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An efficient neural-network model for real-time fault detection in industrial machine

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Abstract

Induction machines have extensive demand in industries as they are used for large-scale production and, therefore, vulnerable to both electrical and mechanical faults. Automated continuous condition monitoring of industrial machines to identify these faults has become one of the key areas in research for the past decade. Among various faults, early-stage identification of insulation failure in stator winding is of significant demand as it is often occurring and accounts for 37% of the overall machine failures. Also, this fault, if identified at its incipient stage, can predominantly improvise machine downtime and maintenance cost. In the proposed work, stator current signal data in the time domain from the experimental setup of both healthy and faulty induction machines are used to train the artificial neural-network models in order to identify the machine’s condition. Reducing the time required to train the neural network, features are extracted from the raw current signal data and then fed to the classifiers. Various performance characteristics of eleven neural-network models such as the number of features, number of epoch runs, training time, activation functions, learning rate, model loss function, and accuracy concerning each model are quantified. Only a few neural networks could classify a healthy and a faulty induction machine with 94.73% efficiency on generalization the neural-network model with the raw data, whereas 98.43% efficiency with the statistical featured data.

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Acknowledgements

The authors would like to express special thanks of gratitude to BITS Pilani-Hyderabad campus for the Research Initiation Grant (RIG) support.

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Authors and Affiliations

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

    Amar Kumar Verma, Shivika Nagpal, Aditya Desai & Radhika Sudha

Authors
  1. Amar Kumar Verma
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  2. Shivika Nagpal
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  3. Aditya Desai
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  4. Radhika Sudha
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Correspondence to Amar Kumar Verma.

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Appendices

Appendix 1: machine parameters

The rated parameters of the three-phase squirrel cage induction motor.

Parameter

Symbols

Ratings

Rated voltage

Vs

400 V

Rated current

Is

7.3 A

Rated power

P

3.7 kW

Turns number per phase

N

432

Rated torque

T

22.7 N-m

Rated speed

N

1440 r/min

Frequency of power supply

F

50 Hz

Power factor

pf

0.82

Efficiency

\(\eta\)

86.3%

  1. The conversion ratio \(K_{N}\) of Hall effect transducer is 1:1000. A resistance of 100 Ω is connected in parallel with each of the transducers to safeguard against elevated surge current. Therefore, the rated stator normalized current is 0.73

Appendix 2: flowchart

Stop learning process for ANN when success in generalization decreases.

figure b

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Verma, A.K., Nagpal, S., Desai, A. et al. An efficient neural-network model for real-time fault detection in industrial machine. Neural Comput & Applic 33, 1297–1310 (2021). https://doi.org/10.1007/s00521-020-05033-z

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  • DOI: https://doi.org/10.1007/s00521-020-05033-z

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