Fault Diagnosis for Distributed Cooperative System Using Inductive Logic Programming

This paper proposes a learning and diagnosis method that can be applied immediately after a distributed system starts cooperative operation. The proposed method first learns behavioral rules for individual systems from their time series data, which are collected under independent operations. Then, anomality is detected and the system is diagnosed following the cooperative specification. The proposed method learns rules for individual systems based on ACEDIA, which is a kind of inductive logic programming; the rules are either transition rules or relationship rules that hold among variables at the same transition time. In a diagnostic phase, inconsistent rules and inconsistent specifications are obtained with ranking information against the diagnostic data, where ranking is performed through evaluation in terms of the generality on each rule and specification. We demonstrate that the proposed method correctly outputs the rules and specifications that are violated by diagnostic data. Moreover, in a case study on a simplified automotive system consisting of multiple control systems, the rules essentially related to the error were ranked higher.