Satish C. Sharma

Abstract: Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on rolling bearings is used. The vibration response are obtained and analyzed for the various defects of ball bearings. The specific defects are considered as crack in outer race, inner race with rough surface and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that the machine learning algorithms mentioned above can be used for automated diagnosis of bearing faults. It is also observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.

Abstract: Bearing failure is one of the foremost causes of breakdown in rotating machines, resulting in costly systems downtime. This paper presents a methodology for rolling element bearings fault diagnosis using continuous wavelet transform (CWT). The fault diagnosis method consists of three steps, firstly the six different base wavelets are considered in which three are from real valued and other three from complex valued. Out of these six wavelets, the base wavelet is selected based on wavelet selection criterion to extract statistical features from wavelet coefficients of raw vibration signals. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. Finally, the bearing faults are classified using these statistical features as input to machine learning techniques. Three machine learning techniques are used for faults classifications, out of which two are supervised machine learning techniques, i.e. support vector machine (SVM), artificial neural network (ANN) and other one is an unsupervised machine learning technique, i.e. self-organizing maps (SOM). The methodology presented in the paper is applied to the rolling element bearings fault diagnosis. The Meyer wavelet is selected based on Maximum Energy to Shannon Entropy ratio and the Complex Morlet wavelet is selected using Maximum Relative Wavelet Energy criterion. The test result showed that the SVM identified the fault categories of rolling element bearing more accurately for both Meyer wavelet and Complex Morlet wavelet and has a better diagnosis performance as compared to the ANN and SOM. Features selected using Meyer wavelet gives higher faults classification efficiency with SVM classifier.

Abstract: This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.

Abstract: In the present paper, the simulation of the mechanical responses of individual carbon nanotubes treated as thin shells with thickness has been done using FEM. The resonant frequencies of the fixed free and the bridged SWCNT have been investigated. This analysis explores the resonant frequency shift of SWCNTs caused by the changes in the size of CNT in terms of length as well as the masses. The results showed the sensitivity of the single walled carbon nanotubes to different masses and different lengths. The results indicate that the mass sensitivity of carbon nanotube nanobalances can reach 10−21 g and the mass sensitivity increases when smaller size nanotube resonators are used in mass sensors. The vibration signature exhibits super-harmonic and sub-harmonic response with different level of mass. In order to explore the suitability of the SWCNT as a mass detector device, the simulation results of the resonant frequency of fixed free SWCNT are compared to the published experimental data. It is shown that the FEM simulation results are in good agreement with the experimental data and hence the current modelling approach is suitable as a coupled-field design tool for the development of SWCNT-based NEMS applications.

Abstract: This work describes a theoretical study concerning the static and dynamic performance of a circular thrust pad hydrostatic bearing having recesses of different geometric shapes. The Finite Element Method has been used to compute the performance characteristics of a circular thrust pad hydrostatic bearing with circular, rectangular, elliptical and annular recesses. The performance has been compared on the basis of the same bearing operating and the same geometric parameters, i.e. the same ratio of bearing to pocket area ( A ) and the same value of restrictor design parameter C s2 . Further, a comparative study of the various bearing configurations has been carried out vis-a-vis different compensating devices such as capillary, orifice, and constant flow valve restrictors so as to study the combined influence of the geometric shape of recesses and the compensating device on bearing performance. The computed results indicate that to get an improved performance from a hydrostatic circular thrust pad bearing, a proper selection of the geometric shape of the recess in conjunction with the type of restrictor and the value of the restrictor design parameter C s2 is essential.

Abstract: Over the last 20 years, particularly in last 10 years, great progress has been made in the theory and applications of wavelets and many publications have been seen in the field of fault diagnosis. This paper attempts to provide a review on recent applications of the wavelets with focus on rotary machine fault diagnosis. After brief introduction of the theoretical background on both classical wavelet transform and second generation wavelet transform, applications of wavelets in rotary machine fault diagnosis are summarized according to the following categories: continuous wavelet transform-based fault diagnosis, discrete wavelet transform-based fault diagnosis, wavelet packet transform-based fault diagnosis, and second generation wavelet transform-based fault diagnosis. In addition, some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, new wavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosis are also discussed.

Abstract: Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.

Abstract: This review covers advances in electrochemical and biochemical sensor development and usage during 2010 and 2011 In choosing scholarly articles to contribute to this review, special emphasis was placed on work published in the areas of reference electrodes, potentiometric sensors, voltammetric sensors, amperometric sensors, biosensors, immunosensors, and mass sensors In the past two years there have been a number of important papers, that do not fall into the general subsections contained within the larger sections Such novel advances are very important for the field of electrochemical sensors as they open up new avenues and methods for future research Each section above contains a subsection titled “Other Papers of Interest” that includes such articles and describes their importance to the field in general For example, while most electrochemical techniques for sensing analytes of interest are based on the changes in potential or current, Shan et al1 have developed a completely novel method for performing electrochemical measurements In their work, they report a method for imaging local electrochemical current using the optical signal of the electrode surface generated from a surface plasmon resonance (SPR) The electrochemical current image is based on the fact that the current density can be easily calculated from the local SPR signal The authors demonstrated this concept by imaging traces of TNT on a fingerprint on a gold substrate Full articles and reviews were primarily amassed by searching the SciFinder Scholar and ISI Web of Knowledge Additional articles were found through alternate databases or by perusing analytical journals for pertinent publications Due to the reference limitation, only publications written in English were considered for inclusion Obviously, there have been more published accounts of groundbreaking work with electrochemical and biochemical sensors than those covered here This review is a small sampling of the available literature and not intended to cover every advance of the past two years The literature chosen focuses on new trends in materials, techniques, and clinically relevant applications of novel sensors To ensure proper coverage of these trends, theoretical publications and applications of previously reported sensor development were excluded We want to remind our readers that this review is not intended to provide comprehensive coverage of electrochemical sensor development, but rather to provide a glimpse of the available depth of knowledge published in the past two years This review is meant to focus on novel methods and materials, with a particular focus on the increasing use of graphene sheets for sensor material development For readers seeking more information on the general principles behind electrochemical sensors and electrochemical methods, we recommend other sources with a broader scope2, 3 Electrochemical sensor research is continually providing new insights into a variety of fields and providing a breadth of relevant literature that is worthy of inclusion in this review Unfortunately, it is impossible to cover each publication and unintentional oversights are inevitable We sincerely apologize to the authors of electrochemical and biochemical sensor publications that were inadvertently overlooked