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A data- and ontology-driven text mining-based construction of reliability model to analyze and predict component failures

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Abstract

A real-life reliability system is proposed by fusing the field warranty failure data with the failure modes extracted from unstructured repair verbatim data by using the ontology-based natural language processing technique to facilitate accurate estimation of component reliability. Traditionally, the reliability estimation process uses the warranty data, but it provides limited support to handle the “failure confounding” problem, whereby different failure modes associated with a component failure are confounded into a single failure mode. The resulting reliability estimation lacks the required level of precision. Because our model takes into account textual failure modes associated with component failures, it enhances the overall reliability estimation. The performance of our system is evaluated with the baseline system for predicting absolute errors by using the real-life data from the automotive domain, e.g., headlamp failure, collected at different miles exposures. In the best case, the absolute errors predicted by our model showed an improvement of 97 % with respect to the baseline model (without considering the failure modes), while in worst case, it was 71 %.

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Notes

  1. The experimental results in Jung and Bai [16] show that when age and usage variables are strongly correlated, the performance of univariate and bivariate approaches is comparable. However, in cases of weak correlations, the bivariate approach performs magnitude better than the univariate approach.

  2. Due to the data non-disclosure agreement to the third party, we have given the dummy values of the data collected in the field.

  3. In our domain, the data can be right or left skewed, and to accommodate such a varying nature of data, the Weibull model was used. The Weibull model provided us with the flexibility to model such hazard functions as decreasing, increasing, or constant and to describe different phrases of component’s lifetime.

  4. RDFS is a World Wide Web Consortium (W3C) standard for the specification of meta-data model.

  5. https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html.

  6. PMI-TR proposed by Turney [41] can be defined as follows: \(Sim(t,t_j )=log_2 (1+\frac{(tANDt_j )^{2}}{hits\left( t \right) .hits(t_j )})\), where \(hits\left( t \right) \) and \(hits(t_j )\) are the number of times \(\left( t \right) \) and \((t_j )\) are observed in the corpus, and hit() is the number of documents where both the tuples are present. The distance proximity between the positions of two words is exploited to construct the tuples

  7. The word widow size is a free parameter, which can be defined in many ways [27]. In our work, we experimented with different word window sizes, such as five, seven, and ten. The large word window sizes, i.e., seven and ten words, yielded noisy results; hence, we settled for the word window of five words while constructing the tuples.

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Acknowledgments

The authors would like to thank reviewers and GM’s internal paper review committee for providing valuable comments on the earlier drafts of this manuscript.

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Authors and Affiliations

  1. Advanced Quality Analytics, General Motors, Creator Building, International Technology Park, Whitefiled, Bangalore, 560066, Karnataka, India

    Dnyanesh Rajpathak & Soumen De

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  1. Dnyanesh Rajpathak
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Correspondence to Dnyanesh Rajpathak.

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Rajpathak, D., De, S. A data- and ontology-driven text mining-based construction of reliability model to analyze and predict component failures. Knowl Inf Syst 46, 87–113 (2016). https://doi.org/10.1007/s10115-014-0806-3

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