The fault diagnosis and condition monitoring of many electrical assets (e.g. induction machine, transformer, power converter, etc.) have critical importance for early diagnosis to prevent severe damage of the assets. Importantly, valuable electrical assets need continuous monitoring to enhance safety, reliability, and availability and to decrease the cost of maintenance. Therefore, many methods based on different techniques are employed as a fault prognosis and diagnosis of these electrical assets. Especially, artificial intelligence (AI) techniques have proven their significance in every field of digital technology. However, industrial machines, automation, and processes are the net frontiers of AI adaptation. So far, are there any successful practical applications of AI technologies for fault diagnosis and condition monitoring of electrical assets?
I can explain in more detail, but electrical (as opposed to electronic) systems, often have "transient" events that are sometimes of no significance and other times indicators of faults or emerging faults. Monitoring such systems requires methods generally unknown in the field of AI. I have attached a short publication on the necessary technology and I can provide more information on its implementation if readers are interested.
Dear Zhenyu Zhao
These articles might help, have a look:
1. http://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20210501.12.pdf
2. https://mdpi-res.com/d_attachment/sensors/sensors-21-04024/article_deploy/sensors-21-04024-v2.pdf
3. https://www.hindawi.com/journals/sv/2020/8843759/
4. Article Condition monitoring of electrical assets using digital IRT ...
5. https://cjme.springeropen.com/articles/10.1186/s10033-021-00570-7
6. Article Sounds and Acoustic Emission-based Early Fault Diagnosis of ...
Kind Regards
Qamar Ul Islam
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