Physics

Pattern recognition (psychology)

Entropy (arrow of time)

Filter (signal processing)

Artificial intelligence

Acoustics

Hilbert–Huang transform

Computer science

Quantum mechanics

Sample entropy

Computer vision

Artificial neural network

Percussion

Smart Materials and Structures

In this paper, a novel percussion-based bolt looseness monitoring approach using intrinsic multiscale entropy analysis and back propagation (BP) neural network is proposed. The percussion-caused audio signals of bolt connection are decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain intrinsic mode functions (IMFs). The IMFs are in order of high-to-low instantaneous frequencies and contain underlying dynamical characteristics of audio signals. Multiscale sample entropy (MSE) is improved by smoothed coarse graining process, and the proposed improved multiscale sample entropy (IMSE) values of certain IMFs are adopted as condition indicators in bolt looseness monitoring. The intrinsic multiscale entropy analysis consisting of CEEDMAN and IMSE extracts underlying dynamical characteristics during percussion-caused audio signal processing to identify bolt looseness conditions. The condition indicators, namely IMSE values at smallest scale factors, are employed as input of BP neural network for training and testing, to achieve accurate and stable bolt looseness condition monitoring. The effectiveness and superiority of the proposed approach have been validated by theoretical derivation and practical experimental researches, and the adaptivity and robustness of the proposed approach are also illustrated. The results of the research in this paper demonstrate the proposed approach is promising in practical applications of bolt looseness monitoring.

Percussion-based bolt looseness monitoring using intrinsic multiscale entropy analysis and BP neural network