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Development of an AI-based FSA for real-time condition monitoring for industrial machine

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Abstract

Automated continuous condition monitoring of industrial electrical machines to identify internal faults has become one of the critical research areas for the past decade. Among various defects, early-stage identification of insulation failure in stator winding is of notable demand as it often occurs and accounts for 37% of the overall motor failures. Identifying the current signature at its embryonic stage will effectively improve industrial machinery’s downtime and repair costs. Recent advances in computational performance and sensor technology concede advanced systems for achieving these goals. The design of an AI-based fault signature analyzer (FSA) has been developed in this paper. FSA uses real-time stator current data in the time and frequency domain from healthy and faulty induction motors to train the various AI-based machine learning classifiers to identify health conditions using wavelets. Comparing machine learning algorithms such as artificial neural network, random forest, fuzzy logic, neuro-fuzzy logic, K-nearest neighbors is performed, and various performance attributes are quantified. A reliable, automatic fault signature from a motor current is thus analyzed using the fusion of a wavelet-based feature extraction technique and a capable knowledge-based efficient artificial intelligence (AI) approach.

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Acknowledgements

The authors would like to express special thanks of gratitude to Birla Institute of Technology and Science, Pilani— Hyderabad, for Additional Competitive Research Grant (BITS/GAU/ACRG/2019/H0595) support for a duration of 2 years.

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  1. Birla Institute of Technology & Science, Pilani, Hyderabad, Telangana, 500078, India

    Amar Kumar Verma, Pallav Devang Raval, Neha Rajagopalan, Vaishnavi Khariya & Radhika Sudha

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  1. Amar Kumar Verma
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  2. Pallav Devang Raval
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Correspondence to Amar Kumar Verma.

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Verma, A.K., Raval, P.D., Rajagopalan, N. et al. Development of an AI-based FSA for real-time condition monitoring for industrial machine. Neural Comput & Applic 34, 8597–8615 (2022). https://doi.org/10.1007/s00521-021-06741-w

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