At present, the bearing fault is one of the major mechanical faults, deep learning-based bearing fault diagnosis methods have been successfully developed. However, the fault data of bearings in the industrial environment are very few, which leads to the failure or performance degradation of conventional intelligent diagnosis methods. It costs a large amount of manpower and material to obtain complete samples of the industrial environment, which is unrealistic. Therefore, it is necessary to use a small number of actual fault signals for incremental learning. The existing incremental learning models cannot solve the entanglement problem of sample features, and the ability to obtain new samples by combining features is limited. In this article, the filter style-based generative adversarial network (FSGAN) is used to separate hidden features by mapping data features to intermediate latent space, and then, new signals are generated by feature reorganization. The different signals are generated by controlling the weight coefficient of style after network training, and then, valuable signals are selected by the t-distributed stochastic neighbor embedding (t-SNE) clustering analysis. The model can generate fault signals with unknown sizes under unknown conditions. Finally, the advantages of FSGAN are further verified by the quality evaluation of the generated signals and comparison with other models.

An Efficient Incremental Learning of Bearing Fault Imbalanced Data Set via Filter StyleGAN

As a typical cyber-physical system, multi-energy system is easier to suffer from the denial of service (DoS) attacks, which brings huge risks for the effective execution of distributed energy management algorithms. To address this issue, this paper studies the collaborative energy management problem for multi-energy system under DoS attacks, where the optimal operation of each energy hub and the optimal energy trading path among energy hubs are taken into account simultaneously. To enhance the system robustness, a distributed resilient double-gradient-descent based energy management strategy is proposed, which is designed by establishing multiple control protocols. The proposed method is initialization-free and can reasonably utilize the estimation to compensate the missing information during attack periods, which possesses strong robustness and adaptability against the DoS attacks. Furthermore, based on theoretical analysis, it is proved that the proposed method can still converge to the global optimal solutions although the connectivity of the communication network is broken during attack periods. Finally, several case studies are provided to verify the effectiveness and correctness of the proposed method.

Distributed Resilient Double-Gradient-Descent Based Energy Management Strategy for Multi-Energy System Under DoS Attacks

This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches

Multi-mode processing is a central feature of modern industry. The application of monitoring technology to multi-mode processing is crucial to ensure process safety and to enhance product quality. This paper describes the definition, nature and data characteristics of the multi-mode process. A complete classification framework for multi-mode process monitoring methods is produced, covering steady-state modes and transitional processes. After introducing basic concepts and describing research outcomes obtained for monitoring methods, prospects for multi-mode process monitoring technology are discussed. 

/pdf/progress-of-process-monitoring-for-the-multi-mode-process-a-3ekgofzw.pdf

Progress of Process Monitoring for the Multi-Mode Process: A Review

The temperature of rotary kiln, as one of the essential equipment in zinc smelting process, determines the product quality and resource utilization. However, since rotary kiln is a large-scale and highly coupled system, there are plenty of variables affecting the rotary kiln temperature, bringing too much redundant information to describe rotary kiln dynamics. In addition, due to the variations in raw materials, production load, and market demand, there exist various operation conditions, making it difficult to achieve stability control of the rotary kiln temperature with traditional control methods. To solve these problems, an error-triggered adaptive model predictive control (ET-AMPC) is proposed in this article. Specifically, since rotary kiln temperature is hard to regulate due to redundancy among variables and strong nonlinearity, an orthogonal maximum mutual information coefficient feature selection (OMICFS) method is first proposed to determine vital variables affecting temperature most. Then, aiming at the problem of changing operating conditions of rotary kilns, an ET-AMPC method is proposed, which can precisely adapt to different operating conditions and achieve stability control. Finally, experiments on a numerical simulation case and an industrial rotary kiln show the strength and reliability of the proposed method, which reduces 10%–20% trajectory tracking error in the period of operating condition changing and improves the control accuracy effectively. 

