Misjudging intermittent faults as permanent faults is the major cause of the problems of false alarms, Cannot Duplication and No Fault Found in aircraft avionics. To address this problem, we propose an approach for diagnosis of PFs and IFs in the context of discrete event systems models. Since fault events are usually unobservable, it is difficult to discriminate PF from IF events captured in succeeding sensors. Thereafter, the environmental stresses are treated as fault events and a stress level evaluation algorithm based on interval grey relational degree is given to identify the fault events by evaluating the level of the correlative environmental stresses. Finally, an example of aeronautic gyroscope is presented to demonstrate the proposed approach, and the analysis results show the approach is effective and feasible. It is a novel and effective way to discriminate both PFs and IFs of discrete event systems without the assumption of knowing the fault types (permanent faults and intermittent faults) a priori.

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The conventional model-based diagnosis usually potentially presumes faults are persistent and does not take intermittent faults into account, which is the major cause of the problems of false alarms, cannot duplicate and no fault found in aircraft avionics and present a tremendous challenge to prognostics and health management. Aiming at the problem that the logical automaton proposed by Sampath et al. cannot distinguish between strings or states that are highly probable and those that are less probable, a stochastic automaton approach is given to distinguish the fault types by extending the fault model to include both permanent faults and intermittent faults. The notions of A-and AA-diagnosability of permanent faults and intermittent faults for stochastic automaton are defined. Thereafter, the diagnoser with a probability matrix appended to each transition that can be used to update the probability distribution on the state estimate is constructed. Finally, an example of aeronautic gyroscope is presented to demonstrate the proposed approach, and the analysis results show that this approach is able to discriminate the fault types within bounded delay if the system is A-and AA-diagnosable. In our previous paper, we have extended the logical automaton model, and investigated the stochastic automaton approach in this article.

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Associating environmental stresses (ESs) with built-in test (BIT) output is an important means to help diagnose intermittent faults (IFs). Aiming at low efficiency in association of traditional time stress measurement device (TSMD), an association model is built. Thereafter, a novel approach is given to evaluate the integrated environmental stress (IES) level. Firstly, the selection principle and approach of main environmental stresses (MESs) and key characteristic parameters (KCPs) are presented based on fault mode, mechanism, and ESs analysis (FMMEA). Secondly, reference stress events (RSEs) are constructed by dividing IES into three stress levels according to its impact on faults; and then the association model between integrated environmental stress event (IESE) and BIT output is built. Thirdly, an interval grey association approach to evaluate IES level is proposed due to the interval number of IES value. Consequently, the association output can be obtained as well. Finally, a case study is presented to demonstrate the proposed approach. Results show the proposed model and approach are effective and feasible. This approach can be used to guide ESs measure, record, and association. It is well suited for on-line assistant diagnosis of faults, especially IFs.