The effect of multi-sensor data on condition-based maintenance policies

Industry 4.0 promises reductions in maintenance costs through access to digital technologies such as the Internet of Things, cloud computing and data analytics. Many of the promised benefits to maintenance are, however, dependent on the quality of the data obtained through sensors and related technologies. In this work, we consider the effect of access to different levels of deterioration data quality, resulting in partial information about the underlying state of the system being monitored, by means of sensors, on condition-based maintenance policies. The sensors may be either internal company sensors, or more informative external sensors of which access is obtained at a cost. We analyze the structure of the optimal policy, where the actions are either to perform maintenance, to pay for external sensor information or to continue system operation with internal sensor information only. We show that the optimal policy consists of at most four regions based on the believed deterioration state of the system. The analysis allows us to numerically investigate the decision maker's willingness to pay for more informative external sensor information with respect to the level of external sensor informativeness, when compared to that of the internal sensor, and the cost thereof. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study examines the impact of data quality obtained through sensors on maintenance policies, showing that the quality of data influences the optimal decision-making process.