Revolutionizing Intelligent Condition Monitoring: GAN-Enhanced Anomaly Detection with Maximum Entropy and Reward Function Modeling for Minimal Historical Data

Registration ID: IJNRD_218904

Published ID: IJNRD2405013

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Ramander Singh, Davesh Singh Som

Anomaly Detection Generative Adversarial Networks (GAN) Maximum

The current model for intelligent condition monitoring requires an extensive dataset with corresponding tags representing various health states for effective training. However, acquiring abnormal samples in certain real-world systems proves challenging. To address this, a novel method for anomaly detection in systems is introduced, which is trained without the need for abnormal samples. This innovative approach integrates a reward function model with both maximum entropy and generative adversarial networks (GAN). Initially, the GAN is trained using expert samples to generate virtual expert samples. Non-expert samples are then generated through a random strategy based on this foundation, forming a mixed sample set of both expert and non-expert samples. By incorporating the maximum entropy probability model, the reward function is calculated, and the optimal reward function is determined using the gradient descent method. Subsequently, the proposed model is trained using normal samples collected in the early stages and is later employed for detecting unknown states. The monitoring of the system involves observing the change in the difference index generated by the GAN with maximum entropy. Experimental analysis results confirm the efficacy of the method. In comparison to traditional algorithms, the proposed approach detects system anomalies at an earlier stage, with the difference index exhibiting a more rapid increase when anomalies occur.

Published Paper   E-Certificate

Ramander Singh & Davesh Singh Som (May-2024). Revolutionizing Intelligent Condition Monitoring: GAN-Enhanced Anomaly Detection with Maximum Entropy and Reward Function Modeling for Minimal Historical Data. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), a118-a132. https://ijnrd.org/papers/IJNRD2405013.pdf

Volume 9 Issue 5, May-2024

Pages : a118-a132

Paper Reg. ID: IJNRD_218904

Published Paper Id: IJNRD2405013

Downloads: 000122258

Research Area: Engineering

Author Type: Indian Author

Country: Ghaziabad, Uttarpradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405013.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405013

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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