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

Timely and effective fault diagnosis of sensors is crucial to enhance the working efficiency and reliability of the aeroengine. A new intelligent fault diagnosis scheme combining improved pattern gradient spectrum entropy (IPGSE) and convolutional neural network (CNN) is proposed in this paper, aiming at the problem of poor fault diagnosis effect and real-time performance when CNN directly processes one-dimensional time series signals of aeroengine. Firstly, raw fault signals are converted into spectral entropy images by introducing pattern gradient spectral entropy (PGSE), which is used as the input of CNN, because of the great advantage of CNN in processing images and the simple and rapid calculation of the modal gradient spectral entropy. The simulation results prove that IPGSE has more stable distinguishing characteristics. Then, we improved PGSE to use particle swarm optimization algorithm to adaptively optimize the influencing parameters (scale factor ), so that the obtained spectral entropy graph can better match the CNN. Finally, CNN mode is proposed to classify the spectral entropy diagram. The method is validated with datasets containing different fault types. The experimental results show that this method can be easily applied to the online automatic fault diagnosis of aeroengine control system sensors.

[1]  Guoan Yang,et al.  A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis , 2015 .

[2]  Wei Liu,et al.  A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology , 2018, Sensors.

[3]  Fulei Chu,et al.  Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples , 2013 .

[4]  Fang Liu,et al.  Characterizing vibration signals utilizing morphological pattern spectrum for gear fault diagnosis , 2015, ICM 2015.

[5]  Liu Peng,et al.  A performance degradation feature extraction method and its application based on mathematical morphological gradient spectrum entropy , 2016 .

[6]  Weidong Qu,et al.  Aero-engine Sensor Fault Diagnosis Based on Convolutional Neural Network , 2018, 2018 37th Chinese Control Conference (CCC).

[7]  Xianyi Zeng,et al.  Multi-Fault Diagnosis of an Aero-Engine Control System Using Joint Sliding Mode Observers , 2020, IEEE Access.

[8]  Jiafu Wan,et al.  Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images , 2020, IEEE Transactions on Industrial Informatics.

[9]  Zhidan Liu,et al.  Aeroengine Robust Gain-Scheduling Control Based on Performance Degradation , 2020, IEEE Access.

[10]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[11]  Li Cao,et al.  LSSVM with Fuzzy Pre-processing Model Based Aero Engine Data Mining Technology , 2007, ADMA.

[12]  Huihui Li,et al.  Aeroengine Control System Sensor Fault Diagnosis Based on CWT and CNN , 2020 .

[13]  Junhao Liu,et al.  A Novel Classification Method for Flutter Signals Based on the CNN and STFT , 2019, International Journal of Aerospace Engineering.

[14]  Xiao Long Zhang,et al.  Faults diagnosis of rolling element bearings based on modified morphological method , 2011 .

[15]  Jun Hong,et al.  A Fault Diagnosis Method of Rolling Bearing Based on Complex Morlet CWT and CNN , 2018, 2018 Prognostics and System Health Management Conference (PHM-Chongqing).

[16]  Ioannis Antoniadis,et al.  APPLICATION OF MORPHOLOGICAL OPERATORS AS ENVELOPE EXTRACTORS FOR IMPULSIVE-TYPE PERIODIC SIGNALS , 2003 .

[17]  Haya Alaskar.  Deep Learning-Based Model Architecture for Time-Frequency Images Analysis , 2018 .

[18]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[19]  Jianbin Xiong,et al.  Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network , 2020, IEEE Access.

[20]  M FrankPaul.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancya survey and some new results , 1990 .

[21]  Bing Wang,et al.  Fault diagnosis using improved pattern spectrum and fruit fly optimization algorithm–support vector machine , 2018, Advances in Mechanical Engineering.

[22]  Huimin Zhao,et al.  An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network , 2020, IEEE Transactions on Instrumentation and Measurement.

[23]  Wu Deng,et al.  Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem , 2020, Appl. Soft Comput..

[24]  Dongyan Li,et al.  Research on vibration suppression method of alternating current motor based on fractional order control strategy , 2017 .

[25]  Anastasios Tefas,et al.  Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits , 2020, Pattern Recognit..

[26]  Feng Lu,et al.  Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle , 2017 .

[27]  Rong Chen,et al.  Fusion of Multi-RSMOTE With Fuzzy Integral to Classify Bug Reports With an Imbalanced Distribution , 2019, IEEE Transactions on Fuzzy Systems.