Technical University of Munich, Department of Computer Science
MQM Paper: [Struss 97d] 


Model-based Diagnosis for Industrial Applications
Peter Struss
Technical University of Munich
Dept. of Computer Science
Orleansstr. 34 D-81667 München,
OCC'M Software GmbH
Gleissentalstr. 22
D-82041 Deisenhofen,

Powerful Systems for Automated Diagnosis - Urgently Needed

The task of diagnosing technical systems becomes increasingly challenging. Their complexity is growing rapidly, and so is the complexity of the task to identify and remove the causes of malfunctions. This holds for all types of systems, production plants, communication networks, transportation systems, but also for consumer products. At the same time, the potential impact of malfunctions grows considerably in terms of costs entailed, threats to human health, and destruction of the environment. In many cases, the capabilities of human operators, users of such systems, maintenance staff etc. do not catch up with this development. Modern ships perfectly illustrate these three aspects: complex systems with tremendous effects in case of an accident are operated by a dramatically reduced crew with limited technical skills. As a result, powerful systems that are able to support or automatically perform the diagnostic process are urgently needed.

The Hardware Basis Is Available

For many, if not most, devices that pose serious diagnosis problems, the computing power is already available or will be in the near future. PCs are or can be used within repair shops. Moreover, many devices carry their own processors, usually for control purposes. This is not only true for large systems, such as ships or production lines, but also for small devices and consumer products like photocopiers, cars or cameras. Shrinking in terms or size and price and expansion in terms of memory and speed allows to equip a broad range of technical systems with a hardware basis for diagnosis software to run on-board. Advances in networks and telecommunication open another possibility: solutions can be found even though the subject of the trouble may be quite distant for the available diagnosis knowledge: the mechanic in the workshop can be connected to a diagnosis server via internet, or a car can transmit data to a service center and receive instructions. So, everything is prepared for the exploitation of advanced computer systems for on-board, off-board and remote diagnosis, but:

What is Missing: the Advanced Diagnosis Software

Of course, there already exist on-board diagnostics, for instance, on control units of various car subsystems, but they deliver a symptom rather than a diagnosis; fault localization and identification starts with the symptom. Also, there are systems available for certain devices, again, such as cars, that guide a technician in diagnosis based on predefined decision trees. But all existing solutions fail to meet the challenge we discussed above. They are hand crafted software programs (or decision trees) targeted to very special types of devices or even individual ones. There are no diagnostics for the diesel injection system, but for EDC7.11 for an Audi type X, Variant Y, year 1996, etc. Producing the variants of diagnosis systems for all variants of the devices under consideration is a costly process, often not feasible because of the sheer number of variants, and too costly in cases where it might be feasible in principle. As a result, a fundamental, inevitable requirement for almost all industrial applications is

The Essence of Model-based Diagnosis

The technology at model-based systems addresses these issues and provides the solid foundation for the future generation of automated diagnosis systems. What enables humans to effectively and efficiently solve diagnosis problems even when they have to deal with new devices and unanticipated symptoms and, hence, cannot simply draw upon previous cases and experience? It is their capability to apply general, principled knowledge, namely knowledge about Based on this knowledge, the diagnosis process is guided by reasoning about how to collect more information about the device (e.g. by probing, testing and direct inspection) in order to discriminate among competing diagnosis hypotheses. Model-based diagnosis systems work exactly the same way: They do not require a pre-specified set of symptom-fault associations, which will always be incomplete, limited, and expensive to maintain. Instead, they are based on an explicit representation of the principled knowledge that allows to derive such associations automatically. The crucial modules of a model-based diagnosis system are The library captures the knowledge specific for a particular application domain, say mechatronic car subsystem or electrical circuits; its model fragments can be re-used for the class of devices in this domain. The structure description is the (only) device-specific element. It allows to automatically generate a device behavior model using elements form the library (see Fig. 1). This is, besides the observations, the input to the diagnosis algorithm which is general and domain-independent and, hence, can be reused across several application domains.
Fig. 1: Generation of Diagnostic Systems

In a nutshell, a diagnosis system of this type works as follows:

These key principles are fairly basic and straightforward. Nevertheless, they provide the foundation for diagnostic systems that are truly revolutionary in two respects.

A Revolution in the Technology of Diagnosis

From the description given, one can see that model-based diagnosis overcomes serious limitations of previous approaches. The diagnosis procedure guarantees completeness of the results with respect to the phenomena captured by the models, the faults modeled, if any, and the observations provided. This means that model-based diagnosis provides the foundation for future diagnosis systems that can take on the challenges we discussed in the beginning. An implication of this approach that is equally important concerns the way diagnosis software is produced.

