IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014
ABSTRACT An ability to predict the mileage at failure of components in a complicated system, part... more ABSTRACT 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.
Consumer-oriented companies are getting increasingly more sensitive about customer's perception o... more Consumer-oriented companies are getting increasingly more sensitive about customer's 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 consumer's perception is often qualitative and is achieved through third party surveys or the company's recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile company's 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 customer's 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.
2009 IEEE International Conference on Automation Science and Engineering, 2009
In the body shop of an automobile assembly plant, having access to correct and timely diagnostic ... more 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
ABSTRACT This paper presents a decision support system ‘Domain Aware Text & Association M... more ABSTRACT 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.
Page 1. Knowl Inf Syst DOI 10.1007/s10115-011-0409-1 REGULAR PAPER A domain-specific decision sup... more Page 1. Knowl Inf Syst DOI 10.1007/s10115-011-0409-1 REGULAR PAPER A domain-specific decision support system for knowledge discovery using association and text mining Dnyanesh Rajpathak · Rahul Chougule · Pulak Bandyopadhyay ...
International Journal of Computer Integrated Manufacturing, 2011
Data inconsistency and data mismatch are critical problems that limit data interoperability and h... more 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 ...
The International Journal of Advanced Manufacturing Technology, 2009
In the body shop of an automobile assembly plant, having access to correct and timely information... more In the body shop of an automobile assembly plant, having access to correct and timely information is very important in solving problems encountered in the assembly process. The Variation Reduction Adviser (VRA) system used within General Motors (GM) is a database containing problems encountered in this process and their possible solutions. The VRA acts as an electronic logbook that shares information across shifts within a plant as well as across multiple plants. The VRA also serves as a problem-solving tool by which solutions to problems encountered may be retrieved and reused. To function effectively as a problemsolving tool, it is important that relevant information is quickly retrieved from the VRA database. Traditionally, keyword-based retrieval strategies have been used. In these approaches, the user types in a list of words or phrases and those records in the database that contain those words or phrases exactly as typed are retrieved. The problem with this approach is that records containing words or phrases that are semantically related to the ones typed in but not exactly the same are not retrieved. For instance, if the user types "lefthand side," the traditional keyword search will not find records that contain the abbreviation "LHS." This paper describes a search mechanism based on a thesaurus (a simple version of an ontology) that overcomes this problem. It describes the standard criteria to measure the effectiveness of a search, defines a new criterion, and shows that in terms of these criteria, the ontology-guided approach gives better search results than the exact match mechanism. The results are shown in the context of real searches during the use of the VRA in a GM assembly plant.
This paper presents an approach to assess quality and reliability related customer satisfaction f... more 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.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014
ABSTRACT An ability to predict the mileage at failure of components in a complicated system, part... more ABSTRACT 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.
Consumer-oriented companies are getting increasingly more sensitive about customer's perception o... more Consumer-oriented companies are getting increasingly more sensitive about customer's 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 consumer's perception is often qualitative and is achieved through third party surveys or the company's recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile company's 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 customer's 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.
2009 IEEE International Conference on Automation Science and Engineering, 2009
In the body shop of an automobile assembly plant, having access to correct and timely diagnostic ... more 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
ABSTRACT This paper presents a decision support system ‘Domain Aware Text & Association M... more ABSTRACT 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.
Page 1. Knowl Inf Syst DOI 10.1007/s10115-011-0409-1 REGULAR PAPER A domain-specific decision sup... more Page 1. Knowl Inf Syst DOI 10.1007/s10115-011-0409-1 REGULAR PAPER A domain-specific decision support system for knowledge discovery using association and text mining Dnyanesh Rajpathak · Rahul Chougule · Pulak Bandyopadhyay ...
International Journal of Computer Integrated Manufacturing, 2011
Data inconsistency and data mismatch are critical problems that limit data interoperability and h... more 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 ...
The International Journal of Advanced Manufacturing Technology, 2009
In the body shop of an automobile assembly plant, having access to correct and timely information... more In the body shop of an automobile assembly plant, having access to correct and timely information is very important in solving problems encountered in the assembly process. The Variation Reduction Adviser (VRA) system used within General Motors (GM) is a database containing problems encountered in this process and their possible solutions. The VRA acts as an electronic logbook that shares information across shifts within a plant as well as across multiple plants. The VRA also serves as a problem-solving tool by which solutions to problems encountered may be retrieved and reused. To function effectively as a problemsolving tool, it is important that relevant information is quickly retrieved from the VRA database. Traditionally, keyword-based retrieval strategies have been used. In these approaches, the user types in a list of words or phrases and those records in the database that contain those words or phrases exactly as typed are retrieved. The problem with this approach is that records containing words or phrases that are semantically related to the ones typed in but not exactly the same are not retrieved. For instance, if the user types "lefthand side," the traditional keyword search will not find records that contain the abbreviation "LHS." This paper describes a search mechanism based on a thesaurus (a simple version of an ontology) that overcomes this problem. It describes the standard criteria to measure the effectiveness of a search, defines a new criterion, and shows that in terms of these criteria, the ontology-guided approach gives better search results than the exact match mechanism. The results are shown in the context of real searches during the use of the VRA in a GM assembly plant.
This paper presents an approach to assess quality and reliability related customer satisfaction f... more 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.
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Papers by Rahul Chougule