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The evolution of the diagnostic equipment for automotive application is the direct effect of the implementation of sophisticated and high technology control systems in the new generation of passenger cars. One of the most challenging issues in automotive diagnostics is the ability to assess, to analyze, and to integrate all the information and data supplied by the vehicle's on-board computer. The data available might be in the form of fault codes or sensors and actuators voltages. Moreover, as environmental regulations get more stringent, knowledge of the concentration of different species emitted from the tailpipe during the inspection and maintenance programs can become of great importance for an integrated powertrain diagnostic system. A knowledge-based diagnostic tool is one of the approaches that can be adopted to carry out the challenging task of detecting and diagnosing faults related to the emissions control system in an automobile.

This paper presents the real time implementation of detection filters for the diagnosis of incipient failures in electronically controlled internal combustion (IC) engines. The detection filters are implemented in a production vehicle. Recent results [1] have demonstrated the feasibility of a model-based failure detection and isolation (FDI) methodology for detecting partially failed components in electronically controlled vehicle subsystems. The present paper describes the real time application of the FDI concept to the detection of faults in sensors associated with the engine/controller In a detection filter, the performance of the engine/controller system is continuously compared to a prediction based on sensor measurements and an analytical model (typically a control model) of the system. Any discrepancy between actual and predicted performance is analyzed to identify the unique failure signatures related to specific system components.

A number of automotive diagnostic equipment and procedures have evolved over the last two decades, leading to two generations of on-board diagnostic requirements (OBDI and OBDII), increasing the number of components and systems to be monitored by the diagnostic tools. The goal of On-Board Diagnostic is to alert the driver to the presence of a malfunction of the emission control system, and to identify the location of the problem in order to assist mechanics in properly performing repairs. The aim of this paper is to suggest a methodology for the development of an Integrated Powertrain Diagnostic System (EPDS) that can combine the information supplied by conventional tailpipe inspection programs with onboard diagnostics to provide fast and reliable diagnosis of malfunctions.

In this paper, we propose an IC engine fuel system diagnostic algorithm based on a discrete-event nonlinear observer using the production oxygen sensor. A mean value engine model is used to describe the engine dynamics. A procedure for designing the discrete event based observer is presented and applied to estimate important engine variables using the measured binary oxygen sensor output. The estimated variables are then used to perform diagnostics of the fuel system of the IC engine. Experimental results on a multi-cylinder production engine are presented to demonstrate the effectiveness of the proposed method.

The viability of many new technologies for improving the drivability and safety of a vehicle has improved with the availability of advanced software and hardware tools. On-line diagnosis of steering system faults is one such area on which a lot of attention has been focused. When used in a manually driven automobile this technology can improve the safety of the vehicle by providing the driver with the fault information. While when used with a computer controlled steering (as envisaged in many of the IVHS technologies) it is of even greater importance, because electronic fault information is crucial to the proper functioning of many such systems. This paper deals with the design of a linear unknown input observer (UIO) based residual generator for steering system diagnosis. The observer was designed based on an accepted model of the automatic car steering problem. The observer was validated through experiments conducted on the OSU-autonomous vehicle.

This paper presents the application of detection filters to the diagnosis of sensor and actuator failures in automotive control systems. The detection filter is the embodiment of a model-based failure detection and isolation (FDI) methodology, which utilizes analytical redundancy within a dynamical system (e.g., engine/controller) to isolate the cause and location of abnormal behavior (i.e., failures). The FDI methodology has been used, among other applications, in the aerospace industry for fault diagnosis of inertial navigation systems and flight controllers. This paper presents the philosophy and essential features of FDI theory, and describes the practical application of the method to the diagnosis of faults in the throttle position sensor in an electronically controlled IC engine. The paper also discusses the incorporation of FDI systems in the design process of a control strategy, with the aim of increasing reliability by embedding diagnostic features within the control strategy.

The introduction of advanced electronic controls in passenger vehicles over the last decade has made traditional diagnostic methods inadequate to satisfy on- and off-board diagnostic needs. Due to the complexity of today's automotive control systems, it is imperative that appropriate diagnostic tools be developed that are capable of satisfying current and projected service and on-board requirements. The performance of available diagnostic and test equipment is still amenable to further improvement, especially as it pertains to the diagnosis of incipient and intermittent faults. It is our contention that significant improvement is possible in these areas. This paper briefly summarizes the evolution of on- and off-board diagnostic tools documented in the published literature, with the aim of giving the reader an understanding of their capabilities and limitations, and it further proposes alternative solutions that may be adopted as a basis for an advanced diagnostic instrument.

Highway and roadway surface measurement is a practice that has been ongoing for decades now. This sort of measurement is intended to ensure a safe level of road perturbances. The measurement may be conducted by a slow moving apparatus directly measuring the elevation of the road, at varying distance intervals, to obtain a road profile, with varying degrees of resolution. An alternate means is to measure the surface roughness at highway speeds using accelerometers coupled with high speed distance measurements, such as laser sensors. Vehicles out rigged with such a system are termed inertial profilers. This type of inertial measurement provides a sort of filtered roadway profile. Much research has been conducted on the analysis of highway roughness, and the associated metrics involved. In many instances, it is desirable to maintain an off-road course such that the course will provide sufficient challenges to a vehicle during durability testing.

Fault diagnosis for automotive systems is driven by government regulations, vehicle repairability, and customer satisfaction. Several methods have been developed to detect and isolate faults in automotive systems, subsystems and components with special emphasis on those faults that affect the exhaust gas emission levels. Limit checks, model-based, and knowledge-based methods are applied for diagnosing malfunctions in emission control systems. Incipient and partial faults may be hard to detect when using a detection scheme that implements any of the previously mentioned methods individually; the integration of model-based and knowledge-based diagnostic methods may provide a more robust approach. In the present paper, use is made of fuzzy residual evaluation and of a fuzzy expert system to improve the performance of a fault detection method based on a mathematical model of the engine.