Fault dependency (D)-matrix is a systematic diagnostic model [7] to capture the hierarchical system-level fault diagnostic information consisting of dependencies between observable symptoms and failure modes associated with a system. Constructing a D-matrix from first principles and updating it using the domain knowledge is a labor intensive and time consuming task. Further, in-time augmentation of D-matrix through the discovery of new symptoms and failure modes observed for the first time is a challenging task. Here, we describe an ontology-based text mining method for automatically constructing and updating a D-matrix by mining hundreds of thousands of repair verbatim (typically written in unstructured text) collected during the diagnosis episodes. In our approach, we first construct the fault diagnosis ontology consisting of concepts and relationships commonly observed in the fault diagnosis domain. Next, we employ the text mining algorithms that make use of this ontology to identify the necessary artifacts, such as parts, symptoms, failure modes, and their dependencies from the unstructured repair verbatim text. The proposed method is implemented as a prototype tool and validated by using real-life data collected from the automobile domain.

In this paper, we propose a generic library of problem-solving methods for scheduling applications. Although some attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision. In our approach, we subscribe to the task-method-domain-application knowledge modeling framework which provides a structured organization for the different components of the library. At the task level, we construct a generic scheduling task ontology to formalize the space of scheduling problems. At the method level, we construct a generic problem-solving model of scheduling that generalizes from the variety of approaches to scheduling problem-solving, which can be found in the literature. The generic nature of this model is demonstrated by constructing seven methods for scheduling as an alternative specialization of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for developing scheduling applications

In this paper we describe a generic library of problem-solving methods (PSMs) for scheduling applications. Although, some attempts have been made in the past at developing libraries of scheduling methods, these only provide limited coverage: in some cases they are specific to a particular scheduling domain; in other cases they simply implement a particular scheduling technique; in other cases they fail to provide the required degree of depth and precision. Our library is based on a structured approach, whereby we first develop a scheduling task ontology, and then construct a task-specific but domain independent model of scheduling problem-solving, which generalises from specific approaches to scheduling problem-solving. Different PSMs are then constructed uniformly by specialising the generic model of scheduling problem-solving. Our library has been evaluated on a number of real-life and benchmark applications to demonstrate its generic and comprehensive nature.