Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques
Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/9/8/173 |
| _version_ | 1797523216157114368 |
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| author | Syed Muhammad Tayyab Steven Chatterton Paolo Pennacchi |
| author_facet | Syed Muhammad Tayyab Steven Chatterton Paolo Pennacchi |
| author_sort | Syed Muhammad Tayyab |
| collection | DOAJ |
| description | Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out. |
| first_indexed | 2024-03-10T08:39:19Z |
| format | Article |
| id | doaj.art-a0edbd4fc5e54b7fa0aafcb6c8e1620e |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T08:39:19Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-a0edbd4fc5e54b7fa0aafcb6c8e1620e2023-11-22T08:24:45ZengMDPI AGMachines2075-17022021-08-019817310.3390/machines9080173Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence TechniquesSyed Muhammad Tayyab0Steven Chatterton1Paolo Pennacchi2Department of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milan, ItalySpiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out.https://www.mdpi.com/2075-1702/9/8/173fault detectionfault severity level identificationartificial intelligence (AI)artificial neural network (ANN)K-nearest neighbors (KNN)features extraction |
| spellingShingle | Syed Muhammad Tayyab Steven Chatterton Paolo Pennacchi Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques Machines fault detection fault severity level identification artificial intelligence (AI) artificial neural network (ANN) K-nearest neighbors (KNN) features extraction |
| title | Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques |
| title_full | Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques |
| title_fullStr | Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques |
| title_full_unstemmed | Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques |
| title_short | Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques |
| title_sort | fault detection and severity level identification of spiral bevel gears under different operating conditions using artificial intelligence techniques |
| topic | fault detection fault severity level identification artificial intelligence (AI) artificial neural network (ANN) K-nearest neighbors (KNN) features extraction |
| url | https://www.mdpi.com/2075-1702/9/8/173 |
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