Condition Monitoring Optimization Part 2: Making Inference with Uncertain Data

Inspectioneering Journal, Nov/Dec 2021 Issue  

This article is Part 2 in a series of articles discussing Condition Monitoring Optimization, which aims to provide a framework for quantitatively optimizing inspection scope, techniques, and intervals based on historical inspection data and subject matter expertise, while also dynamically updating the inspection plan to maximize reliability and return on investment (ROI) as new information becomes available. Using this methodology, data collected through inspection can be used to improve confidence in the asset damage state and determine when additional data is required, when inspection adds little or no value, or when corrective maintenance is required. In Part 1 of this series, entitled “Condition Monitoring Optimization: Going Beyond Traditional CML Optimization” and published in the September/October issue of Inspectioneering Journal, it was assumed that inspection coverage and techniques were sufficient to capture the true damage state of the asset.

This article considers the problem of accurately characterizing the damage state of an asset experiencing local degradation in cases where:
A. inspection is conducted on a subset of the total area, or
B. when the inspection technique has some probability of failing
to detect damage when damage is present.