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A novel optimized fault prediction in magnetic bearing using shaft vibration image database

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Abstract

The magnetic bearing is effectively employed in mechanical applications to run the device or particular applications. However, some faults have to be considered because those faults can reduce the bearing life and damage the shaft. Hence, several mechanisms have been introduced using vibration image data to predict the bearing faults. However, image complexity has made fault prediction and classification difficult. So, the present research work has aimed to design a novel chip-based modular neural model (CbMNM) for forecasting the faulty vibration signal of the magnetic bearing from the image data. The bearing vibration data were initially trained to the system, and the noise features were removed in the preprocessing layer. Moreover, the error-free data entered the classification phase, and then feature extraction and faulty signal prediction were performed. Finally, amplifier fault and base abnormal motion (BAM) are the fault types. Furthermore, the performance metrics have been calculated and compared with other associated model and have achieved the finest results. A novel chip-based modular neural model (CbMNM) was used for bearing fault prediction.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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  1. Mechanical Engineering Department, AISSMS College of Engineering, Pune, Maharashtra, 411001, India

    Priya Gajjal & M. R. Dahake

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  1. Priya Gajjal
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Correspondence to Priya Gajjal.

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Gajjal, P., Dahake, M.R. A novel optimized fault prediction in magnetic bearing using shaft vibration image database. Int. J. Dynam. Control 11, 2058–2068 (2023). https://doi.org/10.1007/s40435-023-01157-x

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