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In this era of Industry 4.0, there are continuing efforts to develop fault detection and diagnosis methods that are fully autonomous; these methods are self-learning, with little or no human intervention. This paper proposes a methodology for the autonomous diagnosis of the root cause of a detected fault in a complex processing system. The methodology comprises steps to detect and classify any newly encountered fault, classify the known faults, and find the root cause of the detected fault condition. The one-class support vector machine (SVM) model is used in the framework to detect the unlabeled fault, and the neural network is used for fault classification and root cause analysis. The developed methodology is capable of self-updating the fault database by detecting and diagnosing any new fault condition. A permutation algorithm is applied in the neural network framework to extract the variable’s contribution to the classified fault condition. Also, Spearman’s rank correlation approach is used to investigate and justify the data correlation and causation. The proposed framework is tested using a continuous stirred tank heater and the benchmark Tennessee Eastman process.

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