This paper aims to investigate the individual effect of corrosion‐induced crack, corroded rebar shape, and rust around rebar on the bond properties of reinforced concrete (RC) members. First, test specimens were corroded by accelerated electric corrosion method with varying corrosion degree ranging from 0 to 25%. Test specimens were divided into different groups. For the normal corrosion group, bond test was conducted directly after the corrosion test to investigate the combined effects including corrosion‐induced crack, corroded rebar shape, and rust on bond deterioration. In another group, after corroded rebar was taken out from test specimen, rust was removed before casted into new concrete to evaluate single effect of corroded rebar shape. Similarly, corroded rebar with rust was casted into new concrete to evaluate the effect of the formation of rust on bond degradation. Experimental results quantitatively illustrate the influential factors including corrosion degree, corrosion crack width and geometry of corroded rebar. As the important finding, corrosion crack in concrete is concluded to be a more dominant factor than the corroded rebar shape and rust accumulation in bond deterioration mechanism. Moreover, rust accumulation contributes to the improvement of bond deterioration induced by corroded rebar shape.

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To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

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