Objectives and State of the Art

Industrial Objectives and Expected Achievements Industrial Need and Issues Involved

Stricter environmental requirements and the desire for better comfort and higher safety raises performance standards for current and even more so for future vehicles. This translates into vehicles with increased sophistication. On the other hand, reliability, availability and maintainability have become essential demands of the automotive product. Increased reliability is of course pursued with proper design solutions. However, since many components of the car are subject to wear and deterioration, a significant increase in availability can be gained with the introduction of on-board monitoring and diagnostic systems that can warn the driver before failures occur, thus providing condition-based maintenance. These on-board tools must be integrated with appropriate off-board diagnostic systems that can lead to rapid and effective repair solutions. Subsystems with an electronic control unit and a high number of transmitted signals are typical components which can be monitored by such a diagnostic solution. There are the test procedures of such a nature that the functions are non-transparent to repairing personnel.

The long-term industrial objectives are ambitious. An increase of vehicle availability by reduction of vehicle breakdown due to wear deterioration of at least one order of magnitude. These causes are responsible of nearly 50% of malfunctions according to statistics recorded by EUROQUALITY. A reduction of repair times of nearly 50% with respect to current best practice is envisioned. At the moment only emission related systems have been taken into account for monitoring: see California Air Resources Board Regulations (CARB) for On-Board Diagnostics (OBD) II. This action could be relevant as an example for future European legislation. No other diagnostic provisions have yet been taken, apart from SAE/ISO standardisa tion concerning the diagnostic interfaces between on-board systems and off board front end equipment (mainly for basic display functions concerning the fault codes of the connected control units). The SAE/ISO standard includes also the possibility of "extended services" to be used by the individual manufacturers.

The goal of the project is the development of an integrated environment containing general diagnostic strategies, a library of exemplary models and a diagnostic shell, which will allow the generation of custom integrated on-board and off-board systems. The approach will be based on the knowledge based systems (KBS) technology, relying on different kinds of models.

There is a need to develop an integrated approach to the design, development and execution of off-board and on-board vehicle diagnostics. The field of off-board vehicle diagnosis is still maturing. The field of on-board vehicle diagnosis is virtually non-existent. Until recently, the capability for on-board diagnosis has been relatively primitive or non-existent. It is now capable of supporting and adopting advanced techniques used for off-board diagnosis.

The integrated approach can be realised in a computer-based design and development environment. This environment should be capable of assisting the designer in addressing a combi nation of the on-board and off-board diagnosis issues and producing the complementary diagnostics. Wherever possible the environment should be capable of automating the design and production of diagnostics.

The prototype environment as developed in the project will need at least another 2 years productisation before being available in the market place. In addition. the tool kit can be regarde as a first step towards standardisation of vehicle diagnosis.

State-of-the-Art and Degree of Innovation

The model-based approach to diagnosis emerged in the early 80's as a solution to some limitations of the traditional expert system approach based on heuristic knowledge. Since then, research in knowledge-based systems has very much focused on exploiting model based reasoning methods in order to overcome limitations due to previously used expert systems technology based on empirical associations. The core of the new technology is an explicit, declarative model of a technical system that can be composed from context independent re-usable behaviour models of components taken from a domain library. Methods for formulating and using such models at a conceptual and qualitative level have been developed in the area of qualitative reasoning (Weld-de Kleer 90), (Faltings-Struss 92) and several partners in the project have made significant contributions, e.g. (Dague et. al. 90), (Lee-Ormsby 94), (Struss 88, 89, 92). Automated diagnosis is the task for which the most advanced work has been carried out (Console-Hamscher-de Kleer 92).

Today, model based diagnosis shows both a strong theoretical foundation in terms of logical theories (Reiter 87), (de Kleer-Mackworth-Reiter 90), (Console-Torasso 91), (Struss-Dressler 89), (Dressler-Struss 92, 94), (Dague-Deves-Raiman 88), (Tatar 94), (Tatar-Iwanowski 94) and considerable work on industrial problems, such as diagnosis of power networks (Beschta et al. 93), (Struss 92, 93), analogue circuits (Dague et al. 87, 94), diagnosis and failure modes and effects analysis of automotive subsystems (Price et al. 92; Hunt et al. 91, 93), (PROMOTEX). The survey indicates that the methods, techniques, and systems developed are mature enough for industrial applications, at least for selected domains and tasks. The first tools for model-based diagnosis are appearing on the market (IDEA, RODON).

