1. A method for verifying, validating and improving a fault model, said method comprising: providing an initial fault model that identifies correlations between symptoms in a vehicle and failure modes in the vehicle; providing field failure data that includes vehicle symptoms and vehicle failure modes of many vehicles that are in the field being operated; performing a what-if analysis using the field failure data that includes using subject matter expert (SME) knowledge to determine a subset of the most significant failure modes and the most significant symptoms, where the most significant failure modes are determined according to frequency of occurrence, cost, and customer walk-home occurrences; learning simulation parameters from the field failure data; simulating faults, by a processor running simulation software, using the learned simulation parameters where simulated faults include a relationship between and one or more failure modes and one or more symptoms; revising the initial fault model using the subset of the most significant failure modes determined by the what-if analysis and the simulated faults; analyzing, by a diagnostic reasoner, the simulated faults using the revised fault model to generate estimated failure modes; correlating the estimated failure modes to the simulated faults to determine true detection and false alarm rates; and performing a benefit analysis by relating the true detection and false alarm rates to costs.