Study on Aero-engine Wear Fault Diagnosis by Direct Reading Ferrograph

2011 ◽  
Author(s):  
Jiang Xufeng ◽  
Wang Jianbo ◽  
Guo Changying ◽  
Zhang Yuan
Keyword(s):  
Fault Diagnosis ◽  
Direct Reading ◽  
Aero Engine ◽  
Engine Wear

scholarly journals Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianwei Cui ◽  
Mengxiao Shan ◽  
Ruqiang Yan ◽  
Yahui Wu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Energy Difference ◽  
Test System ◽  
Support Vector ◽  
Svm Classifier ◽  
Aero Engine ◽  
Aero Engines ◽  
Optimal Set

This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

Joint Wavelet Transform and Time Synchronous Averaging Source Separation Method and its Application on Aero-Engine

2020 ◽  
Author(s):  
Jiqing Cong ◽  
Jianping Jing ◽  
Changmin Chen ◽  
Zezeng Dai ◽  
Jianhua Cheng
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Averaging Method ◽  
Gaussian White Noise ◽  
Rotating Machinery ◽  
Vibration Source ◽  
Vibration Signals ◽  
Aero Engine ◽  
Harmonic Components

Abstract The reliability and safety of aero-engine are often the decisive factors for the safe and reliable flight of commercial aircraft. Hence, the vibration source location and fault diagnosis of aero-engine are of prime importance to detect faults and carry out fast and effective maintenance in time. However, the vibration signals collected by the sensors arranged on the casing of the aero-engine are generally the mixed signals of the main vibration sources inside the engine, and the components are extremely complicated. Therefore, the vibration source identification is a big challenge for a fault diagnosis and health management of the engine. In order to separate the key vibration sources of rotating machinery such as aero-engine, a Joint Wavelet Transform and Time Synchronous Averaging based algorithm (JWTS) is proposed in this paper. Based on the fact that the fundamental frequency and its harmonic and sub-harmonic components are generally included in the vibration spectrum of shaft fault signal of rotating machinery, wavelet transform and time synchronous averaging algorithm are combined to extract them. The algorithm completes separating the main vibration sources with three steps. First, the source number and fundamental frequency of each source are estimated using the wavelet transform. Second, every source is extracted from each observed signal by the time synchronous averaging method. Time synchronous averaging method can effectively extract a signal of cycle and harmonic rotor components and can suppress noise. Third, the optimal estimation of each source is determined according to signal’s 2-norm. Since the extracted source with a larger energy is closer to the real source, and signal’s 2-norm is a good indicator of the signal energy. Hence, the key vibration sources related to rotary speeds of the engine are obtained separately. The method is verified by synthetic mixed signals first. Three periodic signals of different frequencies are used to simulate the vibration sources of the aeroengine. The fundamental, harmonic and sub-harmonic components of them, as well as Gaussian white noise, are randomly mixed. The results show that the JWTS algorithm can estimate the number of the main sources and can extract each source effectively. Then the method is demonstrated using vibration signals of a real aero-engine. The results indicate that the proposed JWTS method has extracted and located the main sources within the aero-engine, including sources from the low-pressure rotor, high-pressure rotor, combustion chamber and accessory. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery, especially for a real aero-engine.

Aero-engine Gas-path Fault Diagnosis by CNN Based on QAR Data

2020 ◽  
Author(s):  
Hou Wenkui ◽  
Xu Yujie ◽  
Yang Kun ◽  
Ye Yong ◽  
Li Pengyu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Aero Engine ◽  
Gas Path Fault

Sensor fault diagnosis of aero-engine based on divided flight status

2017 ◽  
Vol 88 (11) ◽  
pp. 115007 ◽  
Author(s):  
Zhen Zhao ◽  
Jun Zhang ◽  
Yigang Sun ◽  
Zhexu Liu
Keyword(s):  
Fault Diagnosis ◽  
Sensor Fault ◽  
Aero Engine
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