An integrated framework for effective service and repair in the automotive domain: An application of association mining and case-based-reasoning

In this paper, we propose a novel integrated framework combining association rule mining, case-based-reasoning and text mining that can be used to continuously improve service and repair in an automotive domain. The developed framework enables identification of anomalies in the field that cause customer dissatisfaction and performs root cause investigation of the anomalies. It also facilitates identification of the best practices in the field and learning from these best practices to achieve lean and effective service. Association rule mining is used for the anomaly detection and the root cause investigation, while case-based-reasoning in conjunction with text mining is used to learn from the best practices. The integrated system is implemented in a web based distributed architecture and has been tested on real life data.     

Advanced exergy analysis of transcritical CO2 heat pump system based on experimental data

Sādhanā - Conventional exergy analysis identifies component exergy destruction, however, does not give clear picture whether occurs because of component itself or due to remaining...     

Acrylate-based transdermal therapeutic system of nitrendipine

The objective of the present research investigation was to fabricate an acrylate-based transdermal therapeutic system (TTS) of nitrendipine, which could deliver drug at maximum input rate so as to deliver drug in minimum patch size. Transdermal patches were fabricated using synthesized acrylate pressure-sensitive adhesives (PSAs): PSA1, PSA2, and commercially available PSA3 and PSA4 using d-limonene as permeation enhancer. Effect of concentration of d-limonene on permeation kinetics of nitrendipine in PSAs was studied. Fabricated TTS in mentioned PSAs were evaluated for in-vitro release and permeation kinetics through guinea-pig skin. Cumulative release of drug in PSA1, PSA2, PSA3, and PSA4 was observed to be 45%, 40%, 25%, and 25%, respectively, upto 24 hr. Flux of drug through guinea-pig skin calculated at 48 hr in PSA1, PSA2, PSA3, and PSA4, with and without d-limonene, was observed to be 0.346 ± 0.10, 0.435 ± 0.17, 0.410 ± 0.17, and 0.162 ± 0.06, and 0.625 ± 0.19, 1.161 ± 0.46, 0.506 ± 0.17, and 0.520 ± 0.18 (µg/cm²/hr), respectively. The TTS in PSA2 showed comparatively high flux and could deliver drug at high input rate through transdermal route. PSA2 was found to have good rate-controlling property and could be successfully employed in transdermal delivery of nitrendipine.     

A domain-specific decision support system for knowledge discovery using association and text mining

We propose a novel association and text mining system for knowledge discovery (ASTEK) from the warranty and service data in the automotive domain. The complex architecture of modern vehicles makes fault diagnosis and isolation a non-trivial task. The association mining isolates anomaly cases from the millions of service and claims records. ASTEK has shown 86% accuracy in correctly identifying the anomaly cases. The text mining subscribes to the diagnosis and prognosis (D&P) ontology, which provides the necessary domain-specific knowledge. The root causes associated with the anomaly cases are identified by discovering frequent symptoms associated with the part failures along with the repair actions used to fix the part failures. The best-practice knowledge is disseminated to the dealers involved in the anomaly cases. ASTEK has been implemented as a prototype in the service and quality department of GM and its performance has been validated in the real life set up. On an average, the analysis time is reduced from few weeks to few minutes, which in real life industry are significant improvements.     

Modeling stable thermal stratification and its impact on wind flow over topography

Microscale flow models used in the wind energy industry commonly assume statically neutral conditions. These models can provide reasonable wind speed predictions for statically unstable and neutral flows; however, they do not provide reliable predictions for stably stratified flows, which can represent a substantial fraction of the available energy at a given site. With the objective of improving wind speed predictions and in turn reducing uncertainty in energy production estimates, we developed a Reynolds‐Averaged Navier–Stokes (RANS)‐based model of the stable boundary layer. We then applied this model to eight prospective wind farms and compared the results with on‐site wind speed measurements classified using proxies for stability; the comparison also included results from linear and RANS wind flow models that assume neutral stratification. This validation demonstrates that a RANS‐based model of the stable boundary layer can significantly and consistently improve wind speed predictions. Copyright © 2014 John Wiley & Sons, Ltd.