A hybrid approach for aircraft fault diagnosis based on fault inference and fault identification

Abstract

A cockpit instrumentation system provides various elements of information for pilots. However, logical inference based on a cockpit instruments fault tree (FT) and reliability sometimes cannot give a correct diagnosis of failures. In addition, in flight control systems (FCS), a fault identification method based on the multiple-model (MM) estimator cannot find the basic fault cause. To deal with these problems, a hybrid approach which is capable of integrating inference and fault identification is proposed. In this approach, the event nodes of the FT which have correlations to the FCS are separated into modules. Each module corresponds to a fault mode of the FCS. To use these correlations, fault inference and the MM estimator can share fault diagnosis information. Simulation results show that the proposed approach is helpful in detecting the root cause of failure and is more correct than single fault diagnosis method.

Research Progress of Failure Mode Diagnosis Methods for Oil and Gas Field Pipelines

“…Failure diagnosis expert system adopts computerintelligent technology to simulate the knowledge and experience of failure diagnosis experts in related fields to complete the diagnosis of failure problems [18] The expert system comprises six parts: knowledge acquisition mechanism, knowledge base, database, inference machine, explanation mechanism, and man-machine interface. The domain problem is solved through the interaction of inference machine, knowledge base, and comprehensive database.…”

Research on identification method of heavy vehicle rollover based on hidden Markov model

“…HMM is a statistical model based on the Markov chain, which has a simple dynamic Bayesian network structure and has been widely used in pattern recognition and fault diagnosis [10]. In this paper, the vehicle rollover as a "fault-like" state, introducing the GMM correlation analysis method, presents a method of vehicle rollover state identi cation and prediction based on HMM.…”