Method of hydraulic control detection and fault diagnosis of construction machinery based on machine learning

Abstract

Abstract With the continuous development of the times, the hydraulic system of construction machinery has become one of the important components of the hydraulic system and has been widely used in the construction machinery industry. If the hydraulic pressure of the construction machinery detects a fault, the entire production line will stop working, resulting in heavy losses to the enterprise. The hydraulic system of construction machinery is expensive in terms of its own cost and maintenance cost. Therefore, the control detection and fault diagnosis of hydraulic system of construction machinery has the highest value in engineering applications. In this paper, the neural network model used is based on the classification characteristics of neural network in machine learning. Through extracting and standardizing the sample data processing function, the hydraulic control detection and fault diagnosis of construction machinery have been successfully realized. For the defects of parameter selection in machine learning, such as the difficulty of selecting parameter attributes, and the difficulty of falling into local optimization in the face of complex structure, the diagnosis method based on multi-layer hidden MLP model is adopted. This method can directly extract the feature quantity and input it into the MLP model for proper training, so as to make the learning network more in-depth, so as to ensure the reliability of production and improve the accuracy of model fault diagnosis, and use the neural network diagnosis method to process multi-dimensional data in machine learning.