Method and device for detecting abnormal running state of loading arm

Through the abnormality detection model based on the autoencoder, the operation status of the crane tube is detected in real time, which solves the shortcomings of traditional methods in detecting complex working conditions and noise interference, and achieves high accuracy and reliability abnormal detection, ensuring the safe loading and unloading of hazardous chemicals.

CN119826908APending Publication Date: 2025-04-15CHINA ACAD OF SAFETY SCI & TECH +2
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Patent Information

Application Number
CN202510314973.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-18
Publication Date
2025-04-15

AI Technical Summary

Technical Problem

Traditional crane tube abnormality detection methods are difficult to effectively detect early leakage or small-scale abnormalities, and have poor adaptability to complex working conditions, which can easily lead to missed or false alarms, and noise interference in the operating data reduces the reliability and accuracy of detection.

Method used

An abnormality detection model based on the autoencoder is adopted to collect the operating parameters of the crane tube in real time, and data fusion is performed, and the reconstruction error is calculated using the pre-trained abnormality detection model to determine whether the operating status of the crane tube is abnormal.

Benefits of technology

Effectively process multi-dimensional and high-complex data, realize real-time detection of abnormal operating status of equipment, significantly improve the accuracy and reliability of abnormal operating status of crane pipes, provide an early warning mechanism, and ensure the safe loading and unloading of hazardous chemicals.

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Abstract

The invention discloses a crane tube operation state abnormity detection method and device, and the method comprises the steps: collecting crane tube operation parameters in real time in the crane tube operation process; fusing the operation parameters to obtain fused data; inputting the fusion data into a pre-trained anomaly detection model, and obtaining a reconstruction error according to the output of the anomaly detection model; and determining whether the crane tube operation state is abnormal according to the reconstruction error. By means of the scheme, the running state of the crane pipe can be detected in real time, and the accuracy and reliability of anomaly detection are improved.
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