3results about How to "Comprehensive diagnosis" patented technology

This invention discloses a real-time evaluation method for photovoltaic panel defects and performance based on multispectral imaging, belonging to the field of computer processing technology. The method includes the following steps: S01, emitting multispectral light including visible light, near-infrared, and thermal infrared bands to the photovoltaic panel, and simultaneously receiving reflection and radiation signals based on the multispectral light to obtain a multispectral image dataset; wherein, the dataset includes visible light images, photoluminescence images in the near-infrared band, and thermal infrared images; S02, performing registration and fusion processing on the multispectral image dataset to generate a registered multispectral fused image. This invention, through multispectral information fusion and intelligent analysis, achieves simultaneous diagnosis of internal and external defects of photovoltaic panels and quantitative evaluation of power generation performance, significantly improving detection efficiency and maintenance accuracy.

This invention discloses a method and system for the operation and maintenance of a shielded pump in a nuclear reactor experimental setup. By monitoring the stress of devices related to the shielded pump within the experimental setup and utilizing a stress assessment model for the shielded pump, the invention enables a clear, comprehensive, and real-time diagnosis of the pump's operating status. This allows for more rational intervention in preventative partial and full disassembly inspections of the shielded pump, enhancing the targeted nature of maintenance interventions, saving manpower and time costs, and increasing the safety and economy of shielded pump operation and maintenance.

A bearing fault diagnosis method based on multi-sensor, multi-scale feature fusion is proposed. The method involves collecting current and vibration signals to create a dataset, which is then augmented and divided into training, validation, and test sets. A multi-scale feature fusion network model is constructed. The signal data in the training set is transformed using Fast Fourier Transform (FFT) to extract features. An attention mechanism is then introduced to adjust the weights. The fused features are processed alternately using self-calibrating convolutions and dilated convolutions, followed by pooling, feature flattening, and fully connected layers. Finally, a classifier outputs the fault classification result. The validation set is input into the model to fine-tune its parameters. Finally, the test set is used for testing, and the results are output. By using multi-source data such as vibration and current signals as input, and leveraging the ability of neural networks to discover hidden patterns in training samples, this method aims to extract multi-scale features from multi-source data and fuse these features to complete the intelligent fault diagnosis process for bearings.