Diagnosing multiple faults in digital systems

An efficient and fault-model independent method is presented for diagnosing multiple faults in digital system. The method is simpler to implement than the method that uses an ATMS with a constraint system or the method that uses a theorem prover with the minimal hitting set algorithm. Given the model of a Boolean digital system, an input vector, and a set of observed output values, the method computes the set of all minimal diagnoses (candidates). It begins with a system output that is incorrect. Using a system behavior model it computes a set of minimal potential candidates that account for the behavior of that incorrect output. The method then incrementally considers the remaining system outputs and extends the existing minimal potential candidate set to account for their behaviors. A minimal candidate is a minimal set of components whose hypothesized faulty outputs account for all correct and incorrect outputs of the system under some input vector. The authors show that minimal candidates do not contain components whose faulty outputs are either masked or nonobservable. They also show that for Boolean systems, supersets of candidates are candidates only for certain component fault models