Adaptive Stochastic Resonance for Bolt Looseness Identification Under Strong Noise Background
Tao Gong, Jianhua Yang, Miguel A. F. Sanjuán, Houguang Liu
Abstract
Abstract Nowadays, a large number of mechanical equipment working in harsh working environment will lead to strong background noise, which makes it difficult to extract feature information related to equipment fault. Bolt joint looseness inevitably occurs in engineering, which occupies a large proportion of all types of mechanical equipment faults. Therefore, it is quite difficult to extract the bolt looseness feature information. Based on this problem, a method based on subharmonic resonance and adaptive stochastic resonance (ASR) method is proposed to recognize whether the bolt is loose. First, a typical single bolted joint model is carried out dynamic analysis and numerical simulation, which verifies the specific conditions for the generation of subharmonic frequency related to bolt looseness. Then, a bolt looseness identification method based on ASR and coherence resonance (CR) is proposed. A quality factor index is defined, which is used to identify stochastic resonance (SR) and CR for bolt looseness identification. Finally, the effectiveness of this method is successfully verified by experiment, which effectively identifies bolt looseness under strong noise background.