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

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Huihui Li ◽  
Linfeng Gou ◽  
Hua Zheng ◽  
Huacong Li
Keyword(s):  
Fault Diagnosis ◽  
Particle Swarm Optimization Algorithm ◽  
Spectral Entropy ◽  
Intelligent Fault Diagnosis ◽  
Swarm Optimization ◽  
One Dimensional ◽  
Time Performance ◽  
Rapid Calculation ◽  
Efficiency And Reliability ◽  
Working Efficiency

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 λ ), 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.

scholarly journals An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiaofeng Lv ◽  
Deyun Zhou ◽  
Ling Ma ◽  
Yuyuan Zhang ◽  
Yongchuan Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Upper And Lower Bounds ◽  
Swarm Optimization ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis

The fault rate in equipment increases significantly along with the service life of the equipment, especially for multiple fault. Typically, the Bayesian theory is used to construct the model of faults, and intelligent algorithm is used to solve the model. Lagrangian relaxation algorithm can be adopted to solve multiple fault diagnosis models. But the mathematical derivation process may be complex, while the updating method for Lagrangian multiplier is limited and it may fall into a local optimal solution. The particle swarm optimization (PSO) algorithm is a global search algorithm. In this paper, an improved Lagrange-particle swarm optimization algorithm is proposed. The updating of the Lagrangian multipliers is with the PSO algorithm for global searching. The difference between the upper and lower bounds is proposed to construct the fitness function of PSO. The multiple fault diagnosis model can be solved by the improved Lagrange-particle swarm optimization algorithm. Experiment on a case study of sensor data-based multiple fault diagnosis verifies the effectiveness and robustness of the proposed method.

The Research of Hybrid Genetic – Particle Swarm Optimization Algorithm Based Reconfiguration of the Electric Distribution Network

2014 ◽  
Vol 989-994 ◽  
pp. 1204-1207
Author(s):  
Xin Nan Zhou ◽  
De Ping Ke ◽  
Yuan Zhang Sun ◽  
Lu Yang Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Distribution Network ◽  
Particle Swarm ◽  
Recovery Strategy ◽  
Swarm Optimization ◽  
Electric Distribution Network ◽  
Electric Distribution

The fault-diagnosis and recovery strategy of the electric distribution network were discussed. The procedure of the hybrid genetic – particle swarm optimization algorithm, together with a practical example, was also introduced.

scholarly journals A Novel Fault Diagnosis Algorithm for Rolling Bearings Based on One-Dimensional Convolutional Neural Network and INPSO-SVM

2020 ◽  
Vol 10 (12) ◽  
pp. 4303
Author(s):  
Yang Shao ◽  
Xianfeng Yuan ◽  
Chengjin Zhang ◽  
Yong Song ◽  
Qingyang Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Experimental Results ◽  
Learning Ability ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Hybrid Framework

Deep learning based intelligent fault diagnosis methods have become a research hotspot in the fields of fault diagnosis and the health management of rolling bearings in recent years. To effectively identify incipient faults in rotating machinery, this paper proposes a novel hybrid intelligent fault diagnosis framework based on a convolutional neural network and support vector machine (SVM). First, an improved one-dimensional convolutional neural network (1DCNN) was adopted to extract fault features, and the state information and intrinsic properties of the raw vibration signals were mined. Second, the extracted features were used to train the SVM, which was applied to classify the fault category. The proposed hybrid framework combined the excellent classification performance of the SVM for small samples and the strong feature-learning ability of CNN network. In order to tune the parameters of the SVM, an improved novel particle swarm optimization algorithm (INPSO) which combined the Tent map and Lévy flight strategy was proposed. Numerical experimental results indicated that the proposed PSO variant had a better performance in searching accuracy and convergence speed. At last, multiple groups of rolling bearing fault diagnosis experiments were carried out and experimental results showed that, with the proposed 1DCNN-INPSO-SVM model, the hybrid framework was capable of diagnosing with high precision for rolling bearings and superior to some traditional fault diagnosis methods.

Simulation for Automotive Engine Fault Diagnosis Method

2014 ◽  
Vol 687-691 ◽  
pp. 882-885
Author(s):  
Huan Xue Liu ◽  
Guang Dong Zhang ◽  
Zhen Zhong Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Accurate Data ◽  
Swarm Optimization ◽  
Automotive Engine ◽  
Diagnosis Method

For engine fault diagnosis problem, an engine fault diagnosis method based on particle swarm optimization algorithm is proposed. The velocity and spatial position of all the particles in the particle swarm are updated, in order to provide accurate data basis for the engine fault diagnosis. Particle swarm optimization method is utilized to process iteration for all particles, so as to determine whether failure exists in components of engine. Experimental results show that with the proposed algorithm to diagnose engine fault can effectively improve the accuracy of fault diagnosis, and achieved the desired results.

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