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Residual Selection for Consistency Based Diagnosis Using Machine Learning Models

机译：使用机器学习模型进行基于一致性诊断的残差选择

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摘要

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.
机译：基于模型的诊断系统的通用体系结构是使用一组残差来检测和隔离故障。本文的动机是，在许多情况下，候选残差比检测和单个故障隔离所需要的要多，并且候选残差中性能变化的关键源是模型误差和噪声。本文提出了一种系统的方法，该方法可以从一组候选残差中选择具有良好诊断性能的子集。关键的贡献是机器学习模型（这里是随机森林模型）与特定于诊断的性能规范的组合，以选择高性能的残差子集。该方法应用于工业用例，汽车发动机，并显示了如何轻松控制诊断性能和残差数量之间的折衷。所使用的残差数量从原始的42个减少到仅12个，而不会失去明显的诊断性能。

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