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A. S. Sekhar

Bio: A. S. Sekhar is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topic(s): Rotor (electric) & Helicopter rotor. The author has an hindex of 29, co-authored 109 publication(s) receiving 2757 citation(s). Previous affiliations of A. S. Sekhar include Indian Institute of Technology Kharagpur & Indian Institutes of Technology.
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Journal ArticleDOI
S. Prabhakar1, Amiya R Mohanty1, A. S. Sekhar1Institutions (1)
Abstract: Bearing race faults have been detected by using discrete wavelet transform (DWT). Vibration signals from ball bearings having single and multiple point defects on inner race, outer race and the combination faults have been considered for analysis. The impulses in vibration signals due to bearing faults are prominent in wavelet decompositions. It is found that the impulses appear periodically with a time period corresponding to characteristic defect frequencies. It has been shown that DWT can be used as an effective tool for detecting single and multiple faults in the ball bearings.

265 citations

Journal ArticleDOI
A. S. Sekhar1, B.S. Prabhu1Institutions (1)
Abstract: Improper aligning of shafts through couplings often leads to severe vibration problems in many rotating machines. The rotor-bearing system is modelled using higher order finite elements by considering deflection, slope, shear force, bending moment with eight degrees of freedom per node. The reaction forces, moments developed due to flexible coupling misalignment are derived and introduced in the model. The imbalance response in two harmonics is evaluated. The increase in harmonics with misalignment can easily be modelled by using FEM analysis. The location of the coupling with respect to the bending mode shape has a strong influence on the vibrations.

183 citations

Journal ArticleDOI
N. Harish Chandra1, A. S. Sekhar1Institutions (1)
Abstract: Faults such as misalignment, rotor cracks and rotor to stator rub can exist collectively in rotor bearing systems. It is an important task for rotor dynamic personnel to monitor and detect faults in rotating machinery. In this paper, the rotor startup vibrations are utilized to solve the fault identification problem using time frequency techniques. Numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of faults as mentioned above. Three signal processing tools namely Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert Huang Transform (HHT) are compared to evaluate their detection performance. The effect of addition of Signal to Noise ratio (SNR) on three time frequency techniques is presented. The comparative study is focused towards detecting the least possible level of the fault induced and the computational time consumed. The computation time consumed by HHT is very less when compared to CWT based diagnosis. However, for noisy data CWT is more preferred over HHT. To identify fault characteristics using wavelets a procedure to adjust resolution of the mother wavelet is presented in detail. Experiments are conducted to obtain the run-up data of a rotor bearing setup for diagnosis of shaft misalignment and rotor stator rubbing faults.

129 citations

Journal ArticleDOI
A. S. Sekhar1Institutions (1)
Abstract: An important rotor fault, which can lead to catastrophic failure if undetected, is fatigue crack in the shaft. The cracked rotor problem received the first attention in 1970, since when the interest among the researchers started. Vibration behavior of cracked structures, in particular cracked rotors, has received considerable attention in the last two decades. The problem of damage and crack detection in structural components has acquired important role in recent years. However, the studies are mainly dealt with single crack. If the structure is cracked in at least two positions, the problem of crack sizing and location becomes decidedly more complex. Relatively few authors have addressed the multi-crack assessment for structures. The objective of this present study is to summarize the different studies on double/multi-cracks and to note the influences, identification methods in vibration structures such as beams, rotors, pipes, etc. And thus this paper brings out the state of the research on multiple cracks effects and their identification.

123 citations

Journal ArticleDOI
A. S. Sekhar1Institutions (1)
Abstract: The dynamic behaviour of structures, in particular, that of a rotor, containing cracks is a subject of considerable current interest. Several researchers have developed models of cracked rotor systems considering mainly a single transverse surface crack. In the present study Finite Element (FEM) analysis of a rotor system for flexural vibrations has been considered by including a shaft with two open cracks have been carried out and the influence of one crack over the other for eigenfrequencies, mode shapes and for threshold speed limits has been observed.

113 citations

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Journal ArticleDOI
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
Abstract: Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices and possible future trends of CBM.

3,419 citations

Journal ArticleDOI
Andrew D. Dimarogonas1Institutions (1)
Abstract: The presence of a crack in a structural member introduces a local flexibility that affects its vibration response. Moreover, the crack will open and close in time depending on the rotation and vibration amplitude. In this case the system is nonlinear. Furthermore, if general motion is considered, the local stiffness matrix description of the cracked section of the shaft leads to a coupled system, while for an uncracked shaft the system is decoupled. This means that the crack introduces new harmonics in the spectrum. In fact, in addition to the second harmonic of rotation and the subharmonic of the critical speed, two more families of harmonics are observed: 1. (1) higher harmonics of the rotating speed due to the nonlinearity of the closing crack, and 2. (2) longitudinal and torsional harmonics are present in the start-up lateral vibration spectrum due to the coupling. Over 500 papers on the subject were published in the past 10 yrs. A wealth of analytical, numerical and experimental investigations now exists. However, a consistent cracked bar vibration theory is yet to be developed. There are still many unanswered questions, especially in the area of closing cracks in rotating shafts.

996 citations

Journal ArticleDOI
Ruonan Liu1, Boyuan Yang1, Enrico Zio2, Enrico Zio3  +1 moreInstitutions (3)
TL;DR: 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.
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.

698 citations

Journal ArticleDOI
Zhipeng Feng1, Ming Liang2, Fulei Chu3Institutions (3)
Abstract: Nonstationary signal analysis is one of the main topics in the field of machinery fault diagnosis. Time–frequency analysis can identify the signal frequency components, reveals their time variant features, and is an effective tool to extract machinery health information contained in nonstationary signals. Various time–frequency analysis methods have been proposed and applied to machinery fault diagnosis. These include linear and bilinear time–frequency representations (e.g., wavelet transform, Cohen and affine class distributions), adaptive parametric time–frequency analysis (based on atomic decomposition and time–frequency auto-regressive moving average models), adaptive non-parametric time–frequency analysis (e.g., Hilbert–Huang transform, local mean decomposition, and energy separation), and time varying higher order spectra. This paper presents a systematic review of over 20 major such methods reported in more than 100 representative articles published since 1990. Their fundamental principles, advantages and disadvantages, and applications to fault diagnosis of machinery have been examined. Some examples have also been provided to illustrate their performance.

577 citations

Journal ArticleDOI
Runqing Huang1, Lifeng Xi1, Xinglin Li, C. Richard Liu2  +2 moreInstitutions (3)
Abstract: This paper deals with a new scheme for the prediction of a ball bearing's remaining useful life based on self-organizing map (SOM) and back propagation neural network methods. One of the key components needed for effective bearing life prediction is the set-up of an appropriate degradation indicator from a bearing's incipient defect stage to its final failure. This new method is different from the others that have been used in the past, in that it uses the minimum quantisation error (MQE) indicator derived from SOM, which is trained by six vibration features, including a new designed degradation index for performance degradation assessment. Then, using this indicator, back propagation neural networks focusing on the degradation periods can be trained. Thanks to weight application to failure times (WAFT) technology, a useful life prediction model for ball bearings has been developed successfully. Finally, a set of accelerated bearing run-to-failure experiments is carried out, with the experimental results showing that the new proposed methods are greatly superior to those, based on L10 bearing life prediction, currently being used.

454 citations

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Author's H-index: 29

No. of papers from the Author in previous years