Many vision-based bolt looseness detection methods that directly observe the bolts have been developed. However, these methods have many limitations in terms of the conditions and processes of their implementation. To address these problems, this paper proposed a fully automated vision-based bolt looseness detection method for a rigid multi-bolt connection. The proposed method combines digital shearing speckle pattern interferometry (DSSPI) and recurrent neural network (RNN) and involves capturing speckle fringe patterns under various looseness cases using the DSSPI system and classifying these patterns with an RNN model to detect the loose bolts. The proposed method can detect all the bolts within the measured surface at one time, which is efficient. On the other hand, it eliminates the need for prior information such as the initial angle and position of each bolt. It can even detect unseen bolts in multi-bolt connections, making it applicable for connections in complex structures in which occlusion often occurs. Additionally, the method eliminates the complex process of distortion rectification. These features make the method achieve a single-judgment time (four bolts at one detection) of only 4.70 millisecond with a detection accuracy over 99%, which has potential for the real-time detection of loose bolts in multi-bolt connections.

To address the complexity of small or unique reconstruction distances in digital holography, we propose an inverse cross-correlation-based algorithm for the digital holographic imaging of multiplanar objects with a large depth of field. In this method, a planar output mapping is closely around the objects, and it is established by calculating the image inverse cross-correlation matrix of the reconstructed image at similar reconstruction distances, whereby the object edges serve as the result guide. Combining the search for edge planes with the depth estimation operator, the depth of field of digital holography is improved, thus allowing for a digital holography that is capable of meeting the requirements of the holographic imaging of multiplanar objects. Compared with the traditional depth estimation operator method, the proposed method solves the reconstruction ambiguity problem in multiple planes with a simple optical path, and no additional optical or mechanical devices need to be added, thus greatly improving the reconstruction quality. The numerical calculation results and the experimental results with multiplanar samples validate the effectiveness of the proposed method.

Accurate measurement of bolt preload remains a persistent engineering challenge. Conventional vision-based methods, which merely assess loosening status based on bolt angular/height changes without establishing mathematical relationships between these parameters and preload, exhibit inherent limitations. Leveraging the fundamental phenomenon of coupling surface deflection induced by bolt preload, this paper proposes a novel approach employing digital shearing speckle pattern interferometry to quantify bolt preload by measuring the corresponding strain directly. Experimental verification across three distinct coupling structures confirmed a robust linear correlation (coefficient of determination, R2>0.99) between strain and preload. The resolution of the method achieves 50 N, and the relative error is less than ±2%, which are analytically derived and experimentally validated. Crucially, our technique achieves a wide detection angle range (30°–90°) without requiring direct bolt visibility and successfully detects non-rotation loosening scenarios, significantly enhancing applicability in constrained structural environments. Furthermore, the method is independent of bolt length and length-to-diameter ratios and demonstrates broad applicability across diverse structural configurations. Finally, the mathematical relationship between strain and preload was derived based on thin plate theory and validated with experimental results. The proposed method established a novel measurement paradigm for high-precision bolt preload quantification.