In the automotive field diagnostic systems have been mainly developed for off-board applications. The first diagnostic approach in this field was the exploitation of on-board diagnosis provided from the ECU, using a hand-held system compatible with standard ISO 9141. Later, diagnostic tools employed knowledge-based systems technology, mainly based on fault-tree analysis. Examples for such systems are Strategy Engine from HP and TestBench from Carnegie Group. To overcome these limitations second-generation diagnostic systems have been developed: these systems are based on a structural and functional approach, the so-called deep-model. Examples of such systems are IDEA (Fiat Research Centre) and MDS (Daimler-Benz Research).

The main tasks to be solved for the application of model-based diagnosis at a broader scale are:

  • the generation of model libraries covering the domain,
  • the creation of environments that provide support for model building, maintenance of model libraries, and customisation of diagnostic systems,
  • the development of models and strategies appropriate for diagnosing dynamic systems.

From the point of view of the on-board diagnosis at the moment only emission related systems have been taken into account for monitoring/fault detection. No other diagnostic provisions have yet been taken, apart from SAE/ISO standardisation concerning the diagnostic interfaces between on-board systems and out-board front-end equipment, which are mainly related to electrical faults diagnosis (detection of short circuit, open circuit). Error codes generated by current on-board diagnosis systems are more related to classes of symptoms and fault detection, rather than to fault localisation. Centralised diagnostic systems are not present on the market. Some prototypes of on-board systems have been demonstrated on Japanese concept cars (Mitsubishi HSR-IV, Toyota AXV-V), while General Motors and Ford have started research on model-reference diagnostic techniques.

The main innovative aspects of the project are:

  • the use of model based diagnosis for vehicles,
  • the strong integration between on-board and off-board diagnostic systems,
  • definition of general guidelines for on-board vehicle diagnosis. where no standard approaches have yet been defined (apart from the cited OBD II).

The expected results are innovative per se:

  • a set of reusable model libraries for vehicle systems,
  • an environment for building libraries and model-based diagnostic systems,
  • a set of diagnostic systems, tested on a vehicle, that provide more coverage, guarantee for completeness of their results, more discriminative power, and capabilities of fault prediction.

One of the major advances of the project is the development of model libraries covering entire application domains and their evaluation in a variety of application examples. The scope of current modelling and diagnostic techniques will be expanded by not only providing models of correct and faulty behaviour but also by formalising knowledge about the transition from correct (or optimal) to faulty (or suboptimal) behaviour ("wear models").

This project is closely related to INDIA (intelligent diagnosis in application), proposed to BMBF (German Federal Ministry of Education and Research). The INDIA project investigates how the diagnosis relevant knowledge in an enterprise can be acquired automatically. This requires the integration of knowledge from the development about system functions and knowledge about potential faults of components, as it is recorded by quality assurance methods like FMEA.

In the UK-funded project "Jacquard", qualitative models of automotive electrical systems have been built (Lee, Ormsby 94) and used as part of prototype diagnosis and failure modes and analysis (FMEA) systems (Price et al. 92; Hunt-Price-Lee 93).

Currently, two related projects, both funded by the UK Engineering and Physical Sciences Research Council. The FLAME project, is developing further the FMEA work which was under taken on the Jacquard project. The end result of this project will be model-based tools to support FMEA for use by automotive design engineers in their daily work. The tools combine electrical circuit descriptions derived from an ECAD tool and a model of system function in order to reason about the effects of failure (Price et al. 95).

Dream is a much smaller project, concerned with the development of models that can be used to support the development of more traditional diagnostic systems. The aim here is to investigate the extent to which model-based techniques can assist the developers of conventional automotive diagnostic systems.


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These pages are maintained by Neil Taylor (nst@aber.ac.uk).
Dept of Computer Science, UW Aberystwyth (disclaimers).