Residual Selection for Consistency Based Diagnosis Using Machine Learning Models

Abstract A common architecture of model-based diagnosis systems is to use a set of residuals to detect and isolate faults. In the paper it is motivated that in many cases there are more possible candidate residuals than needed for detection and single fault isolation and key sources of varying performance in the candidate residuals are model errors and noise. This paper formulates a systematic method of how to select, from a set of candidate residuals, a subset with good diagnosis performance. A key contribution is the combination of a machine learning model, here a random forest model, with diagnosis specific performance specifications to select a high performing subset of residuals. The approach is applied to an industrial use case, an automotive engine, and it is shown how the trade-off between diagnosis performance and the number of residuals easily can be controlled. The number of residuals used are reduced from original 42 to only 12 without losing significant diagnosis performance.