Linear Bearing Fault Detection in Operational Condition Using Artificial Neural Network

• Main Authors: Lawbootsa Siwanu, Chommaungpuck Prathan, Srisertpol Jiraphon
• Format: Article
• Language: English
• Published: EDP Sciences 2019-01-01
• Series: ITM Web of Conferences
• Online Access: https://www.itm-conferences.org/articles/itmconf/pdf/2019/01/itmconf_amcse18_01004.pdf

Nowadays, Factors of a competition of Hard Disk Drive (HDD) industry have reduced the cost of manufacturing process via increasing the rate of productivity and reliability of the automation machine. This paper aims to increase the efficacy of Condition-Based Maintenance (CBM) of linear bearing in Auto Core Adhesion Mounting machine (ACAM). The linear bearing faults considered in three causes such as healthy bearing, one ball bearing damage and one ball bearing damage with starved lubricant. The Fast Fourier Transform spectrum (FFT spectrum) can be detected for linear bearing faults and Artificial Neural Network (ANN) method used to analyze the cause of linear bearing faults in operational condition. The experimental results show the potential application of ANN and FFT spectrum technique as Fault Detection and Isolation (FDI) tool for linear bearing fault detection performance. The accuracy and decision making of ANN is enough to develop the diagnostic method for automation machine in operational condition.