Quay crane fault monitoring method based on multi-signal fusion and Adam optimization algorithm

Abstract

The invention provides a signal processing method, and common signals of the quay crane are integrated so as to obtain more fusion characteristic parameters, so that information such as vibration, temperature and stress is more effectively utilized, and more accurate fault diagnosis and monitoring are carried out on the state of the quay crane. And a neural network is optimized by using an Adam algorithm, so convenience is provided for monitoring the quay crane state more accurately and quickly. The method comprises the steps: 1, collecting data; 2, preprocessing data, and determining input and output; 3, constructing a neural network model; 4, enabling the training set to train a neural network by using an Adam algorithm to obtain a quay crane state prediction model; 5, checking the quaycrane state prediction model by using the test set; 6, outputting a prediction result, and presenting the prediction result in the human-computer interaction interface. According to the invention, a plurality of signals are adopted for fusion prediction, the fault tolerance of a certain signal is increased, and the accuracy of quay crane operation state monitoring is improved.

Description

technical field [0001] The invention relates to a quay crane fault monitoring method based on multi-signal fusion and Adam optimization algorithm, which belongs to the quay crane state prediction technology. Background technique [0002] Fault diagnosis of quay cranes has always been a technical difficulty, and the stability of quay cranes is of great significance to the safe operation of port transportation and trade. With the development of big data theory, excavating a large amount of information generated during the monitoring process of quayside cranes, and using artificial intelligence technology for signal fusion, processing and diagnosis has become a new direction for quayside crane status prediction and fault diagnosis. Existing technologies mainly focus on independent monitoring of vibration and stress signals, lacking fusion diagnosis of signals such as temperature, vibration, and stress. [0003] The BP neural network has a strong nonlinear fitting ability. The ...

AI Technical Summary

Problems solved by technology

[0003] The BP neural network has a strong nonlinear fitting ability. The traditional BP neural network has a slow convergence speed, difficult selection of hyperparameters, and cannot perform real-time monitoring and diagnosis well. At the same time, when directly using all monitoring quantities for modeling, Too much monitoring will make the model too complex, making the model too computationally intensive, making it difficult to train and prone to overfitting

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