Modeling Continuous Sensor Signals and Discrete Maintenance Events Using the Action Specific-Input Output Hidden Markov Model

Equipment downtime is a significant challenge for many industries. In oil extraction, downtime costs can be as high as $250 000 per day. To prevent downtime, technicians manually interact with the equipment or monitor its health using sensory signals. Sensory data indirectly ascertain equipment health, while manual actions (inspections or repairs) provide a direct and precise insight but are time-consuming and costly. Thus, efficiently leveraging sensory data and outcomes of manual actions to accurately estimate the health of their equipment while finding the critical time points to schedule repairs and minimize the overall downtime is a crucial challenge faced by industries. In this article, we present a novel joint modeling approach called the action specific-input output hidden Markov model (AS-IOHMM) that integrates real-time sensor data and discrete health state information obtained by manual actions to aid prognosis and decision making of industrial equipment. In contrast to existing models that assume nondecreasing degradation without considering maintenance actions, AS-IOHMM estimates the impact of different maintenance actions on equipment health by learning action-specific transition probability matrices. We assess the effectiveness of AS-IOHMM through a numerical case study and validate its performance using mud-pump maintenance and sensory data from an oil rig, demonstrating enhanced prognosis ability and cost reduction of 7–15% over existing methods.