Pattern Recognition Algorithm Using Genetic Neural Networks for Equipment Fault Diagnosis
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This document presents a pattern recognition algorithm utilizing genetic neural networks for equipment fault diagnosis. The algorithm analyzes equipment operating states and characteristic patterns to detect and identify potential fault conditions. By implementing genetic neural networks, we enhance diagnostic accuracy and efficiency through evolutionary optimization of neural network parameters and architecture. Key implementation aspects include chromosome encoding of network weights, fitness evaluation based on classification performance, and genetic operations (selection, crossover, mutation) for population evolution. This approach ensures optimal network configuration adaptation, improving equipment maintenance scheduling and operational reliability. The genetic neural network pattern recognition algorithm therefore holds significant application value for preventive maintenance and fault prediction in industrial equipment systems.
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