Title: Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump
Author: N.R. Sakthivel, V. Indira, Binoy B. Nair, V. Sugumaran
Department of Mechanical Engineering, Amrita School of Engineering, Amrita vishwa vidyapeetham, Ettimadai, Coimbatore – 641105, India.
Department of Mathematics, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry – 605107, India.
Department Electronics and Communication Engineering, Amrita School of Engineering, Amrita vishwa vidyapeetham, Ettimadai, Coimbatore – 641105, India.
Department of Mechatronics Engineering, SRM University, SRM Nagar, Kattankulathur – 603203, Kanchepuram District, India
Abstract: Monoblock 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 resulting in a significant reduction in life-time costs. It is clear that the fault diagnosis and condition monitoring of pumps are important issues that cannot be ignored. Machine learning-based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. There are set of related activities involved in machine learning approach namely, data acquisition from the monoblock centrifugal pump, feature extraction from the acquired data, feature selection, and finally feature classification. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through histogram feature extracted from vibration signals of good and faulty conditions of monoblock centrifugal pump. The performance of the proposed system is compared to that of a Naive Bayes-based system to validate the superiority of the proposed system.
Keywords: monoblock centrifugal pumps; C4.5 algorithm; decision trees; bearing faults; impeller faults; seal faults; cavitation; histogram features; fault diagnosis; condition monitoring; machine learning; vibration signals.
Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2011 Vol.2, No.1, pp.23 - 36
Date of acceptance: 24 Jan 2011
Available online: 23 Jul 2011