The extensive convolution computations, repetitive data transfers, and storage in convolutional neural networks (CNNs) result in prolonged fault diagnosis time. To address this issue, an in-memory convolutional neural network (IMCNN) is proposed for fast open-circuit fault (OCF) diagnosis of cascaded H-bridge multilevel inverters (CHMLIs). To be specific, an extended matrixization encoding (EME) method is first designed to convert the original 1-D diagnostic voltage signal. Then, convolutional calculations are physically performed based on an in-memory feature extraction (IM-FE) using memristor crossbar arrays, with pooling operations used to extract fault features. The extracted features are then input into a fully connected layer, and the Adam algorithm is used to train the model parameters to counter resistance drift. The trained model is then used to identify fault types. Experimental results on the three-phase five-level grid-connected CHMLI platform validate the performance of the proposed approach.

The marine atmospheric environment, characterized by a high chloride content, high humidity, and a high corrosion rate of structural steel, urgently demands anticorrosion methods that are compatible with other technologies such as alloy steel and organic coatings. In this study, an inhibitor-loaded conversion film was grown in situ on a steel surface to prevent chloride-induced corrosion. Specifically, a Mg-Fe layered double hydroxide (LDH) conversion film was grown on the steel surface, and p-aminobenzoate (pAB⁻) inhibitor ions were intercalated into the LDH. After half a year of natural corrosion in the actual marine atmospheric environment, the average corrosion rate of steel with the inhibitor@LDH film was 36.20 µm/a, which was 21% lower than that of the steel substrate (45.91 µm/a). The inhibitor@LDH film also effectively suppressed local pitting corrosion, with the density of corrosion pits significantly reduced by 64%. Furthermore, the inhibitor@LDH film promoted the formation of a denser and thinner rust layer on the steel surface, with a smaller crack width, fewer cracks, and an increased α-FeOOH/γ-FeOOH ratio. In summary, the inhibitor@LDH conversion film inhibits general and local corrosion on steel in a marine atmospheric environment.

The modular multilevel converter (MMC) is a critical component in modern medium- and high-voltage systems, whose reliability is severely compromised by open-circuit faults (OCFs) in insulated gate bipolar transistors (IGBTs). Consequently, accurate real-time fault diagnosis is essential to ensure safe and reliable operation. However, diagnostic accuracy is often compromised by the variability of fault characteristics under different operating conditions. Therefore, a hierarchical diagnosis method based on the arm capacitor voltage span (ACVS) is proposed. First, the adjacent-cycle ACVS values are analyzed to construct an adaptive threshold. A three-level localization scheme is then implemented: the faulty arm is detected via abnormal ACVS deviation; the faulty submodule (SM) is located by analyzing the voltage differences between adjacent SMs after sorting; and the faulty IGBT is identified based on the switching state of the faulty SM. As only a small number of voltage data are processed, the online computational burden is reduced. The proposed method is validated through both simulation and experiment under different operating conditions. The faults are located in less than 20 ms, and an overall accuracy of 0.99 is achieved in the tested scenarios. The results show that fault diagnosis can still be achieved under arm inductance mismatch and load transients.