An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump

Hydraulic axial piston pump is broadly-used in aerospace, ocean engineering and construction machinery since it is the vital component of fluid power systems. In the light of the undiscoverability of its fault and the potential serious losses, it is valuable and challenging to complete the fault identification of a hydraulic pump accurately and effectively. Owing to the limitations of shallow machine learning methods in the intelligent fault diagnosis, more attention has been paid to deep learning methods. Hyperparameter plays an important role in a deep learning model. Although some manual tuning methods may represent good results in some cases, it is hard to reproduce due to the differences of datasets and other factors. Hence, Bayesian optimization (BO) algorithm is adopted to automatically select the hyperparameters. Firstly, the time–frequency images of vibration signals by continuous wavelet transform are taken as input data. Secondly, by setting some hyperparameters, a preliminary convolutional neural network (CNN) model is established. Thirdly, by identifying the range of each hyperparameter, BO based on Gaussian process is employed to construct an adaptive CNN model named CNN-BO. The performance of CNN-BO is verified by comparing with traditional LeNet 5 and improved LeNet 5 with manual optimization. The results indicate that CNN-BO can accomplish the intelligent fault diagnosis of a hydraulic pump accurately. 

Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization.

Intelligent fault diagnosis of hydraulic piston pump combining improved LeNet-5 and PSO hyperparameter optimization

Integrated slipper retainer mechanism to eliminate slipper wear in high-speed axial piston pumps

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

Hydraulic piston pump in civil aircraft: Current status, future directions and critical technologies

Abstract:In order to solve the problem of dynamic gunnery problem solution under fire maneuver condition, this paper proposes a method of hitting the moving targets with rapid fire in the maneuver weapon. Based on the exterior ballistics model, objective model, the dynamic gunnery problem solution model is established. The improved particle swarm optimization (PSO) is adopted to calculate the ballistic data. In the process of realizing the algorithm, the angle of firing, azimuth and flight time of projectile are calculated iteratively synchronously, so as to improve the efficiency of the solution by eliminating the initial time assignment step. Considering the sustained rapid fire of maneuver weapon, the adjusting parameters of fire control elements for dynamic shooting are provided by solving the first and last firing ballistic data. The feasibility of the algorithm is verified by simulation experiment.

https://iopscience.iop.org/article/10.1088/1742-6596/2460/1/012005/pdf

Yueliang Lu

Papers

Hydraulic piston pump in civil aircraft: Current status, future directions and critical technologies

Research on Dynamic Gunnery Problem Solution Based on Exterior Ballistic