State Reconstruction: Generating a Reference for Improved Diagnostics

Abstract

Mechanical systems across most industries are mortal instruments, they will fail due to use or otherwise. Staying ahead of such catastrophes are crucial, especially in mission critical scenarios where loss of life is a very real danger. In such cases, corrective maintenance is too risky and scheduled maintenance is often costly; thus the collective shift towards predictive maintenance. Until recent advancements in artificial intelligence and sensor networks, such a strategy would not be so achievable. The ‘predictive’ aspect of thus type of maintenance implies that anomalies and failures are expected to be forecast ahead of occurrence - this can be accomplished with well placed sensors and sufficiently trained correlation methods. However, aspects such as shifting operating modes and varying sensor ranges make it difficult to make predictions solely using raw sensor data. This paper will outline methods and technologies to estimate healthy states of the monitored system to aid in the detection of failures before they affect function. Mechanical systems across most industries are mortal instruments, they will fail due to use or otherwise. Staying ahead of such catastrophes are crucial, especially in mission critical scenarios where loss of life is a very real danger. In such cases, corrective maintenance is too risky and scheduled maintenance is often costly; thus the collective shift towards predictive maintenance. Until recent advancements in artificial intelligence and sensor networks, such a strategy would not be so achievable. The ‘predictive’ aspect of thus type of maintenance implies that anomalies and failures are expected to be forecast ahead of occurrence - this can be accomplished with well placed sensors and sufficiently trained correlation methods. However, aspects such as shifting operating modes and varying sensor ranges make it difficult to make predictions solely using raw sensor data. This paper will outline methods and technologies to estimate healthy states of the monitored system to aid in the detection of failures before they affect function.M