With the use of sensorless control strategy, mechanical position sensors can be removed from the gearbox, so as to decrease the maintenance costs and enhance the system robustness. In this paper, a switching PI control based model reference adaptive system (MRAS) observer using Fuzzy-Logic-Controller (FLC) is introduced for sensorless control of permanent magnet synchronous motor (PMSM) drives. The main work and innovation of this paper include: 1) A disturbance observer based voltage compensation method is proposed to solve the problems of voltage distortion in voltage source inverter (VSI) fed PMSM systems. 2) A double closed-loop FLC is designed for speed and current control. The response performance of speed regulation and the accuracy of the command voltage are improved, and the difficulty of manual adjustment of control parameters is avoided. 3) A sensorless control strategy of a switching PI control based MRAS observer is proposed for stator resistance tracking. By setting different thresholds, the proposed MRAS observer can choose the appropriate PI adaptive mechanism based on the amplitude of the current error variable, which can improve the accuracy of resistance estimation and improve the dynamic performance of sensorless control. 4) The experimental results are given to verify the availability of the proposed scheme.

Usually, two-phase current sensors are required to realize sensorless control of permanent magnet synchronous motor (PMSM). However, once the phase current sensor fails, the whole system will not work. In this article, a novel second-order extended state observer (SO-ESO) based current sensor fault-tolerant strategy for realizing PMSM sensorless control and multiparameter identification is proposed, which can significantly improve the system reliability. When a fault occurs in the A-C two-phase current sensor, the proposed scheme uses only a B-phase current sensor to estimate the d-q axis stator current and time varying resistance, achieving a high-performance sensorless control function. In order to reduce the chattering of the system, a sigmoid function is introduced in the SO-ESO. In addition, an SO-ESO based parallel speed and multiparameter identification scheme are proposed by using two model reference adaptive system observer (MRASO). The first MRASO is designed based on super twisting algorithm with a corrective feedback loop and is used for estimating rotor speed and position. The second MRASO only needs to identify inductance and permanent flux because the stator resistance is estimated by SO-ESO. The second MRASO and SO-ESO updates the three identified electrical parameters (i.e., stator resistance, inductance, and permanent flux) and estimated currents to the first MRASO to achieve high robustness sensorless control. The relevant results show that the scheme gives good results, and the system has good dynamic performance and better fault tolerance.

As a core tool, anomaly detection based on a generative adversarial network (GAN) is showing its powerful potential in protecting the safe and stable operation of industrial control systems (ICS) under the Internet of Things (IoT). However, due to the long-tailed distribution of operating data in ICS, existing GAN-based anomaly detection models are prone to misjudging an unseen marginal sample as an outlier. Moreover, it is difficult to collect abnormal samples from ICS. To solve these challenges, a dual auto-encoder GAN-based anomaly detection model is proposed for the industrial control system, simply called the DAGAN model, to achieve an accurate and efficient anomaly detection without any abnormal sample. First, an “encoder–decoder–encoder” architecture is used to build a dual GAN model for learning the latent data distribution without any anomalous sample. Then, a parameter-free dynamic strategy is proposed to robustly and accurately learn the marginal distribution of the training data through dynamic interaction between two GANs. Finally, based on the learned normal distribution and marginal distribution, an optimized anomaly score is used to measure whether a sample is an outlier, thereby reducing the probability of a marginal sample being misjudged. Extensive experiments on multiple datasets demonstrate the advantages of our DAGAN model.

In recent years, most existing domain-adapted bearing fault diagnoses for rotating machinery have been designed to decrease domain drifts for various operating conditions with an assumption that sufficient tag data are available. To overcome data scarcity, a possible solution is to use fault information of other machines of the same category to diagnose the status of a target machine (i.e., cross-machine diagnosis). This article proposes a variational auto-encoder (VAE)-based multisource deep domain adaptation model using optimal transport for cross-machine fault diagnosis of rotating machinery (named MDVAEOT). This is fundamentally different from most diagnostic models where both train and test data belong to the same machine. First, it uses unlabeled samples of the machines to be diagnosed to establish the target dataset and faulty samples of machines of the same category (containing labels) to form the source dataset. Additionally, the method performs feature extraction on the dataset using VAE networks and improves the reliability of extracted data features by the approximation of fixed probability. Finally, to shrink cross-machine differences between the two domains, we introduce optimal transport (OT) theory. OT distance is used to share fault-related features between the two domains mentioned above to complete the cross-machine diagnosis task. Better accuracy and timeliness are offered by this proposed means compared to other existing intelligent methods in this field.

The efficient damage detection of the wind turbine blade (WTB), the core part of the wind power, is very improtant to wind power. In this paper, an improved YOLOv7 model is designed to enhance the performance of surface damage detection on WTBs based on the low-quality unmanned aerial vehicle (UAV) images. (1) An efficient channel attention (ECA) module is imbeded, which makes the network more sensitive to damage to decrease the false detection and missing detection caused by the low-quality image. (2) A DownSampling module is introduced to retain key feature information to enhance the detection speed and accuracy which are restricted by low-quality images with large amounts of redundant information. (3) The Multiple attributes Intersection over Union (MIoU) is applied to improve the inaccurate detection location and detection size of the damage region. (4) The dynamic group convolution shuffle transformer (DGST) is developed to improve the ability to comprehensively capture the contours, textures and potential damage information. Compared with YOLOv7, YOLOv8l, YOLOv9e and YOLOv10x, this experiment’s results show that the improved YOLOv7 has the optimal detection performance synthetically considering the detection accuracy, the detection speed and the robustness.