An Integrated Diagnostic Process for Automotive Systems

Abstract

made system monitoring an inevitable component of future vehicles. Consequently, a continuous monitoring and early warning capability that detects, isolates and estimates size or severity of faults (viz., fault detection and diagnosis), and that relates detected degradations in vehicles to accurate remaining life-time predic- tions (viz., prognosis) is required to minimize downtime, improve resource management via condition-based maintenance, and minimize operational costs. The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicle systems is monitored and managed. The availability of data (sensor, command, activity and error code logs) collected during nominal and faulty conditions, coupled with intelligent health management techniques, ensure continuous vehicle operation by recognizing anomalies in vehicle behavior, isolating their root causes, and assisting vehicle operators and maintenance personnel in executing appropriate remedial actions to remove the effects of abnormal behavior. There is also an increased trend towards online real-time diagnostic algorithms embedded in the Electronic Control Units (ECUs), with the diagnostic troubleshooting codes (DTCs) that are more elaborate in reducing cross-subsystem fault ambiguities. With the advancements in remote support, the maintenance technician can use an intelligent scanner with optimized and adaptive state- dependent test procedures (e.g., test procedures generated by test sequencing software, e.g., (47)) instead of pre-computed static paper-based decision trees, and detailed maintenance logs (cases) with diagnostic tests performed, their outcomes, test setups, test times and repair actions can be recorded automatically for adaptive diagnostic knowledge management. If the technician can not isolate the root cause, the history of sensor data and symptoms are transmitted to a technical support center for further refined diagnosis. The automotive industry has adopted quantitative simulation as a vital tool for a variety of functions, including algorithm design for ECUs, rapid prototyping, programming for hardware-in-the-loop simulations (HILS), production code generation, and process management documentation. Accordingly, fault detection and diagnosis (FDD) and prognosis have mainly evolved upon three major paradigms, viz., model-based, data-driven and knowledge-based approaches. The model-based approach uses a mathematical representation of the system. This approach is applicable to systems, where satisfactory physics-based models of the system and an adequate number of sensors to observe the state of the system are available. Most applications of model-based diagnostic approach have been on systems with a relatively small number of inputs, outputs, and states. The main advantage of a model-based approach is its ability to incorporate a physical understanding of the process into the process monitoring scheme. However, it is difficult to apply the model-based approach to large-scale systems because it requires detailed analytical models in order to be effective.