N. Harish Chandra

Bio: N. Harish Chandra is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Wavelet & Wavelet transform. The author has an hindex of 5, co-authored 8 publications receiving 178 citations.

Estimation of damping in rotors using complex morlet wavelet transform

Abstract: In a rotor-bearing system running with uniform angular acceleration, after running through the resonance, typical beat vibrations occur because the response of the system consists of the natural and excited motion. Shortly after resonance both are vibrations with frequencies close to each other. The present study explains a method for estimating damping of rotor-bearing systems from transient beat characteristics observed during the run-up stage. A mathematical justification is provided that the transient response of the rotor-bearing system consists of a beat response when the system crosses resonance. It is also clear from the mathematical model that the beat phenomenon observed depends on the level of damping in the system. The beat time period is also dependent on the angular acceleration of the system. This beat response is considered for wavelet analysis and the damping is estimated. Angular acceleration (α) of the rotor often increases the damping effects. This paper investigates how the damping ra...

A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm

Abstract: Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Abstract: The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system

Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet.

Abstract: Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT. An adaptive DBN is constructed to improve the convergence rate and identification accuracy with multiple stacked adaptive restricted Boltzmann machines (RBMs). The proposed method is applied to the fault diagnosis of rolling bearings. The results confirm that the proposed method is more effective than the existing methods.