Rahul Chougule

A generic ontology development framework for data integration and decision support in a distributed environment

Data inconsistency and data mismatch are critical problems that limit data interoperability and hinder smooth operation of a distributed business. An ontology represents a semantic model that explicitly describes various entities and their properties of a domain of discourse and acts as a vehicle for seamless data integration and exchange. The existing methodologies for ontology development fail to provide a comprehensive coverage for different steps, e.g. pre-development, development and post-development, which are necessary for successfully developing ontologies. We propose a generic and comprehensive methodology that puts ontology engineering on a firm scientific foundation and at the same time provides a collaborative environment for effective knowledge sharing and reuse. Furthermore, our approach also provides a way for automatically extracting frequent terms from the data to construct an ontology in a bottom-up fashion. The performance of our methodology has been evaluated by developing different ontologies to solve the real life applications, e.g. fault diagnosis and root cause investigation and spare parts maintenance.

Decision support for improved service effectiveness using domain aware text mining

This paper presents a decision support system Domain Aware Text & Association Mining (DATAM) which has been developed to improve after-sales service and repairs for the automotive domain. A novel approach that compares textual and non-textual data for anomaly detection is proposed. It combines association and ontology based text mining. Association mining has been employed to identify the repairs performed in the field for a given symptom, whereas, text mining is used to infer repairs from the textual instructions mentioned in service documents for the same symptom. These in turn are compared and contrasted to identify the anomalous cases. The developed approach has been applied to automotive field data. Using the top 20 most frequent symptoms, observed in a mid-sized sedan built and sold in North America, it is demonstrated that DATAM can identify all the anomalous symptom - repair code combinations (with a false positive rate of 0.04). This knowledge, in the form of anomalies, can subsequently be used to improve the service/trouble-shooting procedure and identify technician training needs.

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.

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.

A fuzzy logic based approach for modeling quality and reliability related customer satisfaction in the automotive domain

This paper presents an approach to assess quality and reliability related customer satisfaction from field failure data at each individual customer level. The quality satisfaction has been modeled based on number of failures and severity of failures, while, reliability satisfaction has been modeled based on number of visits to dealer and time span between visits. The satisfaction modeled at an individual vehicle (customer) level is further aggregated to a vehicle model level to determine overall satisfaction of customers with that specific vehicle model. A fuzzy logic approach is used to construct the satisfaction model. A grid search technique is used to tune the model parameters such that the output of the model for specific vehicle models matches with survey based ratings assigned to the vehicle models.

Application of ontology guided search for improved equipment diagnosis in a vehicle assembly plant

In the body shop of an automobile assembly plant, having access to correct and timely diagnostic information is very important for solving equipment and tooling maintenance problems. Variation Reduction Adviser (VRA) is an internal General Motors (GM) system that contains information related to the problems encountered in process, their root cause and possible solutions. This paper presents our work on ontology based diagnosis, where, a thesaurus (which is a simple form of ontology) has been used for retrieving diagnostic information. A thesaurus has been developed from existing problem descriptions and their solutions written in a natural language (such as English). A systematic methodology has been developed for the creation of a thesaurus. The results of ontology based diagnostic information retrieval have been compared with ‘exact match’ information retrieval.

Development, analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization

Graphical abstractDisplay Omitted HighlightsQuantitative modeling of customer satisfaction for consumer vehicles is proposed.Real-world service and sales datasets of five vehicle models are used.Model sensitivity to various features of the service datasets is studied.Classification rules for identifying dissatisfied customers are obtained.Method for identifying high-priority vehicular problems is proposed. Consumer-oriented companies are getting increasingly more sensitive about customers perception of their products, not only to get a feedback on their popularity, but also to improve the quality and service through a better understanding of design issues for further development. However, a consumers perception is often qualitative and is achieved through third party surveys or the companys recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile companys warranty records for different vehicle models and suggest a data mining procedure to assign a customer satisfaction index (CSI) to each vehicle model based on the perceived notion of the level of satisfaction of customers. Based on the developed CSI function, customers are then divided into satisfied and dissatisfied customer groups. The warranty data are then clustered separately for each group and analyzed to find possible causes (field failures) and their relative effects on customers satisfaction (or dissatisfaction) for a vehicle model. Finally, speculative introspection has been made to identify the amount of improvement in CSI that can be achieved by the reduction of some critical field failures through better design practices. Thus, this paper shows how warranty data from customers can be utilized to have a better perception of ranking of a product compared to its competitors in the market and also to identify possible causes for making some customers dissatisfied and eventually to help percolate these issues at the design level. This closes the design cycle loop in which after a design is converted into a product, its perceived level of satisfaction by customers can also provide valuable information to help make the design better in an iterative manner. The proposed methodology is generic and novel, and can be applied to other consumer products as well.

Quantitative modeling of customer perception from service data using evolutionary optimization

This paper proposes a novel method for using the service (field failure) data of consumer vehicles to estimate customer perception. To achieve this, relevant variables are extracted from the vehicle service data and provided as input to the proposed algorithm which then comes up with an optimized mathematical model for predicting the Customer Satisfaction Index or CSI. The methodology is then extended in a way that allows comparison of the CSIs of two or more vehicle models, thus providing a measure of the markets perceived quality of a vehicle model relative to another. Validation against the Consumer Reports data shows that customer experiences and their consequent response in surveys are indeed a reflection of the numbers the service data provides. However, it is argued that the proposed model is more generic than the Consumer Reports because: (1) it doesnt rely on consumer surveys and (2) it can be used to assess individual consumer level satisfaction.

Survival Analysis of Automobile Components Using Mutually Exclusive Forests

An ability to predict the mileage at failure of components in a complicated system, particularly in automobiles, is a challenging task. In the current work, a methodology for estimating the distribution of failure and survival rate of automobile components affected by multiple factors is presented. A novel adaptation of an ensemble recursive partitioning and tree-based learning method, mutually exclusive forest, is introduced. The proposed method is capable of handling a high dimensional dataset and maximizing the extracted information to estimate the distribution of mileage at failure of automobile components. Each tree in the proposed mutually exclusive forest uses a mutually exclusive set of factors in each of its constituent decision trees to classify the failure data. Information across the trees is combined to obtain the failure rate distribution of an automobile component with respect to mileage. A case study, based on real-world field failure data and censored data of automobile components, is presented to evaluate the proposed algorithm. Results show similar results to the C-Forest approach in terms of prediction quality, while generating models with significantly lower space that are easier to interpret.

Identification and impact assessment of high-priority field failures in passenger vehicles using evolutionary optimization

This paper presents a method for prioritizing field failures in passenger vehicles based on their potential for improvement in the Customer Satisfaction Index (({text{ CSI}}_{QSR})). ({text{ CSI}}_{QSR}) refers to Customer Satisfaction Index pertaining to quality, service and reliability of the vehicle and is referred to as simply ‘CSI’ in this paper. A novel method for quantitative modeling of the CSI function using an evolutionary approach was presented in [3]. Such a CSI function can be used to capture individual customer’s perception of a vehicle model as well as to compare overall CSI of multiple vehicle models. This work is firstly aimed at improving the previous modeling technique and validating it against Consumer Reports reliability ratings. More importantly, it presents a procedure for identifying high impact field failures based on their CSI Improvement Potential (CIP). These high priority field failures can then be further studied for root cause analysis.