Research On Key Technology And System For Turbopump Health Monitoring Of Liquid Rocket Engine

Since the first man-made earth satellite revolves round the earth, the space technology is developing rapidly and its achievements have attracted worldwide attention. Meanwhile, it is necessary to enhance the reliability and security of liquid-propellant rocket engine (LRE). Turbopump is the important component of LRE. Because of its execrable working circumstance, its faults evolve very fast and severely endanger the safety of LRE. The goal of health monitoring technology and system for turbopump is to detect faults in time. With various control measures, the system can mitigate faults deterioration and diminish their impact on engine and ground test-bed. Thus, the research on health monitoring technology and system for turbopump is of importance to guarantee the safety of LRE in the synthetic ground test.Aiming at the existing principal problem of turbopump health monitoring in LRE ground test, granted by National 863 Project and Natural Science Foundation of China (NSFC), this dissertation carries out the research on health monitoring technology and system for turbopump combining theory with application. The main contents and innovative work can be summarized as follows:1. The art of state of LRE health monitoring technology and system is analyzed in detail. Based on this, their developing trend is described and the key techniques that need to be resolved presently for LRE turbopump health monitoring are investigated. These techniques consist of the monitoring technology in start-up process, the abrupt fault detection technology in steady working process, the information extraction technology for post-test mass data and the system construction technology for turbopump health monitoring.2. The feature extraction and monitoring methods for turbopump start-up process signal are studied and proposed.(1) The variation of rotational speed in turbopump start-up process presents difficulty to vibration feature extraction. For this reason, the computed order analysis method based on digital resample is studied and realized, which can compute the speed and extract order features effectively.(2) The turbopump start-up process monitoring methods based on the speed and order features are brought forward. With turbopump historical test data, the validity of the methods is verified.(3) Associating with support vector regression, the turbopump start-up process fault detection methods based on nonlinear model and speed safety belt are presented and verified with turbopump test data.Validated by LRE firing test data, the computed order analysis method via digital resample and the monitoring methods based on the speed and order features can recognize availably the initial failure in turbopump start-up process, which supplies an effective approach to the fault detection in the entire working process of turbopump.3. The adaptive detection algorithms based on multiple domain features are studied and proposed for the turbopump abrupt faults in steady working process.(1) After the mechanism and vibration characteristics of turbopump abrupt faults are analyzed, the multiple effective features in time domain and frequency domain are selected. Furthermore, the order domain extraction method of the feature frequency in speed fluctuating condition is put forward.(2) The feature threshold model and adaptive computation theory are studied. According to actual requirement, the adaptive detection algorithm based on multiple time domain features for abrupt faults is improved and realized.(3) The red-line algorithm in frequency domain is studied and the amplitude tracking of feature frequency components is realized. Then, the adaptive red-line detection algorithm based on feature frequency for abrupt faults is proposed.Validated by LRE firing test data, the order domain extraction method can effectively extract the stable feature frequency components, which supplies an effective approach to solve the difficulty of feature extraction as a result of speed fluctuation. The adaptive detection algorithm based on multiple time domain features can avoid the false alarm proceeding from undersized vibration. Compared with traditional methods, it is more applicable to the abrupt fault detection in turbopump steady process. Meanwhile, the adaptive red-line detection algorithm based on feature frequency can detect effectively the abrupt fault of turbopump blade abscission.4. The turbopump post-test health analysis method based on manifold learning is studied and proposed.(1) After the mathematical description, fundamental conception and primary methods of manifold learning are studied in depth, a new strategy for the useful information extracting from mass test data is presented.(2) The manifold features of data are described and the validity of the anomaly recognition for turbopump test data is analyzed. Then, a mass data anomaly recognition algorithm based on manifold features is proposed and verified with simulation and turbopump test data.The research shows that the quantity of data can be greatly reduced after the manifold features are extracted from mass data by the presented algorithm. In the meanwhile, the recognition for turbopump normal and abnormal state can be carried out by means of the difference among manifold regions in feature space.5. The system construction technology for turbopump health monitoring is studied.(1) Aiming at the small-sized requirement for monitoring system, a new solution for the embedded monitoring system based on digital signal processor (DSP) is presented and the soft hardware design is carried out. Then, the real-time health monitoring subsystem is built up.(2) The turbopump post-test health analysis subsystem is constructed by integrating multiple domain analysis with dimensionality reduction method.The research shows that the subsystem based on DSP has the real-time capability of monitoring turbopump health condition. It is able to compute promptly the speed, extract the vibration features in time domain and frequency domain, and then detect faults. The post-test health analysis subsystem can further analyze and confirm the running state of turbopump.In a summary, the key technology and system for LRE turbopump health monitoring are studied thoroughly. Aiming at the difficulty of feature extraction in start-up process, the monitoring methods for turbopump start-up process signal are proposed. The multiple domain features of vibration signal are extracted, and then the adaptive detection algorithms are presented for the turbopump abrupt faults in steady working process. After the manifold learning methods are studied in depth, the anomaly recognition algorithm based on manifold features for turbopump mass test data is brought forward. Finally, the turbopump health monitoring system is designed and built, which is verified with historical test data and LRE firing test data. It is believed that these conclusions can provide academic reference and engineering example for research and development of LRE and reusable engine health monitoring technology and system.