On-Load Tap-Changer Mechanical Fault Diagnosis Method Based on CEEMDAN Sample Entropy and Improved Ensemble Probabilistic Neural Network

The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

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Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine

Applied Sciences ◽

10.3390/app9194122 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4122 ◽

Cited By ~ 2

Author(s):

Bo Wang ◽

Hongwei Ke ◽

Xiaodong Ma ◽

Bing Yu

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Control System ◽

Fault Diagnosis ◽

Probabilistic Neural Network ◽

Support Vector ◽

Engine Control ◽

Piston Engine ◽

Engine Control System ◽

Diagnosis Method

Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.

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Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Energies ◽

10.3390/en12244701 ◽

2019 ◽

Vol 12 (24) ◽

pp. 4701 ◽

Cited By ~ 1

Author(s):

Yunpeng Cao ◽

Xinran Lv ◽

Guodong Han ◽

Junqi Luan ◽

Shuying Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Gas Turbine ◽

Probabilistic Neural Network ◽

Second Law Of Thermodynamics ◽

Fault Detection And Isolation ◽

Exergy Loss ◽

Diagnosis Method ◽

Gas Path Fault ◽

Faulty Component

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.

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