Development and Application of Aero-engine Experimental Data Mining Algorithm Library

This paper presents the application of several anomaly detection algorithms in experiment data from engine test bed. Several anomaly detection algorithms are programmed in Python language and integrated into an algorithm library named PyPEFD (Python Package for Engine Fault Detection). The algorithm...

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Vydáno v:	2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) s. 264 - 269
Hlavní autoři:	Yan, Zichen, Zhang, Haonan, Sun, Jianzhong, Yang, Yi, Xia, Guangwei
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 15.10.2020
Témata:	aero-engine test data Anomaly detection Clustering algorithms data analysis Engines Fault detection Gas detectors Libraries python algorithm package Sensors
On-line přístup:	Získat plný text
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Popis
Shrnutí:	This paper presents the application of several anomaly detection algorithms in experiment data from engine test bed. Several anomaly detection algorithms are programmed in Python language and integrated into an algorithm library named PyPEFD (Python Package for Engine Fault Detection). The algorithm library includes Gaussian Mixture Model, Feature Weighted Fuzzy Compactness and Separation (WFCS), Sequential Probability Ratio Test (SPRT), Variational Autoencoder, Dynamic Time Warping, Mahalanobis Distance, Singular Value Thresholding, Random Forest and Multivariate State Estimation Technique. These algorithms can analyze the structure and characteristics of the engine test data, and mine the hidden fault information in the data, so as to detect the fault or fault trend of aero-engine test data. This paper also presents a preview of the algorithm library.
DOI:	10.1109/ICSMD50554.2020.9261719