Somnath Sarangi

Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods

The prediction of stator winding faults using multiscale entropy is performed for the first time.Real-time vibration and current are used as diagnostics to identify faults.The system complexity associated with motors is investigated using multiscale entropy.GFRG is used to predict fault and also to suggest optimal settings for motor operation.The motor condition has a maximum contribution of 54.21%, as determined from the ANOVA analysis. In the present work, stator winding fault prediction is studied using a multiscale entropy (MSE) algorithm combined with a grey-based fuzzy algorithm. Experiments were performed with a normal motor and a motor with faulty stator winding. Real time, motor current and vibration signals were acquired at different operating speeds and were used for the diagnosis of faults. The obtained signals were denoised by wavelet transform. Grey relational analysis (GRA) coupled with fuzzy logic was used to model the stator winding fault and to predict the optimal setting for running the induction motor within its parameters range. Analysis of variance (ANOVA) was performed to determine the effect of each individual parameter on the response. The results indicate that the proposed novel approach is very effective in predicting the stator winding fault. Furthermore, the best running parameters for the induction motor are also reported.

Experimental Investigation of Misalignment Effects on Rotor Shaft Vibration and on Stator Current Signature

Misalignment is one of the most common faults in any rotating machine. It can cause decrease in efficiency and in the long run may cause failure of machine. Most of the researchers, consider only vibration information for the misalignment. However, this paper inspects the different types of misalignments by using diagnostic medium such as stator current signature as well as rotor vibration signal and it is being found that current signature alone can predict the misalignment effect without use of vibration signal. Diagnostic features obtained by FFT related to misalignments have been explained. Orbit plots are effectively used to explain the unique nature of misalignment fault. In this study, shaft displacement and stator current samples during machine run up under aligned as well as misaligned conditions are measured and analyzed. Result shows that misalignment is the parameter that is more responsible for the cause of instability.

Misalignment Faults Detection in an Induction Motor Based on Multi-scale Entropy and Artificial Neural Network

Abstract Misalignment is one of the most frequent faults observed in rotating machinery. In the present work, the misalignment fault of a motor shaft is studied using multi-scale entropy in combination with a back-propagation neural network algorithm. Experiments were performed, first with an aligned motor shaft, and then with a motor shaft that had angular and parallel misalignment at different operating speeds. Real-time motor current and vibration signals from aligned and different misaligned motor shafts were acquired for the diagnosis of faults. The existing literature mostly focused in the context of frequency-domain analysis. However, in this work multi-scale entropy is used, which accounts for the system complexity. A clear indication of reduction in complexity is observed with the faulty system. The proposed methodology is the first of its kind to detect the misalignment fault using the analysis of both vibration and current signals. The proposed work has also successfully identified for different types of misalignment.

Experimental investigation on misalignment fault detection in induction motors using current and vibration signature analysis

In the rotating machinery one of the commonly known reasons of vibration is misalignment of rotating shaft. In this present work experimental investigation is carried out on three phase induction motor to detect rotor misalignment. Proximity and current probes are used to monitor the vibration and current signal respectively. Fast Fourier Transform is used for signal processing. A full spectrum analysis is presented for both current signal and vibration signal to reveal the fault-specific whirl signatures. The results clearly indicate the potential and feasibility of the discussed approach for the rotor misalignment diagnosis in a shaft/rotor system coupled with a three phase induction motor.

Misalignment fault detection in induction motor using rotor shaft vibration and stator current signature analysis

In any rotating machine one of the most common faults is misalignment. Misalignment in any machine decreases the efficiency of that machine in short run. In long run process it may cause failure because of unnecessary vibration, gives stresses on motor and bearings and also danger of short circuiting in stator and rotor windings. In almost all the previous literatures, only the use of vibration information for the misalignment has been discussed. In this paper inspection of misalignment is done by using diagnostic medium such as rotor vibration as well as stator current. The vibration and the current signals are obtained for parallel, angular and the combination of both the misalignments. FFT was carried out for the obtained responses to investigate the fault. To show the uniqueness of misalignment fault orbit plot of vibrations spectra are used.

