In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

Liraglutide treatment can improve glycemic control with a concomitant weight loss, but the underlying mechanism on weight loss is not completely understood. Cardiac natriuretic peptides (NPs) can resist body fat accumulation through increasing adipocytes lypolysis. In this study, we tested the hypothesis that liraglutide-induced weight loss was associated with increased plasma NPs concentrations. Thirty-one outpatients with type 2 diabetes (T2D) treated with metformin and other oral antidiabetic drugs except for thiazolidinediones (TZDs) were subcutaneously administered with liraglutide for 12 weeks. Body composition, abdominal visceral adipose tissue areas (VAT) and subcutaneous adipose tissue areas (SAT) were assessed at pre- and post-treatment by dual-energy X-ray absorptiometry (DXA) scanning and abdominal computerized tomography (CT). Plasma atrial natriuretic peptides (ANP) and B-type ventricular natriuretic peptides (BNP) concentrations were tested by commercial ELISA Kit quantitatively. Following 12-week liraglutide treatment, body weight, waist circumference, total fat and lean mass, fat percentage, SAT and VAT areas were significantly reduced from baseline. Concurrently, plasma ANP and BNP levels were significantly increased following 12-week liraglutide treatment. There were significant correlations between the reductions in body compositions and the increases in both plasma ANP and BNP levels. There were significant correlations between increases in both plasma ANP and BNP levels and changes in liraglutide-induced body composition. Our data implied that increases in plasma NPs may add a novel dimension to explain how liraglutide induces weight loss.

Object detectors based on convolutional neural networks (CNNs) have been widely deployed in industrial production for safety detection to guarantee the security of workers, and safety helmet detection is one of the most crucial application scenarios. In this article, we first consider the insufficiency of the existing largest open-source helmet detection dataset SHWD and introduce our safety helmet detection dataset which contains various industrial scenarios that are lacking in the former dataset. In addition, we reconsider the sample selection method of the Yolo series and propose a hierarchical positive sample selection (HPSS) mechanism in the training process, which improves the fitting ability of YoloV5. Furthermore, inspired by object detection in continuous frames from videos, we propose a post-processing algorithm based on box density to effectively suppress the appearance of false detection. Under the confidence threshold is 0.1, the combination of the two optimization strategies improves the $F1$ -dscore of YoloV5s by 12.47% without increasing any calculation. The dataset will be open-source in the near future.

This paper proposes a novel framework to address the model-vehicle mismatch (MVM) challenge of yaw stability control, which introduces the Gaussian Process-based Model Predictive Control into the classical two-layer structure. In the upper layer, the supervisor controller calculates the compensation torque required for vehicle turning. Gaussian Process regression is employed to compensate model mismatch and unmodelled dynamics. The chance constraints on sideslip angle are converted into double half-space constraints by the normal distribution quantile function. In the lower layer, the subordinate controller utilises the nonlinear slip rate model to distribute torque to the wheels in the form of braking torque. A new assignment rule of braking torque weight is proposed according to the yaw torque analysis. The stability of the two-layered learning MPC is ensured through proof and simulation. In the Simulink and Carsim co-simulation environment, the superiority of the proposed method is verified in the task of rapid double-lane change. Compared with the traditional MPC-based yaw stability controller, the proposed method has increased the stability tracking index and energy consumption index by around 13% and 8%.

The rapid development of industrial technology has led to the expansion of production scales. As a result, industrial data present hard-to-handle characteristics such as high dimensionality, time sequence coupling, and strong nonlinearity. Data-driven modeling techniques can reduce the difficulty in developing industrial soft sensors, and have been widely utilized in industrial production processes. The long short-term memory (LSTM) network is an effective tool for time series forecasting, but its performance is limited when dealing with high-dimensional data. To address this issue, this article proposes a novel approach called multiattribute space-based LSTM (MAS-LSTM). In MAS-LSTM, the K-shape clustering algorithm is utilized to divide the input attribute space of samples into different categories. Each attribute category is then handled by an individual LSTM network, allowing each LSTM network to contain additional attribute category information and process smaller dimension input data. Next, the processing outcomes of multiple LSTMs are combined into a fully connected layer to obtain the prediction results. Finally, a soft sensor based on MAS-LSTM has been established. To test its performance, real-world industrial data from the debutanizer column and the production process of purified terephthalic acid are used in the case study. Simulation results show that the prediction accuracy of MAS-LSTM is greatly improved compared to other models.