1D CNN-Based Transfer Learning Model for Bearing Fault Diagnosis Under Variable Working Conditions

Hasan, Junayed; Sohaib, Muhammad; Kim, Jong‐Myon

doi:10.1007/978-3-030-03302-6_2

Cited by 27 publications

(20 citation statements)

References 10 publications

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“…Except for the motivation under different operating conditions, there are some research works in which the diagnosis tasks between different fault degrees were considered [85], [90]- [93]. Zhang et al [90] validated their proposed bearing diagnosis method using data with different fault diameters and data with different fault diameters while different loads.…”

Section: B: Motivation 2: Addressing Cross-domain Fault Diagnosis Betmentioning

confidence: 99%

“…Zhang et al [90] validated their proposed bearing diagnosis method using data with different fault diameters and data with different fault diameters while different loads. References [85], [92], [93] also studied the performances of their methods on the diagnosis tasks between different fault degrees. Besides, diagnosis of incipient fault is a very important and difficult issue.…”

Section: B: Motivation 2: Addressing Cross-domain Fault Diagnosis Betmentioning

confidence: 99%

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

et al. 2019

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Data-driven fault diagnosis has been a hot topic in recent years with the development of machine learning techniques. However, the prerequisite that the training data and the test data should follow an identical distribution prevents the conventional data-driven diagnosis methods from being applied to the engineering diagnosis problems. To tackle this dilemma, cross-domain fault diagnosis using knowledge transfer strategy is becoming popular in the past five years. The diagnosis methods based on transfer learning aim to build models that can perform well on target tasks by leveraging knowledge from semantic related but distribution different source domains. This paper for the first time summarizes the state-of-art cross-domain fault diagnosis research works. The literatures are introduced from three different viewpoints: research motivations, cross-domain strategies, and application objects. In addition, the corresponding open-source fault datasets and several future directions are also presented. The survey provides readers a framework for better understanding and identifying the research status, challenges and future directions of cross-domain fault diagnosis. INDEX TERMS Cross-domain, domain adaptation, fault diagnosis, review, transfer learning.

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Section: B: Motivation 2: Addressing Cross-domain Fault Diagnosis Betmentioning

confidence: 99%

Section: B: Motivation 2: Addressing Cross-domain Fault Diagnosis Betmentioning

confidence: 99%

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

et al. 2019

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“…Data-driven condition monitoring and fault diagnosis of a bearing normally consist of data acquisition from the bearing, signal processing and data classification steps. However, due to several important factors, e.g., friction, clearance, and variable working conditions, the acquired vibration signals from these rolling bearings are non-linear and non-stationary, which makes extracting fault feature information a difficult task [ 3 , 9 , 10 , 11 , 12 , 13 , 14 ]. Specifically, when using popular feature extraction methods that analyze features from the time domain, frequency domain, or time-frequency domain, it is very difficult to identify the fault characteristics under variable working conditions [ 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions

Hasan

Sohaib

Kim

2020

Sensors

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Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions.

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“…Consequently, pattern generation from acquired signal domains using several signal-imaging techniques can also differentiate between health conditions for further classification [7]. Besides, several automated feature learning processes driven by deep learning-based algorithms have been studied to ease the inevitability of domain knowledge proficiency [9,10]. However, due to limited amount of data, these deep learning-based approaches are not capable of extracting meaningful features.…”

Section: Introductionmentioning

confidence: 99%

Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

Hasan

Kim

et al. 2020

Applied Sciences

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Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.

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1D CNN-Based Transfer Learning Model for Bearing Fault Diagnosis Under Variable Working Conditions

Cited by 27 publications

References 10 publications

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

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

A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions

Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

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