The Lydia Project

The Lydia project aims at the development of a systems modeling language and compiler framework that enables the automatic generation of model-based embedded software, that provides complex, expensive, possibly life-critical systems with the intelligence, such as prognostic health management and autonomic reconfiguration, needed for optimum dependable operation.

Model-Based Systems

In the following, we provide a rationale for our research into model-based systems. As the impressive, exponential increase in hardware performance/cost ratio is expected to be continued for at least another decade, trillions of embedded systems will pervade in every aspect of our society. A sharp contrast to the potential blessings of this development is the looming software complexity crisis. At present, complex systems already require many millions of lines of code. As systems become more and more integrated, it will becomes increasingly difficult to anticipate responsive behavior at design-time. The dynamic, run-time response capabilities required to cover these issues will only add to the crisis.

It is becoming apparent that under the current traditional IT paradigms this huge complexity makes it impossible to deliver software that provides system capability, correctness, and availability, at any reasonable cost. Moreover, there will not be enough skilled IT professionals to design, test, debug, install, configure, operate, diagnose, and maintain such complex systems, assuming such tasks will stay within human ability to begin with. A natural solution to mastering system complexity is to introduce autonomy, just as found in complex organisms such as humans. This approach, referred to as autonomic computing by IBM [1], aims at providing systems with the intelligent capability to install and run themselves, adjust to varying conditions, anticipate events, monitor their health, recover from incidents, generate contingency plans, etc., without costly and error-prone human intervention. The software complexity involved with realizing autonomy is clearly no less of an inhibiting factor than the complexity problems mentioned earlier. This applies in particular to autonomic embedded systems given the additional constraints such as real-time performance, and energy efficiency, and the fact that such systems involve various disciplines such as physics, mechanical, electrical, and computer engineering, apart from software engineering. Typical for the complexity problems at hand is the recent statement, that in embedded systems a software team doubles every four years to keep up with Moore's Law [2].

A promising way of overcoming this obstacle on the road towards autonomous embedded systems is to apply a model-based approach. In the model-based approach, relevant knowledge about the system is concentrated in a compositional model. In this generic approach, so-called ``intelligent'' modes of operation at run-time, such as health prognosis, fault diagnosis, maintenance, recovery planning, as well as testability analysis at design-time, are realized through an infra-structure comprising application-independent engines (using algorithms from AI) that reason in terms of a model of the system. This approach allows software developers to focus on model development, which greatly amortizes development cost as the self-management code is automaticallly synthesized. Separating modeling concerns from IT-typical concerns such as model source compilation, hardware-software codesign, system interfacing, algorithm development, etc., also allows application domain experts without specific IT skills to efficiently take part in the development process. The compositionality of the model further optimizes the development process by enabling the use of reusable component libraries, possibly produced by third-party component vendors.

The Lydia Project

The Lydia project aims at the development of a systems modeling language which enables the automatic generation of embedded software, that provides complex, expensive, possibly life-critical systems with the intelligence, such as prognostic health management (PHM), needed for optimum reliable operation.

Lydia (Language for sYstem DIAgnosis) is a new modeling language to describe systems such as satellites, copiers, cars, in such a way that their behavior can be simulated, as well as analyzed for faults (fault diagnosis). In a simulation context, a Lydia model is compiled to a simulator. In the fault diagnostic context, a Lydia model is compiled to embedded, fault diagnostic software that continuously monitors the real system and tries to accurately isolate the root cause of any system malfunctioning. Of the above two contexts, the fault diagnostic application of Lydia is of particular interest, and is motivated by the aforementioned importance of model-based PHM in intelligent systems.

The Lydia project comprises two basic research themes:

While starting out with fault diagnosis, we eventually intend to generalize this approach, realising a modeling environment that automatically generates embedded software that identifies any parameter or variable that is yet undetermined. This covers fault diagnosis (fault/health parameters), simulation (normal state variables, health prognosis), but also component system identification (transfer parameters, coefficients), reliability analysis (failure rate parameters). These parameters should automatically be determined or updated as the embedded model tracks the real system (i.e., is continuously being fed with system observations). Related analytic procedures include testability analysis.

Apart from analysis, system models can also be used for synthesis. In particular, the synthesis (planning, scheduling) of reconfiguration and/or recovery procedures in case system analysis has deduced a mission-threatning situation, requires much of the same information (component health parameters, transfer functions, system structure, etc.). By playing the model forward (simulation), recovery procedures can even be tested before actually executed.

Example Applications

Systems that are currently modeled in Lydia include satellites and scientific payloads at ESA, wafer steppers at ASML (within the Tangram project), the LOFAR self healing network (LOFAR project). Parts of the methodology will also be applied with the Trader project (Fault Diagnosis and Reliability Modeling of Philips Digital TVs). A recent project in which a new generation of fault diagnosis algorithms will be developed within the Lydia tool set is the FINESSE project (together with TU/e, Oce Technologies, and LogicaCMG, ranked first out of 18 submissions to the Dependability tender of the national STW/PROGRESS funding agency, of which only 2 have been accepted). Much more applications are foreseen as the project gets under way. Applications that are currently being considered include PHM systems for automobiles, as well as automatically guided vehicles (AGVs) in the context of the CABS project.

Read more about available MSc projects.


Currently the project team is comprised of (in alphabetical order):


The following members have been on the team in the past (in alphabetical order): See the publications section for their work.


Apart from the software (student versions to be made publically available at a later date) our work is currently documented in terms of the following publications:


[1] IBM, "Autonomic Computing: IBM's Perspective on the State of Information Technology".

[2] "Intelligente Apparaten, Een visie op embedded systemen in Nederland", Embedded Systems Instituut, Eindhoven, Nederland, Nov. 2002.


The Lydia project is lead by Arjan J.C. van Gemund
Associate Professor, Delft University of Technology (