首页|A Transfer Learning Approach for Effective Motor Fault Identification of Industrial Machines used in Tile Manufacturing
A Transfer Learning Approach for Effective Motor Fault Identification of Industrial Machines used in Tile Manufacturing
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NSTL
Springer Nature
As the development and usage of autonomous machines and industrial IoT in manufacturing/production companies is becoming more active, machine fault identification has become a core component in any industrial environment. This research proposes a fault identification deep learning methodology to identify motor failures in industrial machines used in tile manufacturing. The deep learning model imports open-source machinery fault dataset of induction motors used in tile industries which contains parameters such as rpm, vibration, radial, axial and tangential direction values obtained using high-end sensors interfaced with an industrial test rig known as MFS-Machinery Fault Simulator. Furthermore, the model is fine-tuned using transfer learning techniques using pre-trained networks, and the performance of the model is assessed using accuracy metrics like Kappa Statistic: K, Overall Accuracy: OA and Average Accuracy: AA. The accuracy rate of 97.61% proves the effectiveness of the proposed fault identification model, thereby ensuring proficient and smooth operation of industrial machines.
Deep learningCondition monitoringFault identificationTransfer learningCLASSIFICATION
NSTL
Springer Nature
