Aeroengine Control System Sensor Fault Diagnosis Based on CWT and CNN

Abstract

The aeroengine control system is a piece of complex thermal machinery which works under high-speed, high-load, and high-temperature environmental conditions over lengthy periods of time; it must be designed for the utmost reliability and safety to function effectively. The consequences of sensor faults are often extremely serious. The inherent complexity of the engine structure creates difficulty in establishing accurate mathematical models for the model-based sensor fault diagnosis. This paper proposes an intelligent fault diagnosis method for aeroengine sensors combining a deep learning algorithm with time-frequency analysis wherein the signal recognition problem is transformed into an image recognition problem. The continuous wavelet transform (CWT) is first applied to seven common health condition signals in an engine control system sensor in order to generate scalograms that capture the characteristics of the signal. A convolutional neural network (CNN) model trained with preprocessed and labeled datasets is then used to extract the features of a time-frequency graph based on which faults can be identified and isolated. This method does not require modeling and design thresholds, so it has strong robustness and accuracy rate of over 97%. The trained model effectively reveals faults in sensor signals and allows for accurate identification of fault types.

Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy

“…We first establish a simulation model of the sensor. The second-order inertial link [6] was adopted to establish the sensor simulation model by referring to previous studies. Its transfer function is as follows:…”

“…Traditional model-based fault diagnosis schemes have inherent limitations such as large interference, low model accuracy, difficulty in obtaining fault information, and ineffective threshold designs [6]. It is yet necessary to secure newer, effective fault feature extraction and fault diagnosis methods.…”

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

“…When the wavelet is transformed by FT, the smallest frequency is the frequency of the wavelet [31]. This smallest frequency is called the center frequency, which best represents the frequency and low-wave energy of the middle part of the wavelet [31]. If the central frequency of the parent wavelet function is c F , the signal frequency equivalent to the subwavelet is shown in 2:…”

A New Hydrogen Sensor Fault Diagnosis Method Based on Transfer Learning With LeNet-5

“…As a branch of ML, deep learning (DL) has powerful functionality and flexibility. DL does not need to rely on expert experience and manual feature extraction (Zhang W. et al, 2018), so many scholars have gradually introduced DL methods, such as the deep belief network (Shao et al, 2018;Wang et al, 2020), sparse autoencoders, and convolution neural networks (CNNs) (Wen et al, 2018;Wu and Zhao, 2018;Gou et al, 2020;Sun et al, 2020) into fault diagnosis processes. These methods can improve the accuracy of fault diagnosis, but there are some limitations.…”