Automotive Applications [chapter]

2009 Optical Science and Engineering  
The aim of this thesis is to contribute to improved diagnosis of automotive vehicles. The work is driven by case studies, where problems and challenges are identified. To solve these problems, theoretically sound and general methods are developed. The methods are then applied to the real world systems. To fulfill performance requirements automotive vehicles are becoming increasingly complex products. This makes them more difficult to diagnose. At the same time, the requirements on the diagnosis
more » ... itself are steadily increasing. Environmental legislation requires that smaller deviations from specified operation must be detected earlier. More accurate diagnostic methods can be used to reduce maintenance costs and increase uptime. Improved diagnosis can also reduce safety risks related to vehicle operation. Fault diagnosis is the task of identifying possible faults given current observations from the systems. To do this, the internal relations between observations and faults must be identified. In complex systems, such as automotive vehicles, finding these relations is a most challenging problem due to several sources of uncertainty. Observations from the system are often hidden in considerable levels of noise. The systems are complicated to model both since they are complex and since they are operated in continuously changing surroundings. Furthermore, since faults typically are rare, and sometimes never described, it is often difficult to get hold of enough data to learn the relations from. Due to the several sources of uncertainty in fault diagnosis of automotive systems, a probabilistic approach is used, both to find the internal relations, and to identify the faults possibly present in the system given the current observations. To do this successfully, all available information is integrated in the computations. Both on-board and off-board diagnosis are considered. The two tasks may seem different in nature: on-board diagnosis is performed without human integration, while the off-board diagnosis is mainly based on the interactivity with a mechanic. On the other hand, both tasks regard the same vehicle, and information from the on-board diagnosis system may be useful also for off-board diagnosis. The probabilistic methods are general, and it is natural to consider both tasks. The thesis contributes in three main areas. First, in Paper 1 and 2, methods are developed for combining training data and expert knowledge of different kinds to compute probabilities for faults. These methods are primarily developed with on-board diagnosis in mind, but are also applicable to off-board diagnosis. The methods are general, and can be used not only in diagnosis of technical system, but also in many other applications, including medical diagnosis and econometrics, where both data and expert knowledge are present. The second area concerns inference in off-board diagnosis and troubleshooting, and the contribution consists in the methods developed in Paper 3 and 4. ii The methods handle probability computations in systems subject to external interventions, and in particular systems that include both instantaneous and non-instantaneous dependencies. They are based on the theory of Bayesian networks, and include event-driven non-stationary dynamic Bayesian networks (nsDBN) and an efficient inference algorithm for troubleshooting based on static Bayesian networks. The framework of nsDBN event-driven nsDBN is applicable to all kinds of problems concerning inference under external interventions. The third contribution area is Bayesian learning from data in the diagnosis application. The contribution is the comparison and evaluation of five Bayesian methods for learning in fault diagnosis in Paper 5. The special challenges in diagnosis related to learning from data are considered. It is shown how the five methods should be tailored to be applicable to fault diagnosis problems. To summarize, the five papers in the thesis have shown how several challenges in automotive diagnosis can be handled by using probabilistic methods. Handling such challenges with probabilistic methods has a great potential. The probabilistic methods provide a framework for utilizing all information available, also if it is in different forms and. The probabilities computed can be combined with decision theoretic methods to determine the appropriate action after the discovery of reduced system functionality due to faults. v Att lära från data för diagnos ställer särskilda krav på algoritmerna som används, och i artikel 5 har ett fem olika metoder anpassats till diagnos-problemet och deras prestanda har jämförts. Genom hela avhandlingen har arbetet drivits av fallstudier av delsystem i en modern lastbil, där olika problem och svårigheter har identifierats. Teoretiskt sunda och generella metoder har utvecklats för att lösa dessa problem. Metoderna har sedan applicerats på de riktiga systemen i lastbilen. vi Preface I believe searching faults is like a detective's work. We observe the system, discuss the hidden relations, using whatever we know about the system, and draw conclusions about whether there are faults present and, if so, which faults. Therefore, searching faults and doing diagnostic work is about understanding relations between observations and different faults, and to distinguish the relevant information in the observations. To design a diagnosis system, we have to find the relations. To perform diagnostic work, we have to reason using the relations and the current observations. There are several different methods for learning the hidden relations in systems to diagnose: building models, using data, applying expert systems, and so on. However, digging deeper into the problem designing a diagnosis system, we notice that the available information is (often) not sufficient to exactly determine if there are faults present, nor to distinguish between them. We are left with a bunch of possible explanations. This fact leads into the field of probability theory. When dealing with probabilities, and in particular probabilities about "real-world" events, such as "what is the probability that this truck is fault free?", one need to know what "probability" is. So, what is probability? Before beginning the work with this thesis, I would have said something like "Well, the probability is the relative frequency. I suppose." However, I must confess, I had some problems with this interpretation. First, even if fault F is present in 1 out of 100 trucks, i.e. has relative frequency 0.01, what is the probability that the fault is present in this particular truck? vii viii Second, if a person I trust tells me that this truck is fault free, what is the probability that this the truck is fault free then? It is reasonable that it depends on how much I trust the person? My problems with the interpretation of probability are, at least philosophically, solved through inspiring and interesting discussions with Mikael Sternard and Mathias Johansson at the Signals and Systems group at Uppsala University five years ago. They introduced me to E. T. Jaynes' book Probability -the Logic of Science on probability as an extension to logic. According to Jaynes, probability is a property of the spectator and his state of knowledge rather than a "physical" property of the object. This gave me an understanding of probability as a measure of belief that has made this thesis possible. Without Mikael and Mathias it is highly probable that this thesis had been something completely different. One of the most important persons during the work with this thesis has been my supervisor Dr. Mattias Nyberg. He has supported me through this work by pushing my ideas further, and efficiently puncturing my bad ideas. He has always new questions coming up, and new ideas about how the world and the work is. It has been an intellectual challenge to work with Mattias -and I love challenges. This thesis has been performed as a collaborative industrial research project between Scania CV AB in Södertälje and the division of Vehicular Systems, Department of Electrical Engineering, Linköping University. I thank my managers at Scania for supporting this work and making it financially possible. Thanks to Prof. Lars Nielsen, for letting me join the Vehicular Systems group in Linköping, and to the people at the group, and in particular at the diagnosis group of Vehicular Systems, for the interesting discussions and for broadening my perspective on diagnosis (and many other things). Other persons that have been more important for this work are my cosupervisor Dr. Jose M. Peña, with his knowledge on Bayesian networks; Dr. Nils-Gunnar Vågstedt and Hans Ivendahl at Scania, with their encouragement, and "real-world related questions" that have helped me to focus on the real problems; and Prof. Petri Myllymäki and Hannes Wettig at the CoSCo group at Helsinki University for hosting me and introducing me to learning methods.
doi:10.1201/9781420067811.ch19 fatcat:34k62wml4bcc7n5tsm3xloy2iu