Acoustic characterization of a three-phase asynchronous machine under stator unbalance defects
Diagnosing faults in three-phase asynchronous machines (ASMs) is crucial in industrial environments, where non-invasive techniques such as acoustic analysis and thermography are preferred for detecting malfunctions in these machines. Acoustics offers a practical and effective means of identifying sp...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671125000658 |
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| Summary: | Diagnosing faults in three-phase asynchronous machines (ASMs) is crucial in industrial environments, where non-invasive techniques such as acoustic analysis and thermography are preferred for detecting malfunctions in these machines. Acoustics offers a practical and effective means of identifying specific sound signatures associated with various faults without the need for sensors mounted directly on the machine. Stator unbalance faults (SUF) generate distinctive acoustic signals that can be analyzed to anticipate faults. Methods based on the intelligent classification of machine sounds give good results in this area. However, despite this progress, there is still a need to build up a more extensive database and better classify faults according to various ASM parameters. Precise characterization of the impact of each fault, both on the machine and its power supply, can facilitate the classification of malfunctions and contribute to earlier and more accurate diagnosis. The goal of this article is to study and characterize the acoustic signal of the ASM supplied by an unbalanced three-phase source or with one phase missing, by means of a statistical analysis (SA) of the acoustic data to detect the first signs of failure and facilitate their classification on the basis of acoustic and electrical measurements. This study reveals that the total harmonics distortion (THD) of the acoustic emissions (AEs) is more significant than that of the stator current, thus the statistical size parameters including the root-mean-square (RMS) and standard deviation (σ) are more significant than the shape parameters as the kurtosis coefficient (kurtosis). |
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| ISSN: | 2772-6711 |