APSO-RVM for Fault Detection of Liquid Rocket Engines Test-bed
نویسندگان
چکیده
منابع مشابه
Hybrid-Knowledge-Models-Based Intelligent Fault Diagnosis Strategies for Liquid-propellant Rocket Engines
This paper focuses on a qualitative fault diagnosis method based on the integration and fusion of shallow and deep knowledge for liquid-propellant rocket engines (LRE). The paper firstly clarifies the concept and the types of LRE diagnosis knowledge. Later, from the isomorphic transform point of view, the paper analyses the correlation of different knowledge and knowledge representation, and fo...
متن کاملFault Injection Test Bed for Clock Violation
In this paper, the International Data Encryption (IDEA) algorithm synthesis models will be used as test encryption algorithm. The Xilinx Digital clock manager component will be used for generation of clocks for different frequencies and phase shifts. The encryption output with faults introduced and without faults introduced is compared as a function of ratio of used clock frequency and maximum ...
متن کاملUnsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring
This article describes the results of applying four unsupervised anomaly detection algorithms to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms. ...
متن کاملSymbolic identification for fault detection in aircraft gas turbine engines
This article presents a robust and computationally inexpensive technique of component-level fault detection in aircraft gas-turbine engines. The underlying algorithm is based on a recently developed statistical pattern recognition tool, symbolic dynamic filtering (SDF), that is built upon symbolization of sensor time series data. Fault detection involves abstraction of a language-theoretic desc...
متن کاملFault Detection and Identification of Automotive Engines Using Neural Networks
Fault detection and isolation (FDI) in dynamic data from an automotive engine air path using artificial neural networks is investigated. A generic SI mean value engine model is used for experimentation. Several faults are considered, including leakage, EGR valve and sensor faults, with different fault intensities. RBF neural networks are trained to detect and diagnose the faults, and also to in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Technology Journal
سال: 2012
ISSN: 1812-5638
DOI: 10.3923/itj.2012.1496.1501