This study aims to increase machine reliability thereby preventing product defects from adhesive dispense and slider attachment by a fault detection and diagnostic technique. The experiment was set up to investigate the vibration signal and motor current. Six fault conditions of a linear bearing were set up. The approaches, including spectrum analysis, crest factor, and analysis of variance, are used for data analysis. It was found that the spectrum analysis was suitable for classifying the frequency domains and the statistics tool was successful in measuring the current. Fault detection and diagnosis results can forecast the status of the linear bearings.

This research presents the novel approach for linear bearing fault detection by using Artificial Neural Network (ANN) based on observable information for high-speed automation machine. The dynamics modelling of feed drives and servo system design using pole placement technique were established to support the propose method. Three conditions of linear bearings which included healthy, 50 % of lubrication oil and starved lubrication were set up. Feature extraction of the data was analyzed by statistical approach. The results explains clearly that the control system design has a performance for tracking response and the ANN model can achieved 99.7 % accuracy by using the Levenberg Marquardt algorithm.

The fault tolerant control (FTC) technique is widely used in many industries to provide tolerance to systems so that they can operate when a system fault occurs. This paper presents a technique for FTC based on the observer signal application, which is used for a high-speed auto core adhesion mounting machine. The utilization of the observer signal information of the linear encoder fault is employed to adjust the gain parameters to achieve the appropriate gain value while maintaining the required performance of the system. The dynamic modeling of the servo motor system design utilizing a pole placement technique was designed to support the proposed method. A scaling gain fault step size adjustment from −1% to 1% with increments of 0.2% is used to simulate the fault conditions of the linear encoder. The statistical mean value of the observer error signal is used to train the artificial neural network (ANN) model. The results showed that the control system design successfully tracked the dynamic response. Furthermore, the ANN model, with more than 98% confidence, was satisfactory in classifying the linear encoder fault condition. The gain compensation was successful in reducing position error by more than 95% compared with the system without compensated gain.

This paper presents the design of a fuzzy-controller-based ultra-high vacuum pressure control system and its performance evaluation for a sputter-ion vacuum pump used in the electron storage ring at the Synchrotron Light Research Institute (Public Organization) in Thailand. The production of synchrotron light requires advanced vacuum technology to maintain stability and prevent interference of electrons in an ultra-high vacuum pressure environment of about 10−9 Torr. The presence of heat and gas rupture from the pipe wall can affect the quality of the light in that area. The institute currently uses a sputter-ion vacuum pump which is costly and requires significant effort to quickly reduce pressure increases in the area. Maintaining stable vacuum pressure throughout electron motion is essential in order to ensure the quality of the light. This research demonstrates a procedure for evaluating the performance of a sputter-ion vacuum pump using a mathematical model generated by a neural network and Molflow+ software. The model is used to estimate the pumping speed of the vacuum pump and to design a fuzzy control system for the ultra-high vacuum system. The study also includes a leakage rate check for the vacuum system.

Fault Detection and Classification (FDC) based on Machine Learning (ML) approach was used to detect and classify mount head fault in the slider attachment process which causes the machine alarm 71 to occur which leads to 2% of machine downtime. This paper has focused on the use of classified pixel surface of mount head with fault difference conditions including Healthy, Fault I, Fault II, and Fault III to detect and diagnose mount head before a vacuum leak. The Artificial Neural Network (ANN) algorithm was a proposed classification model and has to be evaluated before using in the real processes. Three features of mount head surface pixel, i.e., inner, outer, and overall areas were investigated and used as model training data set. The experiment result indicates that the classification using the ANN model with three features performed with an accuracy of 94.3%. According to the result, it was found that the reliability of the production processes of FDC technique has increased as a result of the reduction of machine downtime by 1.886%.