INTELLIGENT FAULT DETECTION IN INDUCTION MOTOR USING VIBRATION SIGNAL
Dr. R. Samson Daniel 1
*,
Abishek J, Akshayaraja E, Bharani Kumar P, Boomishwar S
- 1Anna University Chennai, Associate Professor, Department of ECE, K. Ramakrishnan college of engineering, Tamilnadu, IN
Conclusion
In this paper, using a large corpus of malicious Facebook apps observed over a 9-month period, we showed that malicious apps differ significantly from benign apps with respect to several features. For example, malicious apps are a lot of more expected to share names with other apps, and they classically request less permission than gentle apps. Leveraging our clarifications, we improved FbDetector, an truthful classifier for detecting malicious Facebook applications. Most interestingly, we highlighted the emergence of app-nets—large groups of tightly linked applications that promote each other.
We have offered the first measurement-based characterization of the popularity and usage of third-party Facebook applications. We plan to extend this work with additional datasets, improved models, and study of more dynamic aspects such as application vitality on the social graph.
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Keywords: Induction Motor, Vibration Signals, Fault Detection, Signal Processing, Machine Learning, Predictive Maintenance.
Citation: Dr. R. Samson Daniel*, Dr. R. Samson Daniel ( 2025), INTELLIGENT FAULT DETECTION IN INDUCTION MOTOR USING VIBRATION SIGNAL . , 13(4): 1-4
Received: 15/11/2025; Accepted: 28/11/2025;
Published: 04/12/2025
Edited by:
Mr.ERES JOURNALSReviewed by:
Copyright: @eres journals.
*Correspondence: Dr. R. Samson Daniel , samson.rapheal@gmail.com
