Title: Application of standalone system and hybrid system for fault diagnosis of centrifugal pump using time domain signals and statistical features
Authors: N.R. Sakthivel; Binoy B. Nair; V. Sugumaran; Rajakumar S. Roy
Addresses: Department of Mechanical Engineering, Amrita School of Engineering, Ettimadai, Amrita Vishwa Vidyapeetham, Coimbatore, Tamilnadu, 641105, India; Karpagam University, Eachanari Post, Coimbatore, Tamilnadu, 641021, India. ' Department of Electronics and Communication Engineering, Amrita School of Engineering, Ettimadai, Amrita Vishwa Vidyapeetham, Coimbatore, Tamilnadu, 641105, India. ' Department of Mechanical Engineering, SRM University, SRM Nagar, Kattankulathur, Kancheepuram District, Tamil Nadu, 603 203, India. ' School of Mechanical Sciences, Karunya University, Karunya Nagar, Coimbatore, Tamilnadu, 64114, India
Abstract: Centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus providing a significant reduction in life-time costs. Vibration-based condition monitoring and analysis using machine learning approach is gaining momentum. Vibration monitoring can identify a number of potential pump problems such as bearing fault, impeller fault, seal fault, loose joints or fasteners, and cavitation issues. This paper compares the fault classification efficiency of standalone decision tree classifier, standalone rough set classifier with hybrid systems such as decision tree-fuzzy classifier and rough set-fuzzy classifier. The results obtained using standalone systems are compared with the performance of hybrid systems. It is observed that standalone systems outperform the hybrid systems.
Keywords: centrifugal pumps; decision tree; fuzzy logic; rough sets; standalone systems; hybrid systems; fault diagnosis; condition monitoring; machine learning; vibration monitoring; fault classification.
International Journal of Data Mining, Modelling and Management, 2012 Vol.4 No.1, pp.74 - 104
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 27 Jan 2012 *