Effect of damage on the free radial oscillations of an incompressible isotropic tube

The effect of damage on the finite-amplitude, free radial oscillations of an arbitrary incompressible, isotropic, homogeneous cylindrical tube is investigated. Pressure is applied to the inner and outer surfaces of the damaged cylinder and constrained from both ends. A purely radial motion is observed when the pressure is removed. The corresponding equation of motion is obtained, incorporating the effect of damage. A simple exponential front factor damage function is introduced in the tube problem. The damage function is a function of the first invariant of the left Cauchy–Green deformation tensor and is dependent on its maximum previous ever value. It is found that the period of oscillation for a thin-walled neo-Hookean membrane varies with the damage function. In contrast, the respective period for an undamaged neo-Hookean membrane is a constant. The described study may help in the surgical procedure of angioplasty, performed during inflammatory diseases such as atherosclerosis. During angioplasty, owing to inflation of the balloon, the arterial wall is damaged; this study may help to gain more insight on the surgical procedure. Both thick- and thin-walled analysis of the damaged cylindrical tube are performed and compared with the undamaged case. Several results are inferred and illustrated graphically for two types of parent material model, namely Gent and neo-Hookean.

Fault Diagnosis of Broken Rotor Bars in Induction Motor Using Multiscale Entropy and Backpropagation Neural Network

Interruptions in any process industry due to machinery problem induce a serious financial loss. And as we know that induction motors occupy a major area in machinery and process industry, detection of faults beforehand is a key to avoid the state of financial or production crisis in future. The present work proposes a novel algorithm for the detection of broken rotor bars in induction motor. Stator current in addition to rotor vibration in an induction motor was measured and employed for fault detection of broken rotor bar. Multiscale entropy (MSE) is used as statistic-based approach in order to tackle the nonlinear behavior existing in rotor bar using vibration and current as the diagnostic media, as both cumulatively considered describe the regularity in the diagnostic information. The proposed work presents an approach to analyze features that distinguish the rotor vibration and stator current samples of normal induction motor from those of the broken rotor bar. Further, backpropagation neural network classifier is applied over the resultant feature set which distinguishes the faulty data set from the healthy with an accuracy level of 15.5 % for vibration and 14 % for current.

Misalignment Fault Prediction of Motor-Shaft Using Multiscale Entropy and Support Vector Machine

Rotating machines constitutes the major portion of the industrial sector. In case of rotating machines, misalignment has been observed to be one of the most common faults which can be regarded as a cause for decrease in efficiency and can also for the failure at a time. Till date the researchers have dealt only with the vibration samples for misalignment fault detection, whereas in the present work both stator current samples and vibration samples has been used as a diagnostic media for fault detection. Multiscale entropy (MSE) based statistical approach for feature extraction and support vector machine (SVM) classification makes the proposed algorithm more robust. Thus, any non-linear behavior in the diagnostic media is easily handled. The proposed work has depicted an approach to analyze features that distinguishes the vibration as well as current samples of a normal induction motor from that of a misaligned one. The result shows that the proposed novel approach is very effective to predict the misalignment fault for the induction motor.

Data on the viscoelastic behavior of neoprene rubber

The present article contains data on the multi-step cyclic stress relaxation tests associated with the viscoelastic behavior of the neoprene rubber. Herein, the present data aims the accurate prediction of the time dependent mechanical behavior of the polymeric materials. The findings of the present data include the demonstration of the Mullin׳s stress-softening phenomenon, clearly. These data findings may serve as a benchmark to validate the more advanced phenomenological model developments in future as compared to the existing ones.

Rheological model for stress-softened visco-hyperelastic materials

A rate-dependent thermodynamically consistent constitutive model for hyperelastic materials in finite strain regime is presented on the basis of multiplicative split of deformation gradient tensor into elastic and viscous parts. The total stress is decomposed into an equilibrium stress and a viscosity-induced overstress by following the Zener rheological model. To incorporate the Mullins stress-softening phenomenon in viscoelastic material, an invariant-based stress-softening function is also proposed. An analytical scheme based on Clausius-Duhem inequality is proposed that ascertains thermodynamic consistency with the fundamental relation between the viscous strain rate and the overstress tensor with some limited elastic parent material models. The proposed softening model is validated with uniaxial stress relaxation test data and the proposed analytical scheme confirms the necessity of considering both the current overstress and the current deformation as variables to describe the evolution of the rate-dependent phenomena.