A framework for real-time fault detection and diagnosis using temporal data |
| |
| Affiliation: | 1. Tijuana Institute of Technology, Calzada Tecnologico s/n, Tijuana, Mexico;2. Excel Medical Center, Paseo de los Heroes No. 2507, Zona Rio, Tijuana, Mexico;1. Department of Solid Mechanics, Royal Institute of Technology (KTH), 100 44 Stockholm, Sweden;2. SKF Engineering & Research Centre, P.O. Box 2350, 3430 DT Nieuwegein, The Netherlands;1. Key Laboratory of Space Utilization, Technology and Engineering Centre for Space Utilization, Chinese Academy of Sciences, 100094 Beijing, China;2. University of Chinese Academy of Sciences, 100049 Beijing, China;3. School of Software, Tsinghua University, 100084 Beijing, China |
| |
| Abstract: | Successful real-time sensor-based fault detection and diagnosis in large and complex systems is seldom achieved by operators. The lack of an effective method for handling temporal data is one of several key problems in this area. A methodology is introduced which advantageously uses temporal data in performing fault diagnosis in a subsystem of a Navy ship propulsion system. The methodology is embedded in a computer program designed to be used as a decision aid to assist the operator. It utilizes machine learning, is able to cope with uncertainty at several levels, and works in real-time. Program performance data is presented and analysed. The approach illustrates how relatively simple existing techniques can be assembled into more powerful real-time diagnostic tools. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
