Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review

Zheng, Huailiang; Wang, Rixin; Yang, Yuantao; Yin, Jun; Li, Yongbo; Li, Yuqing; Xu, Min

doi:10.1109/access.2019.2939876

Cited by 138 publications

(60 citation statements)

References 114 publications

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“…The bearing is the broadly applied topic for machinery fault detection or anomaly detection. The reason may be the readily available public dataset [43]. Some of the accessible opensource bearing dataset used for the bearing fault detection and diagnosis are as follows:…”

Section: Datasetmentioning

confidence: 99%

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Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset. INDEX TERMS Bearing, deep learning, machine learning, machinery fault detection and diagnosis, CWRU dataset.

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Section: Datasetmentioning

confidence: 99%

“…4 (c). The rotational speed was kept constant at 2000 rpm [43]. This database consists of three different datasets.…”

Section: E Ims Datasetmentioning

confidence: 99%

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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“…Exploiting large reference datasets is a well-established approach in Computer Vision [8] and Natural Language Processing [9]. In these fields, the commonly used deep learning approaches typically require a large number of training samples, which are not always available.…”

Section: Introductionmentioning

confidence: 99%

“…In these fields, the commonly used deep learning approaches typically require a large number of training samples, which are not always available. By leveraging weights learned from large reference datasets to enhance learning on a target or query dataset [10], transfer learning (TL) models such as image-net [11] and BERT [12] have revolutionized analysis approaches [8,9]: TL has improved method performance with small datasets (e.g., clustering [13], classification/annotation [14]) and enabled sharing of models using model zoos [15][16][17]. Recently, transfer learning has been applied on scRNA-seq for denoising [18], variance decomposition [19], and cell type classification [20,21].…”

Section: Introductionmentioning

confidence: 99%

Query to reference single-cell integration with transfer learning

Lotfollahi

Naghipourfar

Luecken

et al. 2020

Preprint

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Large single-cell atlases are now routinely generated with the aim of serving as reference to analyse future smaller-scale studies. Yet, learning from reference data is complicated by batch effects between datasets, limited availability of computational resources, and sharing restrictions on raw data. Leveraging advances in machine learning, we propose a deep learning strategy to map query datasets on top of a reference called single-cell architectural surgery (scArches, https://github.com/theislab/scarches). It uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building, and the contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, and whole organism atlases, we showcase that scArches preserves nuanced biological state information while removing batch effects in the data, despite using four orders of magnitude fewer parameters compared to de novo integration. To demonstrate mapping disease variation, we show that scArches preserves detailed COVID-19 disease variation upon reference mapping, enabling discovery of new cell identities that are unseen during training. We envision our method to facilitate collaborative projects by enabling the iterative construction, updating, sharing, and efficient use of reference atlases.

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“…According to the review [13], several deep networks with specific structure and training strategies are applied to reduce the gap between the operating conditions. Zhang et al [14] presented the CNN model with dropout in the first layer, small batch size, and ensemble learning strategy.…”

Section: Introductionmentioning

confidence: 99%

Learn Generalization Feature via Convolutional Neural Network: A Fault Diagnosis Scheme Toward Unseen Operating Conditions

et al. 2020

Self Cite

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In recent years, Convolutional neural networks (CNNs) have achieved start-of-art performance in the fault diagnosis field. If there is no available information on the unseen operating conditions, the model trained on the seen operating condition cannot perform well. One of the feasible strategies is to enhance the generalization ability of the network on various seen operating conditions. We introduce the center loss to the traditional CNN and build an end-to-end fault diagnosis framework (called CNN-C). By minimizing the intra-class variations, center loss cluster the learned features across various seen operating conditions. With the joint supervision of the center loss and the softmax loss, the learned features of the same class could minimize the domain difference across various seen operating conditions while the features of different classes are separable. The generalization ability of network is improved on unseen operating conditions. Compared with the shallow methods and traditional CNN, the proposed method is promising to deal with the fault diagnosis tasks of the bearing and gearbox. INDEX TERMS Convolutional neural network, center loss, unseen operating condition, fault diagnosis, feature generalization.

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Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review

Cited by 138 publications

References 114 publications

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Query to reference single-cell integration with transfer learning

Learn Generalization Feature via Convolutional Neural Network: A Fault Diagnosis Scheme Toward Unseen Operating Conditions

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