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Symbolic identification for fault detection in aircraft gas turbine engines
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chakraborty, Subhadeep Sarkar, Soumik Ray, Asok |
| Copyright Year | 2012 |
| Abstract | 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 description from a general dynamical system structure, using state space embedding of output data streams and discretization of the resultant pseudo-state and input spaces. System identification is achieved through grammatical inference based on the generated symbol sequences. The deviation of the plant output from the nominal estimated language yields a metric for fault detection. The algorithm is validated for both single- and multiple-component faults on a simulation test-bed that is built upon the NASA C-MAPSS model of a generic commercial aircraft engine. |
| Starting Page | 422 |
| Ending Page | 436 |
| Page Count | 15 |
| File Format | PDF HTM / HTML |
| DOI | 10.1177/0954410011409980 |
| Volume Number | 226 |
| Alternate Webpage(s) | http://ecpower.utk.edu/Publications/docs/Gas_Turbine_Fault_Detection.pdf |
| Alternate Webpage(s) | http://web.utk.edu/~schakrab/Documents/Journal/GasTurbine.pdf |
| Alternate Webpage(s) | http://www.mne.psu.edu/ray/journalAsokRay/2012/233ChakrabortySarkarRay11.pdf |
| Alternate Webpage(s) | http://www.mne.psu.edu/Ray/journalAsokRay/2012/233ChakrabortySarkarRay12.pdf |
| Alternate Webpage(s) | https://doi.org/10.1177/0954410011409980 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