A Revolution in the Software Process of Constructing Diagnosis Systems

As we pointed our earlier, the only device-specific element is captured by the representation of the device structure. Often, it will be possible to import it from CAD systems. From this information and the elements of the domain-specific model library, a device behavior model can be derived automatically. The general diagnosis algorithm operating on this device model forms a diagnosis system tailored to the particular device which means it is constructed without having to write a single line of code:

Since the diagnosis framework can be bought from the shelf, the entire work of programming and knowledge-base construction is in the establishment and maintenance of the model library. This is essential for solving the variant problem discussed above and, hence, for turning a desirable goal into a feasible one from a practical and economic point of view. Even if the actual runtime system is not a model-based one for some reason, model-based diagnosis provides useful tools for supporting programming of diagnostics, for instance for validation or as a starting point for the programmer who might want to violate the completeness of the model-based results in a controlled way. Also, the model-based technology can be used to generate the input to traditional types of diagnosis tools, e.g. by generating decision trees.

Another important advantage is that the generation of diagnostics requires only a "blueprint" of the device as opposed to a physical prototype and, hence, can be done concurrently with the design process.

The Current State of the Technology

After about 20 years of research, a number of prototypes that reflect practical applications have been constructed in the past few years, including examples from devices like digital and analogue circuits, power networks, photo copiers, helicopters, and coolant systems of power plants. Moreover, recently, emerging industrial applications become visible, mainly a demonstrators. The Tiger project (ESPRIT, see [Milne et al. 94]) included diagnosis of a gas turbine fuel system. Model-based diagnosis of car subsystems (both on-board and off-board) is a target of two projects: "Vehicle Model-based Diagnosis" (VMBD, a BRITE-EURAM project, see and INDIA (a project funded by the German government, see NASA will equip the "New Millennium" space crafts with model-based systems as basis for increased autonomy through self-diagnosis and self-reconfiguration.

The first commercial tools are now available, such as rodon (R.O.S.E. Informatik GmbH) and RAZ'R (OCC'M Software GmbH, see It has to be mentioned that the scope of current successful applications and tools is still restricted. Industrial processes (as opposed to devices with a clear component structure), systems containing embedded software, and occurrence of structural faults may pose problems for which research is continuing and no general of-the-shelf solution may be available. Also, providing the necessary model library is obviously a crucial and potentially extensive task. Even in cases where mathematical models are used in the engineering disciplines they may require re-structuring and transformation to be of utility for model-based diagnosis. Systems supporting or automating this model acquisition process are still limited. All this means that availability of expertise in the field of model-based systems is still important both for selecting applications that are tractable for the state of the art and for designing the solutions. MONET ("Model-based systems and qualitative reasoning", a network of excellency in the ESPRIT program, see ) is dedicated to the promotion of technology transfer in this area. In summary, industrial exploitation of model-based diagnosis technology is in its starting phase, and companies that appreciate the tremendous impact of the technology and invest in its exploitation will have a significant advantage over their competitors.

Beyond Diagnosis

It is fairly obvious that the model-based technology is relevant nor only to diagnosis. In fact, much of the knowledge captured by a domain model library is not specific to the task of diagnosis. Knowledge about the behavior of components is equally fundamental to other tasks, such as design, repair, or re-configuration of a device. Failure modes and effects analysis (FMEA) is a task which can be supported by systems that automatically analyze the impact of component failures based on a behavior model (see e.g. [Struss/Malik/Sachenbacher 96]). The same models might be used for test generation, design for diagnosability, and training purposes. The model library can be considered as a repository of corporate technological knowledge. Based on an explicit and coherent representation, this knowledge can be shared across different phases of the life cycle of a product, either by humans or by automated model-based problem solvers (see Fig. 2).
Fig. 2: The Corporate Technological Knowledge Base

The impact of fully exploiting this technology will be extremely important to

Thus, model-based systems will be a major contribution to improve the mastering of the product life cycle.


[Beschta et al. 93] Beschta, A., Dressler, O., Freitag H., Montag, M., Struss, P.: A model-based approach to fault localization in power transmission networks. In: Intelligent Systems Engineering, Vol. 2, No. 1 , pp. 3-14, 1993.

[Milne et al. 94] Milne, R., et al.: TIGER: Realtime Situation Assessment of Dynamic Systems. In: Intelligent Systems Engineering, Vol. 3, No. 3 , pp. 103-124, 1994.

[Struss/Malik/Sachenbacher 96] Struss, P., Malik, A., Sachenbacher, M.: Qualitative Modeling is the Key to Automated Diagnosis. 13th World Congress of IFAC, San Francisco, CA, USA, Pergamon, 1996.

More Literature

[Dressler/Struss 96] Dressler, O., Struss, P.: The Consistency-based Approach to Automated Diagnosis of Devices. In: Brewka, G. (ed.), Principles of Knowledge Representation, CSLI Publications, Stanford, ISBN 1-57586-057-0, pp. 267-311, 1996.

[Hamscher/Console/deKleer 92] Hamscher, W., Console, L., de Kleer, J. (eds.): Readings in Model-based Diagnosis, Morgan Kaufman Publishers, Mountain View, 1992.

[Struss 97] Struss P. Model-based and qualitative reasoning: An introduction. In: Annals of Mathematics and Artificial Intelligence 19 (1997) III-IV, Baltzer Science Publishers, pp. 355 - 381, 1997.

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created: Jakob Mauss, November 28, 1997, last updated: Jakob Mauss, December 4, 1998