Abstract
Model-Based Diagnosis (MBD) is a well-known approach to diagnosis in medical domains. In this approach, the behavior of a system is modeled and used to identify faulty components, i.e., once a symptom of abnormal behavior is observed, an inference algorithm is run on the system model and returns possible explanations. Such explanations are referred to as diagnoses. A diagnosis is an assumption about which set of components are faulty and have caused the abnormal behavior. In this work, we focus on the case where multiple observations are available to the diagnoser, collected at different times, such that some of these observations exhibit symptoms of abnormal behavior. MBD with multiple observations is challenging because some components may fail intermittently, i.e., behave abnormally in one observation and behave normally in another, while other components may fail all the time (non-intermittently). Inspired by recent success in solving classical diagnosis problems using Boolean satisfiability (SAT) solvers, we describe two SAT-based approaches to solve this MBD with multiple observations problem. The first approach compiles the problem to a single SAT formula, and the second approach solves each observation independently and then merges them together. We compare these two approaches experimentally on a standard diagnosis benchmark and analyze their pros and cons.
Highlights
A diagnosis problem arises when a system does not behave as expected
Encouraged by recent success of model-based diagnosis algorithms for classical diagnosis that are based on compilation to Boolean satisfiability (SAT) [11,12], we investigate in this work a SAT-based approach for MO-ModelBased Diagnosis (MBD) that considers both the intermittent non-intermittent axis as well as the strongfault model (SFM) WFM axis
An MBD problem is defined by a tuple SD, COMPS, OBS, where SD is a model of the diagnosed system, COMPS is the set of system components, and OBS is the observed behavior of the system
Summary
A diagnosis problem arises when a system does not behave as expected. The goal of diagnosis algorithms is to find the set of faulty components that caused the unexpected behavior of the system. We study the MBD problem when multiple observations of the system, taken in different time steps, are given. We focus on a model-based approach for solving MO-MBD, i.e., we assume that a model of the system’s behavior is given and infer diagnoses from the model and the observations. Given the behavior mode of a pipe in such a system and the observed input, one can infer the output value Both the model types (weak or strong) and the fault types (intermittent or nonintermittent) change the way we approach the MO-MBD problem. A second contribution of the paper is by presenting two SAT-based algorithms for solving the MO-MBD problem in the intermittent+WFM configuration: (1) one-SAT: solving all the observations at once, or (2) divide-and-join: solving each observation separately and combing the diagnoses in a way that is consistent with all of them.
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