Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique. (4th March 2020)

• Record Type: Journal Article
• Authors: Arunthavanathan, Rajeevan; Khan, Faisal; Ahmed, Salim; Imtiaz, Syed; Rusli, Risza
• Is Part Of: Computers & chemical engineering. Volume 134(2020)
• Publication Date: 2020-03-04
• Subjects: Shallow neural network -- One class neural network -- Unsupervised learning -- Fault detection and classification -- Tennessee Eastman process -- Cognitive model; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.106697
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: Fault detection and classifications using supervised learning algorithms are widely studied; however, lesser attention is given to fault detection using unsupervised learning. This work focused on the integration of unsupervised learning with cognitive modelling to detect and diagnose unknown fault conditions. It is achieved by integrating two techniques: (i) incremental one class algorithm to identify anomaly condition and introduce a new state of fault to the current fault states if an unknown fault occurs, and (ii) dynamic shallow neural network to learn and classify the fault state. The proposed framework is applied to the well-known Tennessee Eastman process and achieved significantly better results compared to results reported by earlier studies. Laboratory experiments are also performed using a pilot-scale system to test the validity of the approach. The results confirm the proposed framework as an effective way to detect and classify known and unknown faults in process operations.