A fault detection system based on unsupervised techniques for industrial control loops. Issue 4 (19th March 2019)

• Record Type: Journal Article
• Authors: Jove, Esteban; Casteleiro‐Roca, José‐Luis; Quintián, Héctor; Méndez‐Pérez, Juan Albino; Calvo‐Rolle, José Luis
• Other Names: Jiménez Alfredo guestEditor.; Herrero Álvaro guestEditor.
• Is Part Of: Expert systems. Volume 36:Issue 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-03-19
• Subjects: anomaly detection -- control system -- fault detection -- unsupervised techniques; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12395
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: This research describes a novel approach for fault detection in industrial processes, by means of unsupervised and projectionist techniques. The proposed method includes a visual tool for the detection of faults, its final aim is to optimize system performance and consequently obtaining increased economic savings, in terms of energy, material, and maintenance. To validate the new proposal, two datasets with different levels of complexity (in terms of quantity and quality of information) have been used to evaluate five well‐known unsupervised intelligent techniques. The obtained results show the effectiveness of the proposed method, especially when the complexity of the dataset is high.