Application of BP Neural Network in Gearbox Fault Diagnosis

BP neural network demonstrates unique advantages in gearbox fault diagnosis due to its powerful nonlinear mapping capabilities. The network employs backpropagation algorithms to adjust weights, enabling it to learn complex relationships between fault characteristics (extracted from monitoring data like vibration signals and temperature readings) and equipment conditions through iterative training cycles.

In coal mining gearbox applications, the network training process utilizes historical fault data as sample sets. The input layer receives feature parameters (such as spectral amplitude and harmonic components), while the output layer generates probability distributions corresponding to different fault types. The hidden layers continuously adjust parameters through gradient descent optimization, ultimately establishing a predictive model between fault symptoms and degradation levels.

Field verification shows this method can provide early warnings for typical faults like gear tooth fractures and bearing wear, enabling a transition from scheduled maintenance to condition-based maintenance. Compared to traditional threshold alarms, the neural network model demonstrates higher sensitivity to early-stage latent faults while reducing false alarm rates, providing quantitative foundations for preventive maintenance planning through probabilistic fault classification.