Lessons Learned from using a Livingstone Model to Diagnose a Main Propulsion System
Adam Sweet, Anupa R. Bajwa · 2003
NASA researchers have demonstrated that qualitative, model-based reasoning can be used for fault detection in a Main Propulsion System (MPS), a complex, continuous system. At the heart of this diagnostic system is Livingstone, a discrete, propositional logic-based inference engine. Livingstone comprises a language for specifying a discrete model of the system and a set of algorithms that use the model to track the system's state. Livingstone uses the model to test assumptions about the state of a component – observations from the system are compared with values predicted by the model. The intent of this paper is to summarize some advantages of Livingstone seen through our modeling experience: for instance, flexibility in modeling, speed and maturity. We also describe some shortcomings we perceived in the implementation of Livingstone, such as modeling continuous dynamics and handling of transients. We list some upcoming enhancements to the next version of Livingstone that may resolve some of the current limitations. PURPOSE This paper is intended to give an overview of some strengths and weaknesses of the model-based diagnosis tool Livingstone 2 (L2). The authors have seen sufficient interest in L2 to make this overview worthwhile. This paper also lists mitigations of L2's weaknesses that have been used on the applications project PITEX, and what weaknesses will be addressed in the next version of Livingstone, L3. This paper is not intended as a general survey of the model-based diagnosis field; different model-based techniques each have their own strengths and weaknesses.