AUTHORS: Prathan Chommaungpuck, Siwanu Lawbootsa, Jiraphon Srisertpol

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ABSTRACT: Nowadays, the major competition of Hard Disk Drive (HDD) industry is to reduce the cost of manufacturing process via increasing the rate of productivity and reliability of the automation machine. This study aims to increase the efficacy of Condition-Based Maintenance (CBM) of linear bearing in Auto Core Adhesion Mounting machine (ACAM). The linear bearing faults were considered in six fault conditions. The Fast Fourier Transform spectrum (FFT spectrum), motor current and crest factor can be detected for linear bearing faults. The Artificial Neural Network (ANN) method was used to analyze and classify the cause of linear bearing faults into operational condition. The experimental results showed the application of ANN as Fault Detection and Isolation (FDI) tool for linear bearing fault detection performance. The accuracy and decision making of ANN are enough to develop the diagnostic method for automation machine in operational condition.

KEYWORDS: Fault detection and isolation, Linear bearing, Artificial neural network, Fast Fourier transform spectrum, Condition-Based Maintenance

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