Abstract

Recently, predictive maintenance of complex machines is becoming a vital issue in industry 4.0. This is because of inexplicably occur in high voltage rotating machine can give prominent impact towards the industrial economy. Therefore, an accurate predictive maintenance is needed in reducing the degree of damage to the industrial instruments. the current method was not sufficient to indicate satisfactory result to large industrial plants. Additional auxiliary techniques are required to develop predictive maintenance. This paper suggests some methods to optimize the model of Neural Network (ANN) using Particle Swarm optimization (PSO) and Spiral Dynamics Algorithm (SDA). The proposed predictive maintenance algorithm has been verified with actual data of ABC Motor. The ANN model was subsequently modified using PSO and SDA to provide an accurate and optimal network architecture with improved accuracy results. The result demonstrated in this paper by comparing the conventional ANN with optimized ANN with the value of Mean Square Error (MSE) and Root Mean Square Error (RMSE).

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.