The Fault and Anomaly Detection and Isolation (FADI) in Programmable Logic Controller (PLC) controlled systems is an important and challenging problem. In this thesis, we present an automated tool, called the Manufacturing Process Failure Diagnosis Tool (MPFDT) that can detect and isolate the faults and anomalies in the PLC controlled manufacturing systems effectively. MPFDT utilizes two independent knowledge-based process behaviour models of the manufacturing system to satisfy the FADI purpose. The fundamental idea is to detect the inconsistencies between the modelled and the observed manufacturing process behaviour. The first model is a Deterministic Finite-state Automaton (DFA) based control process model of the manufacturing system that is used to determine whether the observed state transition behaviour of the PLC control process is consistent with the modelled state transition behaviour or not. The second model is basically a set of Artificial Neural Network (ANN) based one-class classifiers that are used to identify whether any significant difference exist between the observed and the reference electrical power consumption profile of the manufacturing system or not. The experimental results show that the FADI accuracy rate of the proposed tool is very high (more than 98%).
