V. Sugumaran

Bio: V. Sugumaran is an academic researcher from VIT University. The author has contributed to research in topics: Condition monitoring & Feature extraction. The author has an hindex of 28, co-authored 117 publications receiving 2951 citations. Previous affiliations of V. Sugumaran include Amrita Vishwa Vidyapeetham & SRM University.

A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis

Abstract: In most of the industries related to mechanical engineering, the usage of pumps is high. Hence, the system which takes care of the continuous running of the pump becomes essential. In this paper, a vibration based condition monitoring system is presented for monoblock centrifugal pumps as it plays relatively critical role in most of the industries. This approach has mainly three steps namely feature extraction, classification and comparison of classification. In spite of availability of different efficient algorithms for fault detection, the wavelet analysis for feature extraction and Naive Bayes algorithm and Bayes net algorithm for classification is taken and compared. This paper presents the use of Naive Bayes algorithm and Bayes net algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different discrete wavelet families were calculated and compared to find the best wavelet for the fault diagnosis of the centrifugal pump.

Vibration based fault diagnosis of monoblock centrifugal pump using decision tree

Abstract: Monoblock centrifugal pumps are widely used in a variety of applications. In many applications the role of monoblock centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly artificial neural networks, fuzzy logic were employed for continuous monitoring and fault diagnosis. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through statistical feature extracted from vibration signals of good and faulty conditions.

Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine

Abstract: The shaft and bearing are the most critical components in rotating machinery. Majority of problems arise from faulty bearings in turn affect the shaft. The vibration signals are widely used to determine the condition of machine elements. The vibration signals are used to extract the features to identify the status of a machine. This paper presents the use of c-SVC and nu-SVC models of support vector machine (SVM) with four kernel functions for classification of faults using statistical features extracted from vibration signals under good and faulty conditions of rotational mechanical system. Decision tree algorithm was used to select the prominent features. These features were given as inputs for training and testing the c-SVC and nu-SVC model of SVM and their fault classification accuracies were compared.