#4243. Deep learning based intelligent industrial fault diagnosis model

September 2026publication date
Proposal available till 28-05-2025
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Journal’s subject area:
Engineering
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Abstract:
In the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The proposed model operates on three major processes namely signal representation, feature extraction, and classification. The proposed model uses a Continuous Wavelet Transform (CWT) is for preprocessed representation of the original vibration signal. Finally, a multilayer perceptron (MLP) is applied as a classification technique to diagnose the faults proficiently. Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset. The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6% and 99.64% on the applied gearbox dataset and bearing dataset. The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.
Keywords:
Deep learning; Fault diagnosis; Feature extraction; Industrial control; Intelligent models

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