Pressure chamber fault diagnosis model design based on segmented control and adaptive fuzzy neural network
Nan Zhang, Yong Ma · 2024
Read Pressure chamber fault diagnosis model design based on segmented control and adaptive fuzzy neural network by Nan Zhang, Yong Ma (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11607442.txt.
Description
Nan Zhang and Yong Ma built a fault finding model for aircraft cabin pressure chambers using a hybrid neural design. Pressure chambers are key for safety in high flight, and finding their faults remains hard. Their setup first cleans pressure watch data. An empirical wavelet transform then pulls frequency features from the cleaned signal. A three-layer memory network reads the mixed time and frequency patterns. Finally a fuzzy neural network sorts the fault.
Tests across many pressure watch data sets showed better fault finding results than rival methods. Each stage of the chain has a defined job: clean, split, sequence-model, then decide fuzzily. Split control methods keep the system steady around the fault finding. The 2024 paper speaks to aircraft engineers who need earlier, more reliable warnings from pressure systems that cannot be allowed to fail in silence.
Catalog record
| CREATOR | Nan Zhang, Yong Ma |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 34 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |