The belt drive system is commonly used to transmit power in different industrial systems to maintain high performance and safety. Online condition monitoring techniques (CMTs) are used to monitor the operational conditions of such systems. Vibration-based monitoring techniques (VMT) are among the CMTs that are used in the analysis and diagnosis of the state of a belt drive system. Machine learning techniques are integrated with the VMT based on Industry 4.0 aspects for vibration analysis and fault diagnosis. Most of these techniques are based on the collection of vibration data from the belt drive system under known normal and different known faulty operations. This enables a fault to be diagnosed when it is detected during the operation of a system. In this paper, a new condition monitoring and warning mechanism is proposed to monitor the operational conditions of a belt drive system. The mechanism is based on an integration of a logical analysis of data regression (LADR) with a residual control chart (RCC). It uses vibration data from the belt drive system under normal operation only. This mechanism exhibits better performance in fault detection and also in interpreting the root cause of the faults in a belt drive system. Experimental investigations on a belt drive test rig have been carried out to collect vibration data based on a design of experiment for operational factors during normal operation. The LADR-RCC is implemented to monitor the operation of the belt drive system and detect faulty states. The accuracy of LADR is compared with multiple linear regression-based RCC, support vector regression-based RCC and random forest-based RCC. The LADR-RCC demonstrates significant enhancements in fault detection. The advantage of LADR-RCC over other model-based RCC is that it finds the root cause of a fault that is experienced in the system.
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