Other affiliations: Indian Institutes of Technology
Bio: C. Sujatha is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Vibration & Finite element method. The author has an hindex of 13, co-authored 55 publications receiving 661 citations. Previous affiliations of C. Sujatha include Indian Institutes of Technology.
Papers published on a yearly basis
TL;DR: In this paper, a half car model with magnetorheological dampers is modelled by the modified Bouc-Wen model and the MR damper performance is sought to be improved by suitable choice of input currents to the levels of performance of an active suspension based on H ∞ control without and with preview.
TL;DR: In this article, the authors describe the problems concerning turbine rotor blade vibration that seriously impact the structural integrity of a developmental aero gas turbine, where the blades are fabricated from nickel base super alloy through directionally solidified investment casting process.
TL;DR: In this article, a multilayered feed forward neural network trained with supervised Error Back Propagation technique and an unsupervised Adaptive Resonance Theory-2 (ART2) based neural network were used for automatic detection/diagnosis of localized defects in ball bearings.
TL;DR: In this paper, a semi-active suspension with a Magnetorheological (MR) damper is considered. But the MR damper's parameters are determined optimally using a multi-objective optimization technique Non-dominated Sorting Genetic Algorithm II.
TL;DR: In this article, a simple closed-form expression for free vibration response, valid for both lightly and heavily damped beams, is formulated, and a detailed investigation of the cancellations of free responses is carried out.
•18 Jun 2012
TL;DR: In this paper, the authors present an experimental platform called PRONOSTIA, which enables testing, verifying and validating methods related to bearing health assessment, diagnostic and prognostic, which are performed under constant and/or variable operating conditions.
Abstract: This paper deals with the presentation of an experimental platform called PRONOSTIA, which enables testing, verifying and validating methods related to bearing health assessment, diagnostic and prognostic. The choice of bearings is justified by the fact that most of failures of rotating machines are related to these components. Therefore, bearings can be considered as critical as their failure significantly decreases availability and security of machines. The main objective of PRONOSTIA is to provide real data related to accelerated degradation of bearings performed under constant and/or variable operating conditions, which are online controlled. The operating conditions are characterized by two sensors: a rotating speed sensor and a force sensor. In PRONOSTIA platform, the bearing's health monitoring is ensured by gathering online two types of signals: temperature and vibration (horizontal and vertical accelerometers). Furthermore, the data are recorded with a specific sampling frequency which allows catching all the frequency spectrum of the bearing during its whole degradation. Finally, the monitoring data provided by the sensors can be used for further processing in order to extract relevant features and continuously assess the health condition of the bearing. During the PHM conference, a "IEEE PHM 2012 Prognostic Challenge" is organized. For this purpose, a web link to the degradation data is provided to the competitors to allow them testing and verifying their prognostic methods. The results of each method can then be evaluated regarding its capability to accurately estimate the remaining useful life of the tested bearings.
TL;DR: Neural-network-based models for predicting bearing failures are developed to perform accelerated bearing tests where vibration information is collected from a number of bearings that are run until failure and this information is used to train neural network models on predicting bearing operating times.
Abstract: Maintenance of mechanical and rotational equipment often includes bearing inspection and/or replacement. Thus, it is important to identify current as well as future conditions of bearings to avoid unexpected failure. Most published research in this area is focused on diagnosing bearing faults. In contrast, this paper develops neural-network-based models for predicting bearing failures. An experimental setup is developed to perform accelerated bearing tests where vibration information is collected from a number of bearings that are run until failure. This information is then used to train neural network models on predicting bearing operating times. Vibration data from a set of validation bearings are then applied to these network models. Resulting predictions are then used to estimate the bearing failure time. These predictions are then compared with the actual lives of the validation bearings and errors are computed to evaluate the effectiveness of each model. For the best model, we find that 64% of predictions are within 10% of actual bearing life, while 92% of predictions are within 20% of the actual life.
TL;DR: In this paper, 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 is presented.
••01 Mar 2010
TL;DR: In this article, the reader can understand the dynamics of rotating machines by using extremely simple models for each phenomenon, in which (at most) four equations capture the behavior of rotor vibration.
Abstract: This book equips the reader to understand every important aspect of the dynamics of rotating machines. Will the vibration be large? What influences machine stability? How can the vibration be reduced? Which sorts of rotor vibration are the worst? The book develops this understanding initially using extremely simple models for each phenomenon, in which (at most) four equations capture the behavior. More detailed models are then developed based on finite element analysis, to enable the accurate simulation of the relevant phenomena for real machines. Analysis software (in MATLAB) is associated with this book, and novices to rotordynamics can expect to make good predictions of critical speeds and rotating mode shapes within days. The book is structured more as a learning guide than as a reference tome and provides readers with more than 100 worked examples and more than 100 problems and solutions.
TL;DR: In this paper, a detailed literature review focuses on dynamics-based gearbox fault modeling, detection and diagnosis, focusing on the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, and gearbox transmission path modeling and method validation.