Classifier Inconsistency-Based Domain Adaptation Network for Partial Transfer Intelligent Diagnosis

Jiao, Jinyang; Zhao, Ming; Lin, Jing; Ding, Chuancang

doi:10.1109/tii.2019.2956294

Cited by 92 publications

(23 citation statements)

References 25 publications

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“…This is particularly not practical in industrial setups, where the set of input sensor signals is likely to be different across different machines, and the set of output labels(types of faults, RUL range) may also be different between different machines. Limited number of existing research works [160,161] focus on the case where the output label space is not identical in source and target.…”

Section: Transfer Learningmentioning

confidence: 99%

Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications

Fink¹,

Wang²,

Svensén³

et al. 2020

Preprint

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Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing and predicting faults of complex industrial assets has been limited. The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications.

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Section: Transfer Learningmentioning

confidence: 99%

Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications

Fink¹,

Wang²,

Svensén³

et al. 2020

Preprint

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“…Consequently, the PDA problem is better in line with the actual rotating machines health detection scenario, where the sample type of TD to be diagnosed is the subspace of the SD. Therefore, it is challenging and significant to deal with this PDA problem in intelligent fault diagnosis [22].…”

Section: Introductionmentioning

confidence: 99%

A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines

Liu

Wei

et al. 2021

IEEE Trans. Ind. Inf.

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“…Recently, the partial transfer problem has made initial progress in fault diagnosis. Jiao et al [ 27 ] applied weighted cross entropy loss to give smaller weight to the unique source samples, and such weight is determined by the predicted outputs of two classifiers [ 28 ]. Li et al [ 29 ] presented a weighted adversarial transfer network (WATN) which used adversarial training to reweight the source domain samples.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery

Jia

Wang

Zhang

et al. 2021

Entropy

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Domain adaptation-based models for fault classification under variable working conditions have become a research focus in recent years. Previous domain adaptation approaches generally assume identical label spaces in the source and target domains, however, such an assumption may be no longer legitimate in a more realistic situation that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of fault classes. To address the above deficiencies, we propose a partial transfer fault diagnosis model based on a weighted subdomain adaptation network (WSAN) in this paper. Our method pays more attention to the local data distribution while aligning the global distribution. An auxiliary classifier is introduced to obtain the class-level weights of the source samples, so the network can avoid negative transfer caused by unique fault classes in the source domain. Furthermore, a weighted local maximum mean discrepancy (WLMMD) is proposed to capture the fine-grained transferable information and obtain sample-level weights. Finally, relevant distributions of domain-specific layer activations across different domains are aligned. Experimental results show that our method could assign appropriate weights to each source sample and realize efficient partial transfer fault diagnosis.

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Classifier Inconsistency-Based Domain Adaptation Network for Partial Transfer Intelligent Diagnosis

Cited by 92 publications

References 25 publications

Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications

Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications

A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery

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