The multisensor information fusion-based deep learning model for equipment health monitor integrating subject matter expert knowledge
Nowadays, the modern production machines are usually equipped with advanced sensors to collect the data which can be further analyzed because of the advent of Industry 4.0. This study proposes a novel deep learning (DL) information fusion-based framework collaborating convolutional neural network (C...
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| Vydáno v: | Journal of intelligent manufacturing Ročník 35; číslo 8; s. 4055 - 4069 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.12.2024
Springer Nature B.V |
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| ISSN: | 0956-5515, 1572-8145 |
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| Abstract | Nowadays, the modern production machines are usually equipped with advanced sensors to collect the data which can be further analyzed because of the advent of Industry 4.0. This study proposes a novel deep learning (DL) information fusion-based framework collaborating convolutional neural network (CNN) architecture with subject matter expert (SME) for equipment health monitor. The author integrates the unsupervised learning with supervised learning strategies bringing several benefits. Unsupervised learning assists in identifying the underlying patterns and relation within data without the need for labeled data, while supervised learning trains the model by the labeled data to derive prediction results. Also, due to sensor data characteristics, this study develops the independent CNN-based backbone net to extract the features of multisonsor data and to allow the proposed architecture to flexibly adopt arbitrary number of sensors attached to the equipment. An empirical study is conducted to demonstrate the effectiveness and the practice viability of the proposed framework. The resulting outcomes show that the proposed algorithm has superior performance than other machine learning models. One could adopt the general framework to maintain the performance of the equipment. |
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| AbstractList | Nowadays, the modern production machines are usually equipped with advanced sensors to collect the data which can be further analyzed because of the advent of Industry 4.0. This study proposes a novel deep learning (DL) information fusion-based framework collaborating convolutional neural network (CNN) architecture with subject matter expert (SME) for equipment health monitor. The author integrates the unsupervised learning with supervised learning strategies bringing several benefits. Unsupervised learning assists in identifying the underlying patterns and relation within data without the need for labeled data, while supervised learning trains the model by the labeled data to derive prediction results. Also, due to sensor data characteristics, this study develops the independent CNN-based backbone net to extract the features of multisonsor data and to allow the proposed architecture to flexibly adopt arbitrary number of sensors attached to the equipment. An empirical study is conducted to demonstrate the effectiveness and the practice viability of the proposed framework. The resulting outcomes show that the proposed algorithm has superior performance than other machine learning models. One could adopt the general framework to maintain the performance of the equipment. |
| Author | Dang, Jr-Fong |
| Author_xml | – sequence: 1 givenname: Jr-Fong orcidid: 0000-0003-1034-677X surname: Dang fullname: Dang, Jr-Fong email: jfdang@mail.ntust.edu.tw organization: Graduate Institute of Intelligent Manufacturing Technology, National Taiwan University of Science and Technology |
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| CitedBy_id | crossref_primary_10_1088_1361_6501_ad57de crossref_primary_10_1007_s00521_025_11065_0 crossref_primary_10_1016_j_eswa_2025_128406 crossref_primary_10_1016_j_aei_2025_103167 crossref_primary_10_1007_s10845_024_02499_9 |
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| ContentType | Journal Article |
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