An assembly tightness recognition method for bolted connection states with singular-value entropy and GA least-squares support vector machine

The bolted connections are widely used in high-end equipment such as aerospace and energy fields. However, when the equipment is assembled, the assembly tightness of bolted connection structure is easily affected by friction and manual experience, resulting in inconsistent preload. It will affect the dynamic performance of equipment, resulting in abnormal vibration or failure of equipment. Currently, the existing assembly tightness testing methods mainly focus on whether the bolts are loose, ignoring the impact of assembly tightness on the vibration of the whole machine. This paper proposes an assembly tightness recognition method for bolted connection states based on modified ensemble empirical mode decomposition (MEEMD) and genetic algorithm least squares support vector machine (GALSSVM). Firstly, an ultrasonic pre-tightening force test system is used for multi-bolt pre-tightening force calibration and precise control, and subsequently, accurate vibration signal samples are collected. The feature extraction of multi-bolt connection states is carried out based on MEEMD. Secondly, a quantitative evaluation index of assembly tightness, singular-value entropy, is proposed. Finally, experimental verification is performed. Multiple sets of different preloads case experiments are designed to verify the identification accuracy of the proposed algorithm. The results show that with the bolted connection structure from loose to tight, the singular-value entropy of the quantitative index decreases monotonically. It provides some theoretical references for improving the reliability of high-end equipment and the evaluation of assembly performance.