A systematic review of deep transfer learning for machinery fault diagnosis

Li, Chuan; Zhang, Shaohui; Qin, Yi; Estupinan, Edgar Alberto

doi:10.1016/j.neucom.2020.04.045

Cited by 309 publications

(118 citation statements)

References 133 publications

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“…By transferring knowledge that is learned in source tasks to a related target task, it aims to alleviate the issue of insufficient training data [5,25]. This has attracted a lot attention in machinery fault diagnostics, where, for example, changing operating conditions or external factors cause a shift in the CM data that is not reflected in the training dataset [26]. Means of domain adaption-a branch of transfer learning-have been widely used to address the challenge of adapting a model to new conditions [27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Rombach

Michau

Fink

2021

Sensors

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Reliable fault detection and diagnostics are crucial in order to ensure efficient operations in industrial assets. Data-driven solutions have shown great potential in various fields but pose many challenges in Prognostics and Health Management (PHM) applications: Changing external in-service factors and operating conditions cause variations in the condition monitoring (CM) data resulting in false alarms. Furthermore, novel types of faults can also cause variations in CM data. Since faults occur rarely in complex safety critical systems, a training dataset typically does not cover all possible fault types. To enable the detection of novel fault types, the models need to be sensitive to novel variations. Simultaneously, to decrease the false alarm rate, invariance to variations in CM data caused by changing operating conditions is required. We propose contrastive learning for the task of fault detection and diagnostics in the context of changing operating conditions and novel fault types. In particular, we evaluate how a feature representation trained by the triplet loss is suited to fault detection and diagnostics under the aforementioned conditions. We showcase that classification and clustering based on the learned feature representations are (1) invariant to changing operating conditions while also being (2) suited to the detection of novel fault types. Our evaluation is conducted on the bearing benchmark dataset provided by the Case Western Reserve University (CWRU).

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Section: Related Workmentioning

confidence: 99%

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Rombach

Michau

Fink

2021

Sensors

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“…Several new techniques ( Han et al, 2019a ; Wang et al, 2019a ; Wang and Liu, 2020 ) based on this class of DA approaches have been recently proposed in the PHM literature. Other references on DA and TF approaches in the context of fault diagnosis can be found in the recent review works of Li et al (2020) and Zheng H. et al, 2019 ; Zheng Z. et al, 2019 .…”

Section: Critique and Future Directionsmentioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

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“…Owing to the limited characteristics as well as the complex structure of pump itself, it is hard to accurately diagnose different types of faults only from the experts’ subjective experience and existing knowledge. As a typical representative in the progress of artificial intelligence, deep leaning-based technologies have drawn tremendous attention in the fields of intelligent fault diagnosis [ 10 , 11 , 12 ]. In place of the great dependence of traditional methods on the previous knowledge and experience, intelligent approaches accomplish the automatic feature extraction from the input signals.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

Tang

Zhu

Yuan

et al. 2020

Sensors

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As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing t-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.

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A systematic review of deep transfer learning for machinery fault diagnosis

Cited by 309 publications

References 133 publications

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

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