Efficient characterization of dynamic response variation using multi-fidelity data fusion through composite neural network

A robotic system for combined thermal nondestructive testing of large-size parts, including data fusion, is described. The efficiency of combining results of infrared (IR) and ultrasonic IR thermographic inspection has been demonstrated on a complex-shape reference sample containing 18 surrogates of manufacture and in-service defects. The data fusion algorithms including IR image stitching in space and automated defect detection and characterization by using a neural network have demonstrated efficiency of the proposed approach in practical testing.

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Rock hardness identification based on optimized PNN and multi-source data fusion

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211042048 ◽

2021 ◽

pp. 095440622110420

Author(s):

Ying He ◽

Muqin Tian ◽

Jiancheng Song ◽

Junling Feng

Keyword(s):

Neural Network ◽

Data Fusion ◽

Probabilistic Neural Network ◽

Wavelet Packet ◽

Differential Evolution Algorithm ◽

Vibration Signal ◽

Optimization Method ◽

Rock Wall ◽

Rock Hardness ◽

Source Data

To solve the problem that it is difficult to identify the cutting rock wall hardness of the roadheader in coal mine, a recognition method of cutting rock wall hardness is proposed based on multi-source data fusion and optimized probabilistic neural network. In this method, all kinds of cutting signals (the vibration signal of cutting arm, the pressure signal of hydraulic cylinders and current signal of cutting motor) are analyzed by wavelet packet to extract the feature vector, and the multi feature signal sample database of rock cutting with different hardness is established. To solve the problems of uncertain spread and complex network structure of probabilistic neural network (PNN), a PNN optimization method based on differential evolution algorithm (DE) and QR decomposition was proposed, and the rock hardness was identified based on multi-source data fusion by optimizing PNN. Then, based on the ground test monitoring data of a heavy longitudinal roadheader, the method is applied to recognize the cutting rock hardness, and compared with other common pattern recognition methods. The experimental results show that the cutting rock hardness recognition based on multi-source data fusion and optimized PNN has higher recognition accuracy, and the overall recognition error is reduced to 6.8%. The recognition of random cutting rock hardness is highly close to the actual. The method provides theoretical basis and technical premise for realizing automatic and intelligent cutting of heading face.

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Fault Diagnosis of New Certain Mine Sweeping Plough’s Electrical Control System Based on Data Fusion

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.539 ◽

2015 ◽

Vol 713-715 ◽

pp. 539-543

Author(s):

Yong Zhao ◽

Xiao Qiang Yang ◽

Yin Hua Xu ◽

Jian Bin Li

Keyword(s):

Neural Network ◽

Control System ◽

Fault Diagnosis ◽

Data Fusion ◽

Complex Structure ◽

Evidence Theory ◽

System Level ◽

Sensor Failure ◽

Advanced Technique ◽

Electrical Control

The fault diagnosis of electrical control system of certain type mine sweeping vehicle is difficult due to its complex structure and advanced technique. So in the multi-sensor failure diagnosis process, as a result of various reasons, such as the existence of measurement noise, diagnosis knowledge incomplete and so on, it makes the fault diagnosis uncertainty and affects the reliability and the accuracy of the diagnosis result. This article according to the analysis of electrical control system's fault characteristic of the mine sweeping plough’s, proposes a technique based on data fusion fault diagnosis method. The diagnosis process is divided into the sub system and the system-level, the subsystem uses the BP neural network to classify the fault mode, the system-level uses the D-S evidence theory carries on the comprehensive decision judgment for the whole system's fault. Application shows if some sub-neural network diagnosis has error, using D-S evidence theory fusion can effectively improve the accuracy of diagnosis.

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