Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion
Using multi-source sensing data based on the Internet of things and fusion in conjunction with fuzzy convolutional neural networks to classify and predict mechanical failures has emerged as a very pertinent area of research. This study presents a fuzzy convolutional neural network-based technique to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-04-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423000375 |
| _version_ | 1827994139718844416 |
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| author | Dharmendra Singh Rajput Gaurav Meena Malika Acharya Krishna Kumar Mohbey |
| author_facet | Dharmendra Singh Rajput Gaurav Meena Malika Acharya Krishna Kumar Mohbey |
| author_sort | Dharmendra Singh Rajput |
| collection | DOAJ |
| description | Using multi-source sensing data based on the Internet of things and fusion in conjunction with fuzzy convolutional neural networks to classify and predict mechanical failures has emerged as a very pertinent area of research. This study presents a fuzzy convolutional neural network-based technique to extract and diagnose the defect indicators utilizing the bearing dataset from Case Western Reserve University (CWRU). In addition, the proposed model's effectiveness is evaluated with the help of a model based on a direct convolution neural network. The strategy based on Fuzzification yielded superior results when applied to a fully linked layer for classification. When applied to the bearing dataset, the proposed approach produces an average classification accuracy of 99.87% using the fuzzy layer. The proposed method is evaluated compared to other methods based on machine learning and deep learning. The accuracy achieved using the proposed method is superior to that achieved using the existing approaches in every imaginable working environment. The proposed approach can be used to diagnose mechanical motor faults and provide earlier recommendations. |
| first_indexed | 2024-04-10T04:35:25Z |
| format | Article |
| id | doaj.art-ae35a09027fd4453b821ba19f0f6d945 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-10T04:35:25Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-ae35a09027fd4453b821ba19f0f6d9452023-03-10T04:36:02ZengElsevierMeasurement: Sensors2665-91742023-04-0126100701Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusionDharmendra Singh Rajput0Gaurav Meena1Malika Acharya2Krishna Kumar Mohbey3School of Information Technology and Engineering, VIT, Vellore, IndiaDepartment of Computer Science, Central University of Rajasthan, Ajmer, 305817, IndiaDepartment of Computer Science, Central University of Rajasthan, Ajmer, 305817, IndiaDepartment of Computer Science, Central University of Rajasthan, Ajmer, 305817, India; Corresponding author.Using multi-source sensing data based on the Internet of things and fusion in conjunction with fuzzy convolutional neural networks to classify and predict mechanical failures has emerged as a very pertinent area of research. This study presents a fuzzy convolutional neural network-based technique to extract and diagnose the defect indicators utilizing the bearing dataset from Case Western Reserve University (CWRU). In addition, the proposed model's effectiveness is evaluated with the help of a model based on a direct convolution neural network. The strategy based on Fuzzification yielded superior results when applied to a fully linked layer for classification. When applied to the bearing dataset, the proposed approach produces an average classification accuracy of 99.87% using the fuzzy layer. The proposed method is evaluated compared to other methods based on machine learning and deep learning. The accuracy achieved using the proposed method is superior to that achieved using the existing approaches in every imaginable working environment. The proposed approach can be used to diagnose mechanical motor faults and provide earlier recommendations.http://www.sciencedirect.com/science/article/pii/S2665917423000375Internet of thingsData fusionFault predictionConvolution neural networkFuzzification |
| spellingShingle | Dharmendra Singh Rajput Gaurav Meena Malika Acharya Krishna Kumar Mohbey Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion Measurement: Sensors Internet of things Data fusion Fault prediction Convolution neural network Fuzzification |
| title | Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion |
| title_full | Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion |
| title_fullStr | Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion |
| title_full_unstemmed | Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion |
| title_short | Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion |
| title_sort | fault prediction using fuzzy convolution neural network on iot environment with heterogeneous sensing data fusion |
| topic | Internet of things Data fusion Fault prediction Convolution neural network Fuzzification |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423000375 |
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