Sensor-driven decision models for equipment replacement and spare parts logistics
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abstract
A wide variety of decision models have been developed to support maintenance related logistical decisions. Most of the existing models focus on using failure time distributions acquired from reliability testing to develop decision strategies. Different degradation characteristics of individual components are not accounted for, resulting in inaccurate failure predictions and less sound decisions. We develop a sensor-driven decision model for supporting and dynamically updating equipment replacement and spare parts inventory decisions based on equipment health. This is achieved by incorporating sensory-updated remaining life distributions (RLDs) of individual components using a degradation modeling framework into existing renewal theory-based decision models.
