Telematics data-driven prognostics system for construction heavy equipment health monitoring and assessment Said, Hisham M.; Nicoletti, Tony
Construction heavy equipment is a valuable asset for construction and equipment rental companies, which requires continuous monitoring and assessment for potential failures. Predictive maintenance has recently been proposed to as an alternative to preventive maintenance strategy by scheduling maintenance tasks just before a predicted failure of the equipment. Such predictive approach is dependent on the existence of a data collection and analysis system that monitors the equipment performance, compares it to the previous history, and predicts the failure events before their occurrence. This paper presents the development and validation efforts of a data-driven prognostics system that utilizes timely collected telematics data to monitor the equipment health condition and predict its failure hazard. The system is designed to utilize equipment telematics data to develop regression-based Cox’s proportional hazards functions. Regression analyses are performed for the historical telematics data to develop time-varying hazard functions for the successive life intervals of the equipment to generate dynamic predictions of its failure events. Accordingly, the outcome of the system would be the predicted probability of the equipment failure event considering the timely collected telematics data. The proposed prognostics system was validated by developing the hazard functions of two fleets of dozers and backhoes that provided high fit to the observed data and high prediction accuracy for the testing data. For both analyzed fleets, higher predictive and data fitting performance were achieved for later life intervals due the increased reliability of failure prediction for equipment with longer survival lives.
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Attribution-NonCommercial-NoDerivs 2.5 Canada