Sparse filtering based domain adaptation for mechanical fault diagnosis

Zhang, Zhongwei; Chen, Huaihai; Li, Shunming; An, Zenghui

doi:10.1016/j.neucom.2020.02.049

Cited by 26 publications

(9 citation statements)

References 26 publications

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“…Other approaches that include MMD terms in the training or fine-tuning loss function can be found in [50], [163], [205], [206], [207], [208], [209], and [210] on condition diagnosis of bearings and in [211] and [212] on gearbox condition diagnosis. Other condition diagnosis approaches are presented in [213] and [214] on bearings and gearboxes, [215] on bearings and a crack rotating machinery dataset, and [216] on induction motors. Yang et al [179], Yang et al [217], and Guo et al [218] added an MMD term and a pseudo label term to the loss function.…”

Section: ) Other MMD 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: ) Other MMD 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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“…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%

“…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%

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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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“…DA has been intensively studied in recent years in the context of PHM applications. Proposed approaches include discrepancy-based [13][14][15], adversarial [7,16,17] and reconstruction based methods [18,19]. Most of the proposed approaches have been exclusively developed for ClosedSet DA where the source and target domain cover the same class set -see left column in Fig.…”

Section: Related Workmentioning

confidence: 99%

Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation

Rombach¹,

Michau²,

Fink³

2022

Preprint

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New operating conditions can result in a performance drop of fault diagnostics models due to the domain gap between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the label spaces of the two domains are not congruent. To improve the transferability of the trained models, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework based on a Wasserstein GAN for Partial and OpenSet&Partial domain adaptation. The main contribution is the controlled fault data generation that enables to generate unobserved fault types and severity levels in the target domain by having only access to the healthy samples in the target domain and faulty samples in the source domain. To evaluate the ability of the proposed method to bridge domain gaps in different domain adaption settings, we conduct Partial as well as OpenSet&Partial domain adaptation experiments on two bearing fault diagnostics case studies. The results show the versatility of the framework and that the synthetically generated fault data helps bridging the domain gaps, especially in instances where the domain gap is large.

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Sparse filtering based domain adaptation for mechanical fault diagnosis

Cited by 26 publications

References 26 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

Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation

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