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Corrosion is among the most critical factors leading to the failure of reinforced concrete (RC) structures. Less work has been devoted to nondestructive tests (NDT) to detect the corrosion degree of steel bars. The corrosion degree was investigated in this paper using an NDT method based on self-magnetic flux leakage (SMFL). First, a mathematic model based on magnetic dipole model was settled to simulate the SMFL of a V-shaped defect caused by corrosion. A custom 3-axis scanning device equipped with a magnetometer was used to scan the SMFL field of the 40 corroded steel bars. Experimental data obtained by scanning the 40 steel bars showed that the BZ curve of SMFL was consistent with the theoretical model analysis. Inspired by the qualitative analysis of the results, an index “K” based on a large number of experimental data was established to characterize the corrosion degree of steel bars. The experimental index “K” was linearly related to the corrosion degree α of steel bars. This paper provides a feasible approach for the corrosion degree NDT, which is not affected by the magnetization history and the initial magnetization state of steel bars.

The early fault impulses of rolling bearing are often submerged by harmonic interferences and background noise. In this paper, a fault diagnosis scheme called probabilistic principal component analysis assisted optimal scale average of erosion and dilation hat filter (OSAEDH-PPCA) is presented for the fault detection of rolling bearing. Based on morphological erosion operator and morphological dilation operator, a new morphological top-hat operator, namely average of erosion and dilation hat (AEDH) operator is firstly proposed to extract the fault impulses in the vibration signal. Simulation analysis shows the filter characteristics of proposed AEDH operator. Comparative analyses demonstrate that the feature extraction property of the AEDH operator is superior to existing top-hat operators. Then, the probabilistic principal component analysis is introduced to enhance the filter property of AEDH for highlighting the fault feature information of rolling bearing further. Experimental signals collected from the test rig and the engineering are employed to validate the availability of proposed method. Experimental results show that the OSAEDH-PPCA can effectively extract the early fault impulses from vibration signal of rolling bearing. Comparison results verify that the OSAEDH-PPCA has advantage in early fault detection of rolling bearing than other morphological filters in existence. INDEX TERMSRolling bearing; Morphological filter; Morphological operator; Probabilistic principal component analysis; Fault diagnosis. NOMENCLATURE Acronyms MM mathematical morphology MF morphological filter AVG average of opening and closing CMF average of opening-closing and closing-opening MG gradient of dilation and erosion DIF gradient of opening and closing AVGH average of opening and closing hat CMFH average of opening-closing and closing-opening hat SE structure element WTH white top-hat AED average of erosion and dilation AEDH average of erosion and dilation hat OSAEDH optimal scale average of erosion and dilation hat filter PPCA probabilistic principal component analysis CC correlation coefficient FEF feature energy factor SNR signal-to-noise ratio AMCMFH adaptive multiscale CMFH transform AMAVGH adaptive multiscale AVGH transform AMMGDE averaged multiscale MG filter AMMGCO averaged multiscale DIF filter ACDIF average combination difference morphological filter

Intelligent diagnosis applies deep learning algorithms to mechanical fault diagnosis, which can classify the fault forms of machines or parts efficiently. At present, the intelligent diagnosis of rolling bearings mostly adopts a single-sensor signal, and multisensor information can provide more comprehensive fault features for the deep learning model to improve the generalization ability. In order to apply multisensor information more effectively, this paper proposes a multiscale convolutional neural network model based on global average pooling. The diagnostic model introduces a multiscale convolution kernel in the feature extraction process, which improves the robustness of the model. Meanwhile, its parallel structure also makes up for the shortcomings of the multichannel input fusion method. In the multiscale fusion process, the global average pooling method is used to replace the way to reshape the feature maps into a one-dimensional feature vector in the traditional convolutional neural network, which effectively retains the spatial structure of the feature maps. The model proposed in this paper has been verified by the bearing fault data collected by the experimental platform. The experimental results show that the algorithm proposed in this paper can fuse multisensor data effectively. Compared with other data fusion algorithms, the multiscale convolutional neural network model based on global average pooling has shorter training epochs and better fault diagnosis results.