Name
Title | ||
|---|---|---|
Custom Machine Learning Architectures: Towards Realtime Anomaly Detection For Flight Testing |
Abstract | ||
|---|---|---|
Test flight of a new commercial aeroplane is crucial in validating the functionality, safety and performance of the new aeroplane design before its batch manufacturing can take place. Massive amounts of data streams are typically generated from thousands of sensors on an aeroplane during test flight, which require realtime processing to detect anomaly and to predict malfunctions for emergency response. This paper provides an overview of recent research in custom machine learning architectures which have shown promise for highspeed data processing, and proposes a time series learning model based on LSTM (Long Short Term Memory). This LSTM model is adopted for realtime data analysis used in anomaly detection for the COMAC C919 test flight. A custom architecture targeting FPGA (Field Programmable Gate Array) implementation for the proposed approach can be embedded into realtime data analysis and processing platforms for large commercial aircraft. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IPDPSW.2018.00207 | 2018 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW 2018) |
Keywords | Field | DocType |
custom machine learning architecture, anomaly detection, LSTM, FPGA | Anomaly detection,Data modeling,Architecture,Data stream mining,Data processing,Computer science,Long short term memory,Field-programmable gate array,Feature extraction,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
2164-7062 | 0 | 0.34 |
References | Authors | |
0 | 18 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Wu | 1 | 1 | 0.68 |
| Zhanrui Sun | 2 | 5 | 1.55 |
| Yongxin Zhu | 3 | 13 | 5.27 |
| Tian, L. | 4 | 12 | 8.63 |
| Hanlin Zhu | 5 | 3 | 2.08 |
| Peng Xiong | 6 | 5 | 1.55 |
| Zihao Cao | 7 | 0 | 0.34 |
| Menglin Wang | 8 | 0 | 0.34 |
| Yu Zheng | 9 | 8 | 4.90 |
| Xiong Chao | 10 | 29 | 6.66 |
| hao jiang | 11 | 59 | 17.96 |
| Kuen Hung Tsoi | 12 | 0 | 1.69 |
| Xinyu Niu | 13 | 135 | 23.16 |
| Wei Mao | 14 | 20 | 7.04 |
| Can Feng | 15 | 0 | 0.34 |
| Xiaowen Zha | 16 | 0 | 0.34 |
| Guobao Deng | 17 | 0 | 0.34 |
| Wayne Luk | 18 | 15 | 10.38 |