022

WJE

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Purpose Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm. Design/methodology/approach Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC). Findings Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs. Originality/value Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.

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Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method

Cited by 53 publications

References 26 publications

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

An Adaptive Sampling Framework for Life Cycle Degradation Monitoring

Optimum prediction model of remaining useful life for rolling element bearing based on integrating optimize health indicator (OHI) and machine learning algorithm

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