Development of machine fault simulator for fault identifications of bevel gearbox by using condition indicator parameters

Vibration issues have increased in recent years due to automated processes, more rapid machinery, and more sensitive designs. These difficulties arise from the need for increased production combined with reduced operating, maintenance, and expenditures for investment. To address this issue, a machine faults simulator test rig under healthy and faulty conditions has been developed. An important component of a machine fault simulator is a gearbox widely used in both the military and the industrial sectors. Excessive service load, difficult working conditions, or inevitable fatigue can all lead to gear failures. Equipment failures will continue to deteriorate if they are not identified in a period potentially leading to significant financial loss or even disaster. For condition monitoring that connects fault diagnosis, and degradation assessment application, Condition indicators have been developed in the past few years. The construction of condition indicators is decisive for extracting informative fault information from the monitoring vibration signals. The novelty of condition indicators is to provide accurate information regarding the condition of the gearbox at different levels of damage. Various condition indicators are generally used for fault detection and diagnosis of the gearbox. Condition indicator techniques would assist users in opting for valuable condition indicators and obtain better fault diagnosis performance of the gearbox. The investigation shows that under a variety of operating situations, statistical time-domain features greatly increase the accuracy of defect identification when paired with comprehensive vibration signal processing strategies. By incorporating these signs into intelligent monitoring systems, condition-based maintenance is made easier, which lowers maintenance expenses and unplanned equipment failures. The results highlight how crucial it is to incorporate condition indicators into Industry 4.0 frameworks in order to improve machinery dependability and operational effectiveness.