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Birmingham, United Kingdom

Obeid N.,University of Jordan | Rao R.B.K.N.,COMADEM International
Knowledge and Information Systems | Year: 2010

We develop, in this paper, a representation of time and events that supports a range of reasoning tasks such as monitoring and detection of event patterns which may facilitate the explanation of root cause(s) of faults. We shall compare two approaches to event definition: the active database approach in which events are defined in terms of the conditions for their detection at an instant, and the knowledge representation approach in which events are defined in terms of the conditions for their occurrence over an interval. We shall show the shortcomings of the former definition and employ a three-valued temporal first order nonmonotonic logic, extended with events, in order to integrate both definitions. © Springer-Verlag London Limited 2009. Source


Vijay G.S.,Manipal University India | Pai S.P.,Nmam Institute Of Technology | Sriram N.S.,Vidya Vikas Institute of Engineering and Technology | Rao R.B.K.N.,COMADEM International
Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology | Year: 2013

This article uses the cluster dependent weighted fuzzy C-means based radial basis function neural network for comparing the different dimensionality reduction techniques for the fault diagnosis in the rolling element bearing. The vibration signals from normal bearing, bearing with defect on the inner race, and bearing with defect on the outer race were acquired under one radial load and two shaft speeds. These signals were subjected to the wavelet transform based denoising from which several time and frequency domain features were extracted. Dimensionality reduction techniques, namely, principal component analysis, Fisher's criterion, and separation index, have been used to select the sensitive features. The selected features were used to train and test the radial basis function neural network, where the centers of the radial basis function units have been optimized by the cluster dependent weighted fuzzy C-means and the widths of the radial basis function units have been fixed by trial and error. Finally, a comparison of the dimensionality reduction techniques based on the radial basis function neural network performance is presented. © IMechE 2012. Source


Vijay G.S.,Manipal University India | Kumar H.S.,Nmam Institute Of Technology | Srinivasa P.P.,Nmam Institute Of Technology | Sriram N.S.,Vidya Vikas Institute of Engineering and Technology | Rao R.B.K.N.,COMADEM International
Computational Intelligence and Neuroscience | Year: 2012

The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher's Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal. © 2012 Vijay G. S. et al. Source


Modera Tribological systems (such as aerospace vehicles, industrial processes, manufacturing systems, transportation systems, electrical and electronic systems, etc) have moving parts of one type or other and are becoming increasingly sophisticated and complex. These are subjected to various known and unknown failure modes which impact adversely their reliability, availability, safety and maintainability. Tribology has played a significant role in reducing friction, wear and extending the Remaining Useful Life (RUL) of modern engineering systems. In the past few decades, system designers and thinkers are increasingly becoming aware of the proactive concept of intelligent-based failure diagnosis and prognosis as the way forward to continuously improve the health and performance of their valuable assets. This change in philosophical attitude from reactive to proactive diagnosis/prognosis and a greater appreciation of assessing the 'state of health' and 'overall operational efficiency' of complex engineering systems should bring immense long term performance improvement and socio-economic benefits to global economy. In this state-of-the-art review paper, some condition monitoring and diagnostic engineering management (COMADEM) strategies are identified with specific reference to modern Tribological systems ISSN 1363-7681© 2013 COMADEM International. Source


Pai S.,Nmam Institute Of Technology | Rao R.B.K.N.,COMADEM International
Machining Science and Technology | Year: 2012

The monitoring of tool wear is a most difficult task in the case of various metal-cutting processes. Artificial Neural Networks (ANN) has been used to estimate or classify certain wear parameters, using continuous acquisition of signals from multi-sensor systems. Most of the research has been concentrated on the use of supervised neural network types like multi-layer perceptron (MLP), using back-propagation algorithm and Radial Basis Function (RBF) network. In this article, a new constructive learning algorithm proposed by Fritzke, namely Growing Cell Structures (GCS) has been used for tool wear estimation in face milling operations, thereby monitoring the condition of the tool. GCS generates compact network architecture in less training time and performs well on new untrained data. The performance of this network has been compared with that of another constructive learning algorithm-based neural network, namely the Resource Allocation Network (RAN). For the sake of establishing the effectiveness of GCS, results obtained have been compared with those obtained using Multi Layer Perceptron (MLP), which is a standard and widely used neural network. © 2012 Copyright Taylor and Francis Group, LLC. Source

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