Abstract
The initial design of baggage-lifting machine structures is primarily based on safety and reliability, but they are often damaged because of unforeseen circumstances and overloads. In this study, a machine learning–based logistic regression method for detecting structural damage to bolted truss structures during field work is proposed. Multiple strain gauges attached to the front of the truss model record the amount of deformation occurring in the member when the vertical load generated at the end of the model is applied. In this process, the scatter or error caused by the sample is analyzed, and the data processing method is presented. Experimental results demonstrate that this method provides a good quantitative basis for fault detection, and it can be effectively applied to partial representative data when handling large datasets.
Highlights
Fault analysis and reliability analysis are essential to ensure the long-term usability of devices, and an analysis of environmental factors, including structural analysis and material selection, may be helpful
Among them[1,2] the static identification method requires only structural stiffness, and several studies have been published using strain gauges as a method for obtaining data in this process; artificial damage was made to the joints or bulk material connected to the bridge or reduced by lab-scale model, after which strain gauges and strain transducers were attached to measure strain during loading
For a truss model assembled with a constant torque using bolts, the variation in the strain due to the inclusion or exclusion of cracks in the section steel under repeated loads is confirmed
Summary
Fault analysis and reliability analysis are essential to ensure the long-term usability of devices, and an analysis of environmental factors, including structural analysis and material selection, may be helpful. A decision boundary layer between two selected data on logistic regression was generated, and the error rate can be calculated by using this layer as a reference and how to distinguish normal-fault data accurately.
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