Last M.,Ben - Gurion University of the Negev |
Sinaiski A.,Ben - Gurion University of the Negev |
Subramania H.S.,Technical Center India Pvt Ltd
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010
Unexpected failures occurring in new cars during the warranty period increase the warranty costs of car manufacturers along with harming their brand reputation. A predictive maintenance strategy can reduce the amount of such costly incidents by suggesting the driver to schedule a visit to the dealer once the failure probability within certain time period exceeds a pre-defined threshold. The condition of each subsystem in a car can be monitored onboard vehicle telematics systems, which become increasingly available in modern cars. In this paper, we apply a multi-target probability estimation algorithm (M-IFN) to an integrated database of sensor measurements and warranty claims with the purpose of predicting the probability and the timing of a failure in a given subsystem. The multi-target algorithm performance is compared to a single-target probability estimation algorithm (IFN) and reliability modeling based on Weibull analysis. © 2010 Springer-Verlag Berlin Heidelberg.
Ramani A.,Technical Center India Pvt Ltd
Structural and Multidisciplinary Optimization | Year: 2011
A heuristic approach to handle strength constraints based on material failure criteria in multi-material topology optimization is presented. This is particularly advantageous if different materials have different failure criteria. The change in the material failure function in an element due to a contemplated material change is estimated without the need for expensive matrix factorizations. This change is used along with the changes to the objective and deflection-based constraint functions, computed using pseudo-sensitivities, to determine a single aggregated ranking parameter for the element. Elements are ranked on the basis of their ranking parameters and this rank is used to modify the material ID-s of a few top-ranked elements during an optimization iteration. The working of the algorithm is demonstrated on a few example problems showing its effectiveness and utility in deriving optimal topologies with multiple materials in the presence of stress and strain-based failure criteria, in addition to the conventional stiffness-based constraints. © 2010 Springer-Verlag.
Routray A.,Indian Institute of Technology Kharagpur |
Rajaguru A.,Indian Institute of Technology Kharagpur |
Singh S.,Technical Center India Pvt Ltd
2010 IEEE International Conference on Automation Science and Engineering, CASE 2010 | Year: 2010
In this paper, we propose a data-driven method to detect anomalies in operating Parameter Identifiers (PIDs) and in the absence of any anomaly, classify faults in automotive systems by analyzing PIDs collected from the freeze frame data. We first categorize the operating parameter data using automotive domain knowledge. The dataset thus obtained is then analyzed using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for finding coherence among the PIDs. Then we use clustering algorithms based on both linear distance and information theoretic measures to assign coherent PIDs to the same class or cluster. A comparative analysis of the behavior of PIDs belonging to the same cluster can now be made for detecting anomaly in PIDs. Since a system fault is characterized by the values by of all PIDs across all the clusters, we use the joint probability distribution of the independent components of all PIDs to characterize the fault and find the divergence between the joint distributions of training and test data to classify faults. The proposed method can analyze available parameter data, categorize PIDs into informative or non-informative category, and detect fault condition from the clusters. We demonstrate the algorithm by way of an application to operating parameter data collected during faults in catalytic converters of vehicles. © 2010 IEEE.
Patham B.,Technical Center India Pvt Ltd
Journal of Applied Polymer Science | Year: 2013
Simulations of evolution of cure-induced stresses in a viscoelastic thermoset resin are presented. The phenomenology involves evolution of resin modulus with degree of cure and temperature, the development of stresses due to crosslink induced shrinkage, and the viscoelastic relaxation of these stresses. For the simulations, the detailed kinetic and chemo-thermo-rheological models for an epoxy-amine thermoset resin system, described in Eom et al. (Polym. Eng. Sci. 2000, 40, 1281) are employed. The implementation of this model into the simulation is facilitated by multiphysics simulation strategies. The trends in simulated cure-induced stresses obtained using the full-fledged viscoelastic model are compared with those obtained from two other equivalent material models, one involving a constant elastic modulus, and the other involving a cure-dependent (but time-invariant) elastic modulus. It is observed that the viscoelastic model not only results in lower estimates of cure-induced stresses, but also provides subtle details of the springback behavior. Copyright © 2012 Wiley Periodicals, Inc.
Rajpathak D.G.,Technical Center India Pvt Ltd
Computers in Industry | Year: 2013
In automotive domain, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis (FD) process. Here, the aim of knowledge discovery using text mining (KDT) task is to discover the best-practice repair knowledge from millions of repair verbatim enabling accurate FD. However, the complexity of KDT problem is largely due to the fact that a significant amount of relevant knowledge is buried in noisy and unstructured verbatim. In this paper, we propose a novel ontology-based text mining system, which uses the diagnosis ontology for annotating key terms recorded in the repair verbatim. The annotated terms are extracted in different tuples, which are used to identify the field anomalies. The extracted tuples are further used by the frequently co-occurring clustering algorithm to cluster the repair verbatim data such that the best-practice repair actions used to fix commonly observed symptoms associated with the faulty parts can be discovered. The performance of our system has been validated by using the real world data and it has been successfully implemented in a web based distributed architecture in real life industry. © 2013 Elsevier B.V.