Temporal Decision Trees: Model-based Diagnosis of Dynamic Systems On-Board

📝 Abstract

The automatic generation of decision trees based on off-line reasoning on models of a domain is a reasonable compromise between the advantages of using a model-based approach in technical domains and the constraints imposed by embedded applications. In this paper we extend the approach to deal with temporal information. We introduce a notion of temporal decision tree, which is designed to make use of relevant information as long as it is acquired, and we present an algorithm for compiling such trees from a model-based reasoning system.

💡 Analysis

The automatic generation of decision trees based on off-line reasoning on models of a domain is a reasonable compromise between the advantages of using a model-based approach in technical domains and the constraints imposed by embedded applications. In this paper we extend the approach to deal with temporal information. We introduce a notion of temporal decision tree, which is designed to make use of relevant information as long as it is acquired, and we present an algorithm for compiling such trees from a model-based reasoning system.

📄 Content

Journal of Artificial Intelligence Research 19 (2003) 469-512 Submitted 12/02; published 10/03 Temporal Decision Trees: Model-based Diagnosis of Dynamic Systems On-Board Luca Console luca.console@di.unito.it Claudia Picardi claudia.picardi@di.unito.it Dipartimento di Informatica, Universita di Torino, Corso Svizzera 185, I-10149, Torino, Italy Daniele Theseider Dupr´e dtd@mfn.unipmn.it Dipartimento di Informatica, Universita del Piemonte Orientale Spalto Marengo 33, I-15100, Alessandria, Italy Abstract The automatic generation of decision trees based on off-line reasoning on models of a domain is a reasonable compromise between the advantages of using a model-based ap- proach in technical domains and the constraints imposed by embedded applications. In this paper we extend the approach to deal with temporal information. We introduce a notion of temporal decision tree, which is designed to make use of relevant information as long as it is acquired, and we present an algorithm for compiling such trees from a model-based reasoning system.

1. Introduction The embedding of software components inside physical systems became widespread in the last decades due to the convenience of including electronic control into the systems them- selves. This phenomenon occurs in several industrial sectors, ranging from large-scale prod- ucts such as cars to much more expensive systems like aircraft and spacecrafts. The case of automotive systems is paradigmatic. In fact, the number and complexity of vehicle subsystems which are managed by software control increased significantly since the mid 80s and will further increase in the next decades (see Foresight-Vehicle, 2002), due to the possibility of introducing, at costs that are acceptable for such wide scale products, more flexibility in the systems, for e.g. increased performance and safety, and reduced emissions. Systems such as fuel injection control, ABS (to prevent blockage of the wheels while braking), ASR (to avoid slipping wheels), ESP (controlling the stability of the vehicle), would not be possible at feasible costs without electronic control. The software modules are usually installed on dedicated Electronic Control Units (ECUs) and they play a very important role since they have complete control of a subsystem: hu- man “control” becomes simply an input to the control system, together with inputs from appropriate sensors. For example, the position of the accelerator pedal is an input to the ECU which controls fuel delivery to the injectors. A serious problem with these systems is that the software must behave properly also in presence of faults and must guarantee high levels of availability and safety for the controlled system and for the vehicle. The controlled systems, in fact, are in many cases safety critical: the braking system is an obvious example. This means that monitoring the systems c⃝2003 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved. Console, Picardi, & Theseider Dupr´e behaviour, detecting and isolating failures, performing the appropriate recovery actions is a critical task that must be performed by control software. If any problem is detected or suspected the software must react, modifying the way the system is controlled, with the primary goal of guaranteeing safety and availability. According to recent estimates, about 75% of the ECU software deals with detecting problems and performing recovery actions, that is to the tasks of diagnosis and repair (see again Foresight-Vehicle, 2002). Thus the design of the diagnostic software is a very critical and time consuming activ- ity, which is currently performed manually by expert engineers who use their knowledge to perform the “Failure Mode and Effect Analysis (FMEA)” 1 and define diagnostic and recovery strategies. The problem is complex and critical per-se, but it is made even more difficult by a number of other issues and constraints that have to be taken into account: • The resources that are available on-board must be limited, in terms of memory and computing power, to keep costs low. This has to be combined with the problem that near real time performance is needed, especially in situations that may be safety critical. For example, for direct injection fuel delivery systems, where fuel is maintaned at a very high pressure (more than 1000 bar) there are cases where the system must react to problems within a rotation of the engine (e.g. 15 milliseconds at 4000 rpm), to prevent serious damage of the engine and danger to passengers. In fact, a fuel leakage can be very dangerous if it comes from a high pressure line. In this case it is important to distinguish whether a loss of pressure is due to such a leak, in order to activate some emergency action (for example, stop the engine), or to some other failure which can simply be signalled to the user. • In order to keep costs acceptable for a large scale product, the set of sensors available on board is usually limited to those necessa

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