Deep learning recognition of bolt looseness and axial force compensation of shape memory alloy

Abstract

Loosening of bolts, which is a common form of failure in bolted connections, causes relative slippage between the connected surfaces. The bolts fail under the action of external shear forces due to fatigue and breakage, thereby affecting the service performance and connection strength of the equipment, potentially resulting in major accidents. At present, condition monitoring, which is used to detect the tightness of bolt connections, has obtained acceptable results; however, most of them are still carried out under laboratory conditions and cannot be applied to engineering. In addition, effective remedial measures should be implemented after detecting bolt looseness. On the basis of such problems, a multi-bolt looseness monitoring method based on machine vision and deep learning is proposed. At the same time, shape memory alloy is used in the design of a structure that actively compensates for loose bolts. This method realises bolt recognition of the bolt connection structure through video monitoring and looseness monitoring of multi-target bolts at the same time. When the system detects that the bolts are loosened, an alarm signal is issued and, at the same time, the control device is activated to compensate, to increase the time available for repair time and to ensure the service performance of major equipment.