Rotary Kiln Temperature Control Under Multiple Operating Conditions: An Error-Triggered Adaptive Model Predictive Control Solution

The invention discloses an industrial process intelligent monitoring method and system based on distributed dictionary learning. The method comprises the following steps: each distributed node of an industrial system establishes a local dictionary through data collected by the distributed nodes; diffusion summation processing is carried out on the local dictionaries of all the distribution nodes to obtain a global dictionary of the industrial system; a fusion calculation center obtains a plurality of data from the industrial system as training samples, calculates a reconstruction error of eachtraining sample according to the global dictionary, and calculates a control limit of industrial process monitoring according to the reconstruction errors of all the training samples by adopting a kernel density estimation process; and the fusion calculation center obtains to-be-monitored data on line, calculates the reconstruction error of the to-be-monitored data according to the global dictionary, compares the reconstruction error of the to-be-monitored data with the control limit, and judges whether the industrial system is abnormal or not according to the comparison result. The load of an integration center is reduced through distributed dictionary learning, and the calculation efficiency is improved, so that the operation state of the industrial process is accurately monitored.

Industrial process intelligent monitoring method and system based on distributed dictionary learning

With the development of sensor and communication technology, industrial systems have accumulated a large amount of data. This data has provided new perspectives and methods for industrial system analysis, monitoring and control, which is proven to be of great significance. However, with the collection and storage of industrial data in a 7 × 24 manner, the computing and information processing capabilities of edge controllers and computers at industrial sites face new challenges. Therefore, this paper proposes a distributed dictionary learning algorithm based on the MapReduce framework. The dictionary learning method can efficiently extract useful information from high-dimensional data for process monitoring. In addition, deploying the algorithm under the MapReduce framework can achieve the purpose of parallel distributed computing, which would solve the issue that the ability of calculation and information processing is limited at industrial sites. Based on extensive numerical experiments, the proposed method can improve the effectiveness and robustness of process monitoring for industrial processes.

Distributed dictionary learning for industrial process monitoring with big data

Modern industrial processes are essentially complex multivariable systems with high interaction among variables. Moreover, in modern process control, it is imperative not only to track a setting point for the controlled object but also the performance indicators for the entire operation process, where the optimal operational feedback control of multivariable industrial processes is derived. In this paper, we propose an operation output-feedback predictive control (OOFPC) method for multivariable industrial processes based on model order reduction and model predictive optimization. Firstly, causality networks are employed to extract potential correlations between variables to reduce the high-dimensional feature space. Subsequently, a setpoint compensator is developed within the framework of model predictive control (MPC), taking into account both performance indicators and operational constraints. Finally, closed-loop control with stable adjustment of the controlled object is achieved by utilizing proportional-integral-derivative (PID) controllers. Feedback correction and rolling optimization enable optimal setpoint tracking and stable closed-loop control. Experiments on a grinding process control case demonstrate the effectiveness of the proposed control strategy.

Operation Output-Feedback Predictive Control Based on Model Order Reduction and Predictive Optimization in Industrial Processes

In modern industrial systems, the processes often operate under different modes. Potential fault diagnosis and mode identification are extremely vital to maintain the system safe and reliable. Recently, many methods have been proposed to address these problems separately. Moreover, many of them make an assumption that the data from industrial site only contain the Gaussian noise. However, this assumption is not held in practice, which further reduces their performances. Considering the complicated noise feature of industrial data, we came up with an improved dictionary learning method to settle these problems simultaneously. First, the measurement data were decomposed into three parts: clean data, mode-based noise, and dense Gaussian noise. Then, the dictionary learning method was proposed to characterize each part separately. Inspired by the framework of label-consistent K-SVD, the mode information was incorporated into the dictionary learning method, and we developed a solution to settle the multiple dictionary learning optimization problem. Finally, when new samples arrive, we reconstruct them under the learned dictionary so that each sample’s mode and abnormal data and can be determined. The experiments on two different types of simulated process and aluminum electrolysis process show the strength and reliability of our method, which indicates the engineering application value of the proposed method.

Multimode Process Monitoring and Mode Identification Based on Multiple Dictionary Learning

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