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Machine vision-based structural health monitoring is gaining popularity due to the rich information one can extract from video and images. However, the extraction of characteristic parameters from images often requires manual intervention, thereby limiting its scalability and effectiveness. In contrast, deep learning overcomes the aforementioned shortcoming in that it can autonomously extract feature parameters (e.g. structural damage) from image datasets. Therefore, this study aims to validate the use of machine vision and deep learning for structural health monitoring by focusing on a particular application of detecting bolt loosening. First, a dataset that contains 300 images was collected. The dataset includes two bolt states, namely, tight and loosened. Second, a faster region-based convolutional neural network was trained and evaluated. The test results showed that the average precision of bolt damage detection is 0.9503. Thereafter, bolts were loosened to various screw heights, and images obtained from different angles, lighting conditions, and vibration conditions were identified separately. The trained model was then employed to validate that bolt loosening could be detected with sufficient accuracy using various types of images. Finally, the trained model was connected with a webcam to realize real-time bolt loosening damage monitoring.

As the most widely used coupling structure in electromechanical systems, bolt coupling is the important part in these systems. The reliability and strength of bolted joint are affected by pretension force, which is one of the most important factors to ensure the stability of bolt coupling. The inspection personnel hit the bolt with a hammer and judge the state of the bolt based on the sound. Although this method is very simple, the ability of the human ear to distinguish the knocking sound is poor, it can only distinguish the bolt with larger looseness. So a bolt loosening detection method based on audio classification is presented in this article. First, the hammering sound at different levels of bolt loosening was collected by smartphone. Then, the audio data were extracted to form a dataset. Finally, the support vector machine was used to train and test the dataset, and obtain the bolt loosening quantitative detection. A series of experiments were carried on to verify the accuracy and stability of this method. The results show that this method has high recognition accuracy and strong noise immunity. Therefore, this method can effectively reduce the occurrence of disasters.

Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method.

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