Integrated diagnosis and prognosis architecture for fleet vehicles using dynamic case-based reasoning

This paper presents a hybrid reasoning architecture for integrated fault diagnosis and health maintenance of fleet vehicles. The aim of this architecture is to research, develop and test advanced diagnostic and decision support tools for maintenance of complex machinery. Artificial intelligence based diagnostic approach has been proposed with particular reference to dynamic case-based reasoning (DCBR). This system refines an asynchronous stream of symptom and repair actions into a compound case structure and efficiently organizes the relevant information into the case memory. Diagnosis is carried out into two steps for fast and efficient solution generation. First the situation is analyzed based on observed symptoms (textual descriptions) to propose initial diagnosis and generate corresponding explanation hypothesis. Next, based on the generated hypothesis relevant sensor data is collected and corresponding data analysis modules are activated for data-driven diagnosis. This approach reduces the computational demands to enable fast experience transfer and more reliable and informed testing. This system also tracks the success rates of all possible hypotheses for a given diagnosis and ranks them based on statistical evaluation criteria to improve the efficiency of future situations. Since the system can interact with multiple vehicles it learns about several operating environments resulting in a rich accumulation of experiences in relatively very short time. A distributed and generic architecture of this system is outlined from technical implementation point of view which can be used for widespread applications where both qualitative and quantitative observations can be gathered. Further, a concept of expanding this architecture for carrying out prognostic tasks is introduced.