Real-time environmental analysis for industrial vehicles based on synthetic sensor data and deep learning

In this paper, we proposed a new faults diagnosis method based on the fusion of visible and infrared images of electrical equipment. Firstly, the discrete wavelet transform method is used to fuse the visible and infrared images, which enables the accurate location of the electrical equipment. Then, a deep convolution neural network (CNN) model is employed to identify faults in electrical equipment. Three parameters of CNN including connection weight, convolution layer parameters and pooling layer strategy are designed in this paper. The inputs of CNN are the fused reconstructed images and the outputs are the classifications of faults. Finally, simulation experiments and analysis show that the algorithm proposed in this paper can effectively improve the contrast and clarity of the fused images. It can reduce noise interference, and improve the location accuracy of electrical equipment. More specifically, the faults diagnosis rate is improved by 2-6% with the proposed method.

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Real-time environmental analysis for industrial vehicles based on synthetic sensor data and deep learning

Cited by 3 publications

References 12 publications

Cost-effective AIoT-based hazard detection using 2D camera in manufacturing environments

Cost-effective AIoT-based hazard detection using 2D camera in manufacturing environments

Recognition of car parts in automotive supply chains by combining synthetically generated training data with classical and deep learning based image processing

Infrared-Visual Image Fusion and CNN Model in Electrical Faults Diagnosis

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