Megha Singh

Bio: Megha Singh is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Induction motor & Fault (power engineering). The author has an hindex of 4, co-authored 14 publications receiving 61 citations.

Incipient Fault Detection in Stator Windings of an Induction Motor Using Stockwell Transform and SVM

Abstract: In this article, Stockwell transform (ST) is used to analyze the stator current signals for diagnosis of various motor conditions such as healthy, stator winding interturn shorts, and phase to ground faults. ST decomposes the current signals into complex ST matrix whose magnitude has been utilized for the fault detection. The nature of the fault, that is, ground or interturn is identified using the zero sequence currents followed by postfault detection. Two separate frequency bands are defined to extract the features which are fed to two different support vector machine (SVM) models for faulty phase detection for both types of faults. Under both cases, a heuristic feature selection approach is utilized to find the optimal features for classification purposes. Average classification accuracy of 96% has been achieved for both types of faults.

Broken Rotor Bar Fault Diagnosis of a Three-phase Induction Motor using Discrete Wavelet Transform

Abstract: This paper presents the application of wavelet transform for broken rotor bar fault detection in a three-phase induction motor. One phase stator current signals are decomposed with db8 mother wave into approximate and detail coefficients. The detail coefficient including the frequencies near fundamental is chosen for analysis. Using the statistical analysis of the detail coefficient, it is found that mean absolute deviation varies in proportion to the fault severity. With the help of this parameter, not only fault detection can be accomplished, but also fault severity can be measured. The performance of the parameter is also tested under significant reduction in voltage drop and it is found that the mean absolute deviation remains undeterred in detecting and identifying fault severity under variation of supply voltage. The study has been performed on the experimentally collected data on laboratory simulated breakages in bars of the rotor.

Bearing fault diagnosis of a three phase induction motor using stockwell transform

Abstract: This paper presents bearing fault detection and its diagnosis in a three phase induction motor using Stockwell transform of stator currents. The maximum magnitude and maximum phase angle plots are obtained from S-transform for various bearing conditions both on shaft-side and fan-side. The standard deviation of these plots are utilized to detect and analyze the bearing faults. The various bearing faults analyzed under case studies include defects in innerrace, outerrace, cage and balls. The experimental study shows that the S-transform based feature extraction can be effectively utilized for bearing fault detection and diagnosis in three phase induction motor.

Application of stockwell transform in bearing fault diagnosis of induction motor

Abstract: This paper presents a novel method for detecting bearing faults using multi-resolution analysis of stator current signals based on Stockwell Transform. The sampled stator current signals are analyzed with the help of Stockwell Transform to extract features namely maximum magnitude and maximum phase angle. The statistics of these features are further utilized to detect and classify the bearing faults in outer race, inner race, cage and balls. The experimental results show that the proposed approach is capable of detecting various bearing faults in induction motor.

PhysioBank

Abstract: PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档。目前包含多参数的心肺、神经和其他生物医学信号，尤以心电图（ECG）为主。信号来自健康受试者和各种疾病的患者。涉及的疾病包括心脏猝死、充血性心力衰竭、癫痫、步态不稳、睡眠呼吸暂停和衰老等。

Computational and Mathematical Methods in Medicine

Abstract: This issue marks a transition and a changing of the guard for Computational and Mathematical Methods in Medicine (CMMM). It is with some nostalgia that we look back on our long and illustrious association with Taylor and Francis; however, at the same time we look to the future with optimism and hope as Hindawi takes the helm and converts CMMM to the community-based, open access model that they have so successfully championed. The Hindawi Publishing Corporation is one of the fastest growing academic publishers worldwide with over 200 academic journals in their portfolio and a commitment to the highest levels of peer review and excellence. Reflecting on the genesis and evolution of CMMM, it is clear that Brian Sleeman, the founding Editor-in-Chief, showed great foresight in creating a journal that brought together the disparate disciplines of mathematics and medicine and that continues to play a major role in the development of mathematical medicine. He worked passionately to develop and promote the journal through some difficult times, with the insight and courage to bring together both biomedical/clinical scientists and mathematical scientists onto a single editorial board (a practice that has become more commonplace in subsequent journals in the field). The success that the journal has enjoyed thus far is a clear testament to his hard work, dedication, and vision. The journal has continued to provide a unique forum for the dissemination of interdisciplinary research resulting from collaborations between clinicians/experimentalists and theoreticians. CMMM has also continued to evolve rapidly, reflecting the increased focus on systems and interdisciplinary collaborative efforts across the breadth of biomedical, clinical, and translational research areas. The past year also saw the result of much hard work, with the inclusion of the journal in PubMed/Medline and the Science Citation Index Expanded. This was a great development for the journal since it not only has had an enormous impact on the general awareness and profile of the journal but has also resulted in increased submissions and downloads from the journal website over the past year. It has been exciting and rewarding to see the journal develop and evolve in this manner, and we look forward to increased success following this higher profile. The future looks extremely bright for the field of mathematical medicine as it emerges from its period of infancy and takes its place as a legitimate and central field of research and enquiry. Our sincere hope and wish is that CMMM continues from strength to strength and fulfills its role and promise as envisioned originally by its founding editor. Pamela Jones Sivabal Sivaloganathan

Entropy Based Fault Classification Using the Case Western Reserve University Data: A Benchmark Study

Abstract: Fault diagnosis of bearings using classification techniques plays an important role in industrial applications, and, hence, has received increasing attention. Recently, significant efforts have been made to develop various methods for bearing fault classification and the application of Case Western Reserve University (CWRU) data for validation has become a standard reference to test the fault classification algorithms. However, a systematic research for evaluating bearing fault classification performance using the CWRU data is still lacking. This paper aims to provide a comprehensive benchmark analysis of the CWRU data using various entropy and classification methods. The main contribution of this paper is applying entropy-based fault classification methods to establish a benchmark analysis of entire CWRU datasets, aiming to provide a proper assessment of any new classification methods. Recommendations are provided for the selection of the CWRU data to aid in testing new fault classification algorithms, which will enable the researches to develop and evaluate various diagnostic algorithms. In the end, the comparison results and discussion are reported as a useful baseline for future research.