Study On The Techniques Of Fault Detection And Diagnosis For High Pressure Staged Combustion LOX/Kerosene Rocket Engine

Some key technologies involved in the health monitoring for a certain high pressurestaged combustion LOX/Kerosene rocket engine have been comprehensively studied,analyzed and designed in the thesis. These include the configuration, optimization, faultdetection and data accommodation of sensors, the fault simulation, real-time fault detectionand diagnosis methods for the engine, the integrated design, implementation and validationof the system, and the health inspection technique in the preflight, inflight and postflightphases of reusable engines. Results of the research can not only provide solid theoreticaland application foundation in the development of health monitoring system for expendablerocket engines, but also they can provide important reference in the improvement ofreliability and safety for future reusable liquid-propellant rocket engines.Firstly, due to the extreme operating condition, rapid occurrence and severe effect offaults, and the bad repetition of fault modes, it is difficult to obtain the feature of faultmodes, the diagnostic knowledge and the sample data of engines. So modularizedsimulation models of faults are founded and a visual fault simulation software system isdeveloped based on the hierarchical decomposition of the engine’s structure. Then faultsimulation and effect analysis are carried out for the principal faults such as the cavitationin the oxidant pump, the ablation of the combustion chamber throat, and so on.Comparison results show that the simulation results are in good agreement with the realtest data of the engine. It can provide important fault sample data in the research of faultdetection and diagnosis methods for the engine.Secondly, due to the intrinsical uncertainty existed in the fault detection and diagnosis,especially in the absence of prerequisite knowledge and sufficient sample data, uncertainreasoning and decision methods are proposed and studied. The methods are all based onthe cloud theory, which is effective in the integrated description and manipulation of therandomness and fuzziness. They include the following three aspects such as the parameterselection, configuration, fault detection and data accommodation for sensors, the real-timefault detection method and the uncertain diagnosis reasoning method for the engine.In the first aspect, a parameter selection method for fault detection with unchangeableability in the classification of the engine’s faults is developed. Then a mathematical modelof configuration and optimization for sensors is built based on the cloud theory to satisfythe performance index constraint of fault detection and diagnosis. A computation method isalso proposed based on the particle swarm optimization. In addition, a fault detection anddata accommodation method for sensors is developed based on the cloud neural networkand verified with test data of the LOX/Kerosene rocket engine. Results show that themethod is effective. By this way, reliable data for the fault detection and diagnosis of theengine can be obtained. In the next aspect, a real-time fault detection method for LRE is proposed based onthe cloud neural network to meet the accurate, in-time and real-time requirements. For themethod, a framework and structure of the cloud neural network are designed, and theforward propagation based computation and the backward propagation based learningalgorithms are developed. Then the method is verified and validated with test data of theLOX/Kerosene rocket engine in both of its steady processes and transient processes. Thesetransient processes include a starting process, a process from the rated condition to the highoperating condition, a process from one high operating condition to another high operatingcondition with high mixing ratio, a process from one high operating condition with highmixing ratio to one high operating condition. These steady processes include a process ofrated condition, a process of high operating condition, and a process of high operatingcondition with high mixing ratio. Results show that the developed method can not onlyproduce right and timely estimations for operating condition of the engine with no falsealarm and missing alarm, and also it can detect the fault earlier than other fault detectionalgorithms such as the RS, IATA, ACA and RBF.In the last aspect, the cloud Petri net model of fault diagnosis and its modeling methodbased on the productive rules are proposed. The model is used to describe and analyze theengine’s behavior and the change between states effectively. Then an uncertain reasoningmethod for the fault diagnosis and discrimination of its reasoning results is proposed anddeveloped for the engine based on the constructed cloud Petri net model. The method isverified and validated with test data of the LOX/Kerosene rocket engine. Results showthat the method is able to isolate and diagnose the typical faults of the engine such as theblock of pipeline before oxidizer turbopump, the cavitation of oxidizer turbinepump, andso on.Thirdly, the function and requirements of HMS for LRE are analyzed thoroughly, andthe common problems are also abstracted due to the many deficiencies existed in thecurrent HMS such as tight coupling of system structure and function modules, poorreusability and interoperability, the slow response for the demand change and maintenance.Then a layered, open and reusable framework of HMS is analyzed and designed based onthe Data-Model-View-Control hierarchy. Moreover, based on the combination of theprevious research results, a real-time fault detection system for the LOX/Kerosene rocketengine is designed and realized. The system is verified and validated with test data andground tests of the engine. Results show that the system can meet all the requirements inthe engineering application and is able to work in a high real-time and on-line way. It cannot only acquire the parameters accurately, and also produce no false alarm and missingalarm. So it is another important breakthrough in the research of health monitoring forLRE in China.Fourthly, in order to meet the higher requirement of health monitoring for reusable engines, key health inspection technologies are analyzed on principle and designedsummarily such as the preflight integrated performance test, real-time flight data recordingand postflight structure detection. The principle structure of automatic test systems for theon-ground integrated performance test and intelligent Built-in Test in preflight phases ofengines are analyzed and designed. Then the principle structure of real-time flight datarecorder is analyzed and designed which is combined with fiber gratings and otheradvanced sensor technology. It is composed of auxiliary function module, system controlmodule, data acquisition and storage module and recall-bach module. Moreover, theprinciple structure of borescopic inspection detection system is also analyzed and designedbased on image acquisition and detection and identification of structural damage. Theseresults will provide significantly important reference in the research of health inspectiontechnology and development of engineering application systems for the preflight, inflightand postflight phases of future reusable LRE.