Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Abstract

The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

Intelligent fault diagnosis of hydroelectric units based on radar maps and improved GoogleNet by depthwise separate convolution

“…For method A to D, the input is radar maps, the training epoch is set to 100, the initial value of the learning rate is 0.005, and the batch_size = 32. [44] Radar maps VGG16 Method C [45] Radar maps AlexNet Method D [46] Radar maps LeNet5 Method E [47] The Hu-invariant matrix of the radar maps BPNN Method F The Hu-invariant matrix of the radar maps SVM 0.001, and a target error of 10-5. Method F uses the Hu invariant moment-SVM (Hu-SVM) diagnostic algorithm with radial basis function kernel function and the same inputs as method E. The parameters of SVM, including the penalty coefficient C and kernel function parameter g, are optimized using a genetic algorithm with 50 generations.…”