การวางแผนการซ่อมบำรุงเชิงพยากรณ์ของเกียร์ขับสายพานลำเลียงด้วยการวิเคราะห์แนวโน้มการสั่นสะเทือน

• Main Author: ธีรสิทธิ์ สุริยา
• Other Authors: ผศ.ดร.วรพจน์ เสรีรัฐ
• Format: Independent Study
• Language: Thai
• Published: เชียงใหม่ : บัณฑิตวิทยาลัย มหาวิทยาลัยเชียงใหม่ 2020
• Online Access: http://cmuir.cmu.ac.th/jspui/handle/6653943832/68944
• Institution: Chiang Mai University

This research purposed to reduce the repairing time when any problem occur, by finding the appropriate time to stop the machine so that its spare can be purchased in advance. Furthermore, it provided the time to prepare before full-repairing and the process could be progressed as soon as the machine becomes error. With this reason, the researcher used ISO 10816-3 as the standard to evaluate the condition of the machine, applying with Predictive Maintenance according to the real condition and maintenance and Gearbox for belt conveyor by Vibration Analysis. All methods began with data collection of vibration warning and block the unrequired signal out of the vibration warning set. Next was to gather the data from the aforementioned process for graphing the vibration trend of the machine. After that, do the prediction to find the appropriate time to stop the machine which results to purchase spares in advance and to prepare for the full-repairing so that the machine can be enter the full-repairing process as soon as it becomes error. As aforementioned method, it could be included that the forecast by exponential square was the most appropriate process when its forecasting result indicated the working duration at 58,000th hour that became the suitable time to stop the machine. At the same time, the Material Requirement Planning or MRP would be progressed at 56,900th hour and the maintenance of the machine would begin from the spare purchasing order to the delivery of machine entering support system. This process spent 1 months, decreasing from 3 months. In addition, the said result also leaded to the cost reduction from Maintenance for THB 7,479,472.