Data-driven imbalance and hard particle detection in rotating machinery using infrared thermal imaging

Currently, temperature-based condition monitoring cannot be used to accurately identify potential faults early in a rotating machines' lifetime since temperature changes are only detectable when the fault escalates. However, currently only point measurements, i.e. thermocouples, are used. In this article, infrared thermal imaging is used which - as opposed to simple thermocouples - provides spatial temperature information. This information proves crucial for the identification of several machine conditions and faults. In this paper the conditions considered are outer-raceway damage in bearings, hard-particle contamination in lubricant and several gradations of shaft imbalance. The fault detection is done using an image processing and machine learning solution which can accurately detect the majority of the faults and conditions in our data set.