Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification
| Autor: | István Vajda, Máté Gyimesi, Amir Mosavi, Timon Rabczuk, Narjes Nabipour, Adrienn Dineva |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
energy conversion
business.product_category Computer science 020209 energy Big data soft computing 02 engineering and technology multiple fault detection computer.software_genre Fault (power engineering) drive systems and power electronics Fault detection and isolation big data rotating electrical machines 0202 electrical engineering electronic engineering information engineering General Materials Science Instrumentation multi-label classification Fluid Flow and Transfer Processes Soft computing Electric machine Multi-label classification business.industry Process Chemistry and Technology 020208 electrical & electronic engineering General Engineering Automation Signature (logic) electric machine Computer Science Applications fault severity machine learning fault classifiers Data mining data science business computer |
| Zdroj: | Applied Sciences Volume 9 Issue 23 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app9235086 |
| Popis: | Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment. |
| Databáze: | OpenAIRE |
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