An explainable artificial intelligence based approach for interpretation of fault classification results from deep neural networks. (15th March 2022)

• Record Type: Journal Article
• Authors: Bhakte, Abhijit; Pakkiriswamy, Venkatesh; Srinivasan, Rajagopalan
• Is Part Of: Chemical engineering science. Volume 250(2022)
• Publication Date: 2022-03-15
• Subjects: Process monitoring -- Deep learning -- Explainable artificial intelligence -- Shapley value -- Tennessee Eastman; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.117373
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: Deep neural networks are popular for fault detection and diagnosis. A key shortcoming of such black-box models is the difficulty in interpreting their results. We propose a variable attribution method for Deep neural networks-based fault diagnosis. The method identifies the most important variables whose values led to the neural network's result about the process state. We illustrate the benefits of the method using a numerical example and the Tennessee Eastman challenge problem. Abstract: Process monitoring is crucial to ensure operational reliability and to prevent industrial accidents. Data-driven methods have become the preferred approach for fault detection and diagnosis. Specifically, deep learning algorithms such as Deep Neural Networks (DNNs) show good potential even in complex processes. A key shortcoming of DNNs is the difficulty in interpreting their classification result. Emerging approaches from explainable Artificial Intelligence (XAI) seek to address this shortcoming. This paper proposes a method based on the Shapley value framework and its implementation using integrated gradients to identify those variables which lead a DNN to classify an input as a fault. The method estimates the marginal contribution of each variable to the DNN, averaged over the path from the baseline (in this case, the process' normal state) to the current sample. We illustrate the resulting variable attribution using a numerical example and the benchmark Tennessee Eastman process. Our results show that the proposed methodology provides accurate, sample-specific explanations of the DNN's prediction. These can be used by the offline model developer to improve the DNN if necessary. It can also be used by the plant operator in real-time to understand the black-box DNN's predictions and decide on operational strategies.