Task-incremental broad learning system for multi-component intelligent fault diagnosis of machinery

Fu, Yang; Cao, Hongrui; Chen, Xuefeng; Ding, Jianming

doi:10.1016/j.knosys.2022.108730

Cited by 18 publications

(5 citation statements)

References 34 publications

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“…The gearbox dataset of Case 1 is provided by the University of Connecticut (UoC), 39,40 which has been extensively used to explore the performance of fault diagnosis methods. [41][42][43][44][45][46][47][48] In addition, Zhao et al 41 tested seven publicly available datasets through four benchmark types of deep learning models and showed that the UoC gearbox dataset is the most difficult to diagnose among the seven datasets. The gearbox data of Case 1 is used to study the universality and progressiveness of the proposed method.…”

Section: Case Studymentioning

confidence: 99%

Robust fault diagnosis of quayside container crane gearbox based on 2D image representation in frequency domain and CNN

Zhang

Qin

et al. 2023

Structural Health Monitoring

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To accurately diagnose the quayside container crane (QCC) gearbox faults, this article proposes a method that combines the frequency-domain Markov transformation field (FDMTF) and multi-branch residual convolutional neural network (MBRCNN). Firstly, the gearbox vibration signal is converted into the frequency domain to reveal the components and amplitude of signals stably and concisely. Then, the one-dimensional frequency signal is encoded into the two-dimensional image by the Markov transformation field to capture the dynamic characteristics of signals. Thirdly, the MBRCNN network is constructed, which can extract multi-scale features and alleviate the problems caused by the deep network structure. Finally, the FDMTF image is fed into the constructed MBRCNN model for pattern recognition. The effectiveness of the proposed FDMTF–MBRCNN method is verified by two case studies. In Case 1, the diagnosis results of a benchmark dataset achieve 100% accuracy, better than seven state-of-the-art methods published in recent 3 years. In Case 2, the diagnosis results of the dataset collected from a 1:4 scaled test rig achieve 98.85% accuracy, better than eleven encoding methods and four convolutional neural network methods. It also can obtain a recognition accuracy of more than 94% under the conditions of small sample, different network hyper-parameters, or variable loads, which verifies its robustness. These case studies show that the FDMTF–MBRCNN method is expected to be applied to the actual fault diagnosis of QCC gearboxes.

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Section: Case Studymentioning

confidence: 99%

Robust fault diagnosis of quayside container crane gearbox based on 2D image representation in frequency domain and CNN

Zhang

Qin

et al. 2023

Structural Health Monitoring

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“…Recently, reinforcement learning has been widely introduced into BLS for performance improvement [24][25][26]. In addition, BLS and its variants have been widely applied to many practical applications, such as industrial intrusion detection [27], driver fatigue detection [28], disease detection [29][30][31], tropical cyclogenesis detection [32], intelligent fault diagnosis [33][34][35], license plate recognition [36], indoor fingerprint localization [37], air quality forecasting [38], photovoltaic power forecasting [39], stock price prediction [40], traffic flow prediction [41], machinery remaining useful life prediction [42], static voltage stability index prediction [43], time series prediction [44][45][46][47] and many others.…”

Section: Related Workmentioning

confidence: 99%

RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure

Wei

Zhou

et al. 2023

Electronics

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Recently, the broad learning system (BLS) has been widely developed in virtue of its excellent performance and high-computational efficiency. However, two deficiencies still exist in BLS and preclude its deployment in real applications. First, the standard BLS performs poorly in outlier environments because the least squares loss function it uses to train the network is sensitive to outliers. Second, the model structure of BLS is likely to be redundant since the hidden nodes in it are randomly generated. To address the above two issues, a new robust and compact BLS (RCBLS), based on M-estimator and sparsity regularization, is proposed in this paper. The RCBLS develops from the BLS model and maintains its excellent characteristics, but replaces the conventional least squares learning criterion with an M-estimator-based loss function that is less sensitive to outliers, in order to suppress the incorrect feedback of the model to outlier samples, and hence enhance its robustness in the presence of outliers. Meanwhile, the RCBLS imposes the sparsity-promoting l2,1 -norm regularization instead of the common l2-norm regularization for model reduction. With the help of the row sparsity of l2,1-norm regularization, the unnecessary hidden nodes in RCBLS can be effectively picked out and removed from the network, thereby resulting in a more compact network. The theoretical analyses on outlier robustness, structural compactness and computational complexity of the proposed RCBLS model are provided. Finally, the validity of the RCBLS is verified by regression, time series prediction and image classification tasks. The experimental results demonstrate that the proposed RCBLS has stronger anti-outlier ability and more compact network structure than BLS and other representative algorithms.

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“…Consequently, BLS addresses the challenges of extended training periods and the interpretability deficit inherent in deep learning, an increasing number of scholars are employing BLS in the realm of fault diagnosis. Fu et al [21] proposed a Task-Incremental Broad Learning System (TiBLS) tailored for multi-component intelligent fault diagnosis. They introduced a structure-incremental learning capability for TiBLS to enhance individual tasks without retraining, yielding favorable results in experiments.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on Multi-Domain Feature Selection and the Fuzzy Broad Learning System

Wu,

Zhang,

Qin

et al. 2024

Processes

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In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced the Fuzzy Broad Learning System (FBLS) by replacing the feature nodes of BLS with fuzzy subsystems, thereby further reducing the training time. However, the traditional FBLS, with its straightforward structure, falls short in achieving higher fault diagnosis accuracy when handling raw vibration signals. This paper presents a bearing fault diagnosis approach employing multi-domain feature selection and the fuzzy broad learning system (MS-FBLS), aiming to enhance the diagnostic accuracy of FBLS through multi-domain feature selection. Primarily, a set of 49 features spanning time domain, frequency domain, time-frequency domain, and entropy values is extracted from the original vibrational signals. This combination builds a 49-dimensional multidomain feature set that exploits the information behind the input data as much as possible, thus compensating for the lack of feature extraction capability in FBLS. Afterward, the Random Forest algorithm assesses the significance of all features, leading to a reordering of the multidomain feature set based on their respective importance levels. Ultimately, the reorganized multidomain feature set is then fed into the FBLS, enabling the identification of various failure states within the bearing. The experimental validation conducted on the rolling bearing fault simulation test bed showcased that, in comparison to the traditional FBLS, the MS-FBLS method not only elevates diagnostic accuracy by 23.46%, but also substantially enhances diagnostic speed. These results serve as comprehensive evidence affirming the effectiveness of the method.

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Task-incremental broad learning system for multi-component intelligent fault diagnosis of machinery

Cited by 18 publications

References 34 publications

Robust fault diagnosis of quayside container crane gearbox based on 2D image representation in frequency domain and CNN

Robust fault diagnosis of quayside container crane gearbox based on 2D image representation in frequency domain and CNN

RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure

Bearing Fault Diagnosis Method Based on Multi-Domain Feature Selection and the Fuzzy Broad Learning System

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