Metal roof fault diagnosis method based on RBF-SVM

Abstract

Metal roof enclosure system is an important part of steel structure construction. In recent years, it has been widely used in large-scale public or industrial buildings such as stadiums, airport terminals, and convention centers. Affected by bad weather, various types of accidents on metal roofs frequently occurred, causing huge property losses and adverse effects. Because of wide span, long service life and hidden fault of metal roof, the manual inspection of metal roof has low efficiency, poor real-time performance, and it is difficult to find hidden faults. On the basis of summarizing the working principle of metal roof and cause of accidents, this paper classifies the fault types of metal roofs in detail and establishes a metal roof monitoring and fault diagnosis system using distributed multisource heterogeneous sensors and Zigbee wireless sensor networks. Monitoring data from strain gauge, laser ranging sensor, and ultrasonic ranging sensor is utilized comprehensively. By extracting time domain feature, the data trend characteristics and correlation characteristics are analyzed and fused to eliminate erroneous data and find superficial faults such as sensor drift and network interruption. Aiming to the hidden faults including plastic deformation and bolt looseness, an SVM fault diagnosis algorithm based on RBF kernel function is designed and applied to diagnose metal roof faults. The experimental results show that the RBF-SVM algorithm can achieve high classification accuracy.