Research On Novelty Detection Methods Oriented To Flight Vehicle Health Management

Health management (HM) technologies for flight vehicle, such as space shuttle, carrier rocket, fixed-wing aircraft and helicopter, are of significance to enable operating condition monitoring, degradation trend assessment, remaining useful life prediction and safe operation assurance. Thus HM is considered the premise and foundation that supposes condition based maintenance, even autonomic logistics. Occasionally occurred accidents warn us constantly that it is imperative to research on key techniques of HM.Condition monitoring is an important theme in the architecture of vehicle HM. And it provides foundation for other themes including diagnosis, prognosis and mitigation. One of the challenges in condition monitoring of flight vehicles or their key components is lack of fault samples and prior knowledge about fault modes. Accordingly, this dissertation takes the turbopump of a liquid rocket engine as object and researches on novelty detection methods oriented to vehicle HM. The detailed contents and innovative work can be summarized as follows.1. Facts associated with the generalization of novelty detection methods are deeply analyzed. Philosophy of popular novelty detection methods is systematically studied and some excellent methods of them are analyzed via numerical simulation. Oriented to HM, design principles are summarized and three strategies for fault detection with novelty detection methods are presented.The research shows that novelty detection is able to recognize known and unknown abnormities according to known samples. Some issues, such as robustness trade-off, generalization, computational complexity, detection strategies that suit for different cases, should be considered when develop novelty detectors.2. One-class support vector machine (OCSVM) based novelty detection method is deeply studied for turbopump condition monitoring.(1) Two kinds of OCSVMs with different geometrical explanations are introduced in detail. The influence of OCSVM's parameters to its performance is thoroughly analyzed integrating numerical simulation. And principles to set OCSVM's parameters are given accordingly.(2) OCSVM based novelty detector is applied to detect historical test data of the turbopump. OCSVM's parameters are optimized according to their set principles and cross-validation of detection results, and by which false alarms are reduced.The above research shows that OCSVM's performances are closely related to its parameter set. The principle error of OCSVM and the bad consistency of feature vectors are main factors that cause false alarms. These false alarms can be reduced evidently by increasing OCSVM's Gaussian kernel width and by introducing an offset scaling parameter.3. An online adaptive novelty detection algorithm based on double-offset OCSVM is presented and applied to the detection of turbopump real test data.(1) A sequential minimal optimization (SMO) algorithm is introduced to solve the quadratic optimization problem in OCSVM, which reduced the computational complexity of OCSVM.(2) A double-offset OCSVM online detection algorithm based on SMO algorithm is developed. In this algorithm, detection error caused with OCSVM itself and changing environmental conditions is eliminated. Abnormal samples detected are prevented from contributing to the adaptive update of the detection model.(3) The online detection algorithm presented above is applied to turbopump historical test data detection, which also verifies this algorithm.The research shows that SMO algorithm can improve the training efficiency of OCSVM evidently. The online detection algorithm based on double-offset OCSVM is able to detect kinds of novel events of the turbopum, involving vane shedding and rub-impact. And there are not any false alarms.4. A complete sample region description (CSRD) method that integrates OCSVM with incremental clustering is presented for novelty detection and turbopump condition monitoring.(1) An incremental clustering algorithm that enables sample compression is presented to solve the learning problem of large samples in CSRD.(2) A CSRD method integrating OCSVM with incremental clustering is presented and a complete description model is established for the time-domain statistical features of the turbopump.(3) A turbpump fault detection system is constructed and the CSRD method is integrated into the system. Validity of the CSRD method is validated with turbopump historical test data.The research shows that the CSRD method is able to extract a more representative sample set from large rude samples incrementally. The representative set distributes uniformly and covers the entire target region. And the size of the representative set is under control. The CSRD method is also able to generate a boundary surrounding the target region. Detection results of turbopump historical test data demonstrate that the method can identify different spikes in vibration signals caused by abnormal events such as vane shedding, rub-impact and sensor faults. And there are not any false alarms.5. Novelty detection is exploited to fault diagnosis. Diagnosis methods based on parallel OCSVMs and series OCSVMs are presented and validated.The research shows that compared with diagnosis methods based on SVM, OCSVM based methods have better diagnosis efficiency and expansibility. And they are able to recognize known and unknown conditions. Their validity is verified with the classification of simulation data and the classification of turbopump historical test data.