Author
Tanya
Bio: Tanya is an academic researcher from Lovely Professional University. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has co-authored 1 publications.
Papers
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01 Jan 2019
TL;DR: S-Transformation, which is superior as compared to CWT and STFT as it does not contain any cross terms, is used for bearing fault detection, and random forest, an algorithm which is easy to implement and requires minimum memory, are used for detection of external faults.
Abstract: Induction motors are extensively used motor type for various industrial applications for the reason that they are robust, simple in structure, and efficient. On the other hand, induction motors are prone to different faults during their lifetime due to hostile environments. If the fault is not detected in its rudimentary phase, it may cause unexpected shut down of the entire system and colossal loss in industry. It is conspicuous that scope of this field is huge. This work presents detection of internal and external faults of induction motor. S-Transformation, which is superior as compared to CWT and STFT as it does not contain any cross terms, is used for bearing fault detection, and random forest, an algorithm which is easy to implement and requires minimum memory, is used for detection of external faults. The fault can be detected with more accuracy in premature state leads to improve the reliability of the system.