Abstract

Manufacturing Industries nowadays are looking for a possible competitive advantage to increase their production line output while lowering costs by adopting condition-based maintenance (CBM). One of the initiatives taken to address this issue is to study the condition monitoring solutions which, are becoming more popular for predicting machine problems through Predictive Intelligence systems in production machines such as motors and drives. This will prevent sudden equipment failures and unscheduled downtime. Tapping the vibration signals at the various section of the motor and drives and analyzing them is one of the most effective methods for preempting and early detection of equipment failures. Vibration analysis is a process that detects a machine's vibration level and frequency and then analyses the machine and its components' health conditions. The development of IoT-based machines and drive vibration sensor detection helps to increase production efficiency by minimizing unscheduled machine downtime. The captured vibration signals are analyzed by using Sliding Window Technique to study the variations with respect to the
machine speed and running time. Based on the studies, the vibration of a machine increases as the speed of the machine increases but stabilizes over a period. Any continuous abnormal signals denote that the machines warrant further investigations and maintenance.

Tham Ming Sum

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Analysis of Continuous Vibration Monitoring on Frequency Domain Using Various Sensors