A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data

Zhao, Ke; Jiang, Hongkai; Wu, Zhenghong; Lu, Tengfei

doi:10.1007/s10845-020-01657-z

Cited by 55 publications

(22 citation statements)

References 37 publications

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“…Instance-based [25], [26], [27] Feature-based [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75],…”

Section: Approach Referencesmentioning

confidence: 99%

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

et al. 2023

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The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

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Section: Approach Referencesmentioning

confidence: 99%

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

et al. 2023

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“…The research on transfer learning in fault diagnosis applications has increased rapidly over the last few years, with many successful transfers between different lab datasets (Lei et al, 2020). As models improve, transfer learning can enable broader implementation of these models in process industry with a lower demand for labeled instances compared to supervised learning (Cao et al, 2018a) Zhao et al, 2020); auto-encoders are also used (Wen et al, 2019), and recently weak supervision and digital twin-based transfer learning (Xu et al, 2019) have been successfully implemented.…”

Section: Transfer Learningmentioning

confidence: 99%

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry

Karl

Taal²,

Schnabel³

et al. 2022

IJPHM

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In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions and work-orders written by domain experts are increasingly digitised in modern condition monitoring systems, for example in the context of rotating equipment monitoring. Thus, domain-specific knowledge about fault characteristics and severities exists as technical language annotations in industrial datasets. Furthermore, recent advances in natural language processing enable weakly supervised model optimisation using natural language annotations, most notably in the form of natural language supervision (NLS). This creates a timely opportunity to develop technical language supervision (TLS) solutions for IFD systems grounded in industrial data, for example as a complement to pre-training with lab data to address problems like overfitting and inaccurate out-of-sample generalisation. We surveyed the literature and identify a considerable improvement in the maturity of NLS over the last two years, facilitating applications beyond natural language; a rapid development of weak supervision methods; and transfer learning as a current trend in IFD which can benefit from these developments. Finally we describe a general framework for TLS and implement a TLS case study based on Sentence-BERT and contrastive learning based zero-shot inference on annotated industry data.

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“…Manifold learning: Several publications apply manifold learning techniques [135]- [137]. In context of fault diagnosis transfer, Zhao et al [136] apply manifold embedded distribution alignment (MEDA), a method originally proposed for TL with image data. Saeedi et al [49] propose a manifoldbased transfer method for cross-subject HAR.…”

Section: F1) Feature Transformationmentioning

confidence: 99%

Transfer Learning With Time Series Data: A Systematic Mapping Study

Weber

Auch

Doblander³

et al. 2021

IEEE Access

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Transfer Learning is a well-studied concept in machine learning, that relaxes the assumption that training and testing data need to be drawn from the same distribution. Recent success in applying transfer learning in the area of computer vision has motivated research on transfer learning also in context of time series data. This benefits learning in various time series domains, including a variety of domains based on sensor values. In this paper, we conduct a systematic mapping study of literature on transfer learning with time series data. Following the review guidelines of Kitchenham and Charters, we identify and analyze 223 relevant publications. We describe the pursued approaches and point out trends. Especially during the last two years, there has been a vast increase in the number of publications on the topic. This paper's findings can help researchers as well as practitioners getting into the field and can help identify research gaps.

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A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data

Cited by 55 publications

References 37 publications

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry

Transfer Learning With Time Series Data: A Systematic Mapping Study

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