Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review
| Autor: | Rajkumar Porwal, Purushottam Gangsar, Ashok Atulkar, Vikas Singh |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning Condition monitoring Fault (power engineering) Fuzzy logic Industrial and Manufacturing Engineering Support vector machine Artificial Intelligence Robustness (computer science) Artificial intelligence business Software Induction motor |
| Zdroj: | Journal of Intelligent Manufacturing. 34:931-960 |
| ISSN: | 1572-8145 0956-5515 |
| DOI: | 10.1007/s10845-021-01861-5 |
| Popis: | The fault monitoring and diagnosis of industrial machineries are very significant in Industry 4.0 revolution but are often complicated and labour intensive. The application of artificial intelligence (AI) techniques have now been an important part of condition monitoring of the mechanical and electrical machines because of its fast computation, higher accuracy, and robustness in performance, reducing the dependence on experienced personnel with expert knowledge. This paper presents a review of applications of AI-based fault diagnosis techniques that have had demonstrated success when applied to various industrial machineries. The important literature published in the last twenty years (i.e., 2000 to 2020) have been reviewed and added. In this work, first, a brief of various AI techniques such as artificial neural networks (ANN), deep learning (DL), fuzzy logic (FL), and support vector machine (SVM) are added. The literature on AI-based diagnostics used for various industrial machines, such as induction motor, bearing, gear, and centrifugal pump, are added and discussed in detail. The observation, research gap, and new ideas have been discussed, followed by a conclusion. |
| Databáze: | OpenAIRE |
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