This article presents a novel second-order output spectrum–based method for detecting and localizing multiple bolt loosening faults in complex structures with a sensor chain. This new method is dev...

https://journals.sagepub.com/doi/pdf/10.1177/1475921719836379

Fault diagnosis of bolt loosening in structures with a novel second-order output spectrum–based method:

By considering the tightening process, a three-dimensional elastic finite element analysis is conducted to explore the mechanism of bolt self-loosening under transverse cyclic loading. According to the geometrical features of the thread, a hexahedral meshing is implemented by modifying the node coordinates based on cylinder meshes and an ABAQUS plug-in is made for parametric modeling. The accuracy of the finite element model is verified and validated by comparison with the analytical and experimental results on torque-tension relationship. And, then, the fastening states acquired by different means are compared. The results show that the tightening process cannot be replaced by a simplified method because its fastening state is different from the real process. With combining the tightening and self-loosening processes, this paper utilizes the relative rotation angles and velocities to investigate the slip states on contact surfaces instead of the Coulomb friction coefficient method, which is used in most previous researches. By contrast, this method can describe the slip states in greater detail. In addition, the simulation result reveals that there exists a creep slip phenomenon at contact surface, which causes the bolt self-loosening to occur even when some contact facets are stuck.

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Self-Loosening Failure Analysis of Bolt Joints under Vibration considering the Tightening Process

Axial load distribution and self-loosening behavior of bolted joints subjected to torsional excitation

Bolted connections are widely used in timber structures. Bolt looseness is one of the most important factors leading to structural failure. At present, most of the detection methods for bolt looseness do not achieve a good balance between cost and accuracy. In this paper, the detection method of small angle of bolt loosening in a timber structure is studied using deep learning and machine vision technology. Firstly, three schemes are designed, and the recognition targets are the nut’s own specification number, rectangular mark, and circular mark, respectively. The Single Shot MultiBox Detector (SSD) algorithm is adopted to train the image datasets. The scheme with the smallest identification angle error is the one identifying round objects, of which the identification angle error is 0.38°. Then, the identification accuracy was further improved, and the minimum recognition angle reached 1°. Finally, the looseness in a four-bolted connection and an eight-bolted connection are tested, confirming the feasibility of this method when applied on multi-bolted connection, and realizing a low operating costing and high accuracy.

Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning

Design and nonlinear structural responses of multi-bolted joint composite box-beam for sectional wind turbine blades

A UNIQUE TEST method and apparatus now make it possible to reproduce conditions of vibration that are not only certain to loosen bolted joints but which also closely simulate actual conditions. It can be shown that properly preloaded fasteners loosen as a result of rotation as soon as relative motion occurs between the mating threads and between the bearing surfaces of the fastener and the clamped material.

Gerhard H. Junker

Papers

Criteria for self loosening of fasteners under vibration