Research On The Intelligent Built In Test Fault Diagnosis Method And Its Applications To More-Electric Aircraft Electrical Power System

The Built-in Test (BIT) is an integral capability of the mission equipment whichprovides an on-board, automated test capability to detect, diagnose, or isolate systemfailures. It is an effective approach to improving testability and maintainability of acomplex system. However, with the increasing requirements of fault detection and themaintenance time, many problems of this conventional BIT technique are manifested,such as notorious False Alarm (FA), Cannot Duplicate (CND) and Retest OK (RTOK),which have some strong effects on the readiness of military aircraft, especially on that ofMore-Electric Aircraft (MEA). Due to the electric power widespread using in the MEA,the reliability and fault tolerance capability of More-Electric Aircraft Electrical PowerSystem (MEAEPS) must be significantly higher than conventional aircrafts. Furthermore,the MEA requires that more effective and efficient testing and fault diagnosis techniquesbe developed to improve the reliability of the EPS. So it is very important to study theintelligent BIT technique and its application in MEAEPS in order to improve theintegrated capability of MEA. Supported by the National Project—Research on keytechnologies of more-electric aircraft electrical system, this paper is aiming to in-depthresearch on theories and methods on intelligent BIT fault diagnosis in order to enhance thediagnostic capability of the BIT system of MEAEPS. The main contents of the dissertationare as follows:1. All of faults which may happen in MEAEPS are analyzed and summarized in thisdissertation, and a Failure Mode and Effect Analysis (FMEA) table is established. On thebasis of that, according to the selecting principle of fault detecting points, some soundfault detecting points of BIT system are selected.2. A mathematical description model of BIT dynamic system of aircraft electricalpower system is established. Aimed to the lack of reasonable basis in terms of selectingfault diagnosis methods employed in aircraft electrical power system, the shortage of BITfault diagnosis methods which now applied in aircraft electrical power system is analyzedbased on the given BIT mathematical model, and several methods which can improve thediagnosis capability of BIT system are given.3. Aimed to the shortage of-conditional BIT techniques employed in aircraftelectrical power system, an unsupervised clustering neural network based on competinglearning method is studied. Firstly, aimed to the drawbacks of original GLVQ network interms of classification; an improved IGLVQ is proposed. The IGLVQ algorithm adopts anew form of loss factor, and its learning rules are derived through finding a minimum ofthe loss function, which avoid the influence of the input space scale and the class numberto classification. Secondly, in order to overcome the common drawback of unsupervised networks that it can not using the prior classifying information, a LVQ layer is added tothe, IGLVQ network to construct a hybrid neural network model (HIGLVQ), whichimproves the ability to distinguish the similar classes. Thirdly, the new HIGLVQ networkhas been applied to the intelligent BIT system of the MEAEPS, and the results show thatthe proposed method has a good performance in pattem classification and it is promisingto improve the fault diagnosis capability of the BIT system.4. Through analyzing the characteristics of MEAEPS and the status-in-quo of studyon BIT false alarm of electronic system, the causes which influence BIT decision aregiven. A main cause is that the conditional BIT techniques which applied to the aircraftelectrical power system avoid intermittent faults and transient faults, which induce thehigh FAR of BIT system. On the basis of that, the mechanisms of how the intermittentfaults and the transient faults produce and the characteristics of them are analyzed. Basedon the probability mathematical model of BIT FAR, an important conclusion is provedthat identifying these faults can reduce the FAR of BIT system effectively. And a falsealarm filter model, based on HIGLVQ-optimal Bayes decision, is proposed. Theexperimental results show that this proposed model can eliminate effectively false alarmcaused by the intermittent faults and the transient faults in BIT system of MEAEPS.5. In order to enhance the intelligent diagnosis level, fault predicting theories appliedto BIT system of MEAEPS are studied. Aimed to the slow-emerging faults, a faultpredicting method based on frequency spectrum and one-dimension time series signal isproposed, which uses the Hidden Markov Model (HMM). On the basis of that, a novelRadial Basis Hidden Markov Model (RBHMM) and its online parameter-updatingalgorithm are given since the original HMM model can not update in real time. Using theRBHMM-based fault predicting model to predict the one-dimension time series faultsignals of the BIT system of MEAEPS, the proposed method shows that it has a betterperformance than that of the original HMM model in term of fault predicting, and it canimprove the intelligent fault diagnosis capability of the BIT system of MEAEPSeffectively.