A Novel Transfer Learning Method for Fault Diagnosis Using Maximum Classifier Discrepancy With Marginal Probability Distribution Adaptation

Jia, Sixiang; Wang, Jinrui; Han, Baokun; Zhang, Guowei; Wang, Xiaoyu; He, Jingtao

doi:10.1109/access.2020.2987933

Cited by 27 publications

(12 citation statements)

References 35 publications

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“…Mao et al [275] and Mao et al [274] presented other approaches for condition diagnosis and health index construction for bearings. Jia et al [277] combined the adversarial training of two classifiers with a feature extractor using the minimization of a similarity measure to determine the condition of bearings.…”

Section: E Combined Approachesmentioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.INDEX TERMS Condition diagnosis, condition prognosis, data-driven methods, fleet learning, prognostics and health management (PHM), similar system approach, similar system problem, transfer learning.

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Section: E Combined Approachesmentioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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“…Homogeneous Learning [148], [112], [28], [182], [29], [30], [31], [147], [32], [83], [153], [33], [84], [34], [183], [152], [36], [37], [38], [39] , [155], [40], [35] , , [41], [25], [42], [85], [43], [44], [60], [47], [48], [49], [65], [160], [86], [87], [89], [54], [91], [56], [81], [92], [58], [93], [162], [53], [94], [163], [61], …”

Section: Space-setting Referencesunclassified

“…12, only a few studies have been devoted to a deeper understanding of the above-mentioned TLRM scenario, and its related issues, via TL. For instance, with the aim of identifying the health conditions of Scenario References TIM [146], [148], [112], [28], [182], [29], [30], [31], [149], [147], [32], [150], [33], [84], [34], [183], [36], [37], [38], [196], [155], [40], [35], [157], [41], [25], [42], [159], [85], [43], [60], [46], [48], [49], [65], [190], [86], [87], [89], [50], [51], [52], [56], [81], [92], [58], [162],…”

Section: ) Application Categorizationmentioning

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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“…To verify the superiority of the ADIG framework, a bearing fault dataset from Shandong University of Science and Technology (SDUST) is adopted [30]; data are collected at variable speeds. The experimental setup for data collection is shown in Fig.…”

Section: B Case 2: Sdust 1) Dataset Descriptionmentioning

confidence: 99%

Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions

Chen

Shen

et al. 2022

IEEE Trans. Ind. Inf.

107

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Recently, various fault diagnosis methods based on domain adaptation (DA) have been explored to solve the problem of discrepancy between the source and target domains. However, given complex industrial scenarios, DA-based methods usually fail when the working conditions of machines are unseen, i.e., target data are unavailable during model training. In this work, a generic domain-regressive framework for fault diagnosis, namely, adversarial domain-invariant generalization (ADIG), is proposed. ADIG leverages multiple available domain data to exploit domain-invariant knowledge through adversarial learning between the feature extractor and the domain classifier. Simultaneously, the fault classifier generalizes the knowledge from the source-related domain to diagnose the unseen but related target domain signals. Moreover, customized strategies of feature normalization and adaptive weight are proposed to promote diagnosis performance. Comprehensive case studies show that ADIG achieves satisfactory diagnosis accuracy and robustness under unseen conditions, indicating that ADIG is a remarkably potential diagnosis tool for real-case industrial machines.

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A Novel Transfer Learning Method for Fault Diagnosis Using Maximum Classifier Discrepancy With Marginal Probability Distribution Adaptation

Cited by 27 publications

References 35 publications

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

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

Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions

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