A statistical methodology for the design of condition indicators

Recent studies in the field of diagnostics and prognostics of machines have highlighted the key role played by non-stationarity - often in the form of cyclostationarity - or non-Gaussianity - often in the form of impulsiveness as characteristic symptoms of abnormality. Traditional diagnostic and prognostic indicators (e.g. the kurtosis) are however sensitive to both types of symptoms without being able to differentiate them. In an effort to investigate how the signal characteristics evolve with the different phases of machine components degradation, this paper proposes a new family of condition indicators able to track cyclostationary or non-Gaussian symptoms independently. A statistical methodology based on the maximum likelihood ratio is introduced as a general framework to design condition indicators. It arrives with the possibility of setting up statistical thresholds, as needed for a reliable diagnosis. The methodology is validated with numerically generated signals and applied to the dataset made available by NSF I/UCR Center for Intelligent Maintenance Systems (IMS). This particular application shows high potential for bearing prognostics by providing condition indicators able to describe different phases of the bearing degradation process.