A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction qualit...

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Main Authors: Xiaojia Wang, Cunjia Wang, Keyu Zhu, Xibin Zhao
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/15/1/83
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author Xiaojia Wang
Cunjia Wang
Keyu Zhu
Xibin Zhao
author_facet Xiaojia Wang
Cunjia Wang
Keyu Zhu
Xibin Zhao
author_sort Xiaojia Wang
collection DOAJ
description In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.
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spelling doaj.art-3c8ed9bf6d564ea0943e31144991e27c2023-12-01T00:51:34ZengMDPI AGSymmetry2073-89942022-12-011518310.3390/sym15010083A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning SystemXiaojia Wang0Cunjia Wang1Keyu Zhu2Xibin Zhao3Institute of Artificial Intelligence and Data Science, School of Management, Hefei University of Technology, Hefei 230009, ChinaInstitute of Software Systems and Engineering, School of Software, Tsinghua University, Beijing 100084, ChinaInstitute of Artificial Intelligence and Data Science, School of Management, Hefei University of Technology, Hefei 230009, ChinaInstitute of Software Systems and Engineering, School of Software, Tsinghua University, Beijing 100084, ChinaIn an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.https://www.mdpi.com/2073-8994/15/1/83fault diagnosisintelligent manufacturingfeature extractionTSK fuzzy systembroad learning system
spellingShingle Xiaojia Wang
Cunjia Wang
Keyu Zhu
Xibin Zhao
A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System
Symmetry
fault diagnosis
intelligent manufacturing
feature extraction
TSK fuzzy system
broad learning system
title A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System
title_full A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System
title_fullStr A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System
title_full_unstemmed A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System
title_short A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System
title_sort mechanical equipment fault diagnosis model based on tsk fuzzy broad learning system
topic fault diagnosis
intelligent manufacturing
feature extraction
TSK fuzzy system
broad learning system
url https://www.mdpi.com/2073-8994/15/1/83
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