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Using different fast charging strategies for lithium-ion batteries can affect the degradation rate of the batteries. In this case, predicting the capacity fade curve can facilitate the application of new batteries. Considering the impact of fast charging strategies on battery aging, a battery capacity degradation trajectory prediction method based on the TM-Seq2Seq (Trend Matching—Sequence-to-Sequence) model is proposed. This method uses data from the first 100 cycles to predict the future capacity fade curve and EOL (end of life) in one-time. First, features are extracted from the discharge voltage-capacity curve. Secondly, a sequence-to-sequence model based on CNN, SE-net, and GRU is designed. Finally, a trend matching loss function is designed based on the common characteristics of capacity fade curves to constrain the encoding features of the sequence-to-sequence model, facilitating the learning of the underlying relationship between inputs and outputs. TM-Seq2Seq model is verified on a public dataset with 132 battery cells and multiple fast charging strategies. The experimental results indicate that, compared to other popular models, the TM-Seq2Seq model has lower prediction errors. [ABSTRACT FROM AUTHOR]

The inherent randomness, fluctuation, and intermittence of photovoltaic power generation make it difficult to track the scheduling plan. To improve the ability to track the photovoltaic plan to a greater extent, a real-time charge and discharge power control method based on deep reinforcement learning is proposed. Firstly, the photovoltaic and energy storage hybrid system and the mathematical model of the hybrid system are briefly introduced, and the tracking control problem is defined. Then, power generation plans on different days are clustered into four scenarios by the K-means clustering algorithm. The mean, standard deviation, and kurtosis of the power generation plant are used as the features. Based on the clustered results, the state, action, and reward required for reinforcement learning are set. In the constraint conditions of various variables, to increase the accuracy of the hybrid system for tracking the new generation schedule, the proximal policy optimization (PPO) algorithm is used to optimize the charging/discharging power of the energy storage system (ESS). Finally, the proposed control method is applied to a photovoltaic power station. The results of several valid experiments indicate that the average errors of tracking using the Proportion Integral Differential (PID), model predictive control (MPC) method, and the PPO algorithm in the same condition are 0.374 MW, 0.609 MW, and 0.104 MW, respectively, and the computing time is 1.134 s, 2.760 s, and 0.053 s, respectively. The consequence of these indicates that the proposed deep reinforcement learning-based control strategy is more competitive than the traditional methods in terms of generalization and computation time. [ABSTRACT FROM AUTHOR]

With the increasing proportion of solar grid-connected, the establishment of an accurate photovoltaic (PV) power prediction model is very important for safe operation and efficient dispatching of a power grid. Considering the multi-level periodicity of PV power caused by many factors, such as seasons and weather, a short-term PV power prediction model based on transfer component analysis is designed by introducing the idea of transfer learning. In order to measure the uncertainty of numerical weather prediction (NWP) and power sequence, a novel algorithm considering weather similarity and power trend similarity is proposed. First, the intrinsic trend is measured by extracting permutation entropy, variance, and mean from the historical PV power sequence. Second, weighting of NWP is accomplished based on the Pearson correlation coefficient. PV power data are divided into different clusters by K-medoids clustering. At the same time, the transfer component analysis alleviates the time-varying problem of data distribution caused by multi-level time periodicity and effectively improves the prediction accuracy of the model. Finally, simulation experiments are carried out on the PV power output dataset (PVOD). The results show that the prediction accuracy of the proposed method is better than the traditional methods, and the accuracy and applicability of the proposed method are verified. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning fault detection and prediction framework combining principal component analysis (PCA) and Informer is proposed to solve the problem of online monitoring of nuclear power valves which is hard to implement. More specifically, PCA plays the role of dimensionality reduction and fault feature extraction. It maps data with multi-dimensional space to low-dimensional space and extracts the main features. At the same time, the T-square and Q statistic thresholds are also provided to realize abnormal status monitoring. Meanwhile, Informer is a long-term series prediction method. It encrypts and decrypts data through the encoder and decoder to train a prediction model. Through the training of fault data, fault prediction can be realized. Experiments based on the sound waves collected from real valves can be continued, which also illustrates the effectiveness of the PCA–Informer model for fault diagnosis and fault prediction of nuclear power valves. Therefore, the online monitoring and maintenance of nuclear valves and other important equipment, without shutting down the nuclear station, can be achieved. [ABSTRACT FROM AUTHOR]

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method. [ABSTRACT FROM AUTHOR]

Exfoliation of two-dimensional (2D) materials is an issue of concern among scientific researchers. This is because many solvents such as N, N-dimethylformamide and N-methyl-2-pyrrolidone that are capable of better dispersion of 2D materials are relatively toxic and nonvolatile. This work focused on the reasonable design and mixture of two or three less toxic and volatile solvents based on Hansen solubility parameters theory to demonstrate the excellent exfoliation of 2D materials particularly reduced graphene oxide (rGO) and black phosphorus (BP). Polyvinylpyrrolidone (PVP) was introduced as a surfactant to functionalize rGO to help improve its dispersion. Results showed that PVP could effectively functionalize graphene. Few layers of rGO and BP were facilely achieved with 2–3 nm thickness from the use of the designed solvent mixtures, indicating the accomplishment of solvent mixtures in exfoliation/dispersion roles instead of the use of other toxic and nonvolatile solvents. [ABSTRACT FROM AUTHOR]

Probability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense. [ABSTRACT FROM AUTHOR]

For high-precision positioning and navigation systems, the positioning precision of receiver is jeopardized by multipath interference. Multipath suppression methods based on data processing have drawn much attention recently. The critical step of data processing-based method is to estimate multipath parameters. However, most multipath suppression methods falling into the category of data processing-based methods are limited to Gaussian noises, which means the performance of these methods may be degraded in non-Gaussian noises which are encountered quite often in reality. Besides, only static multipath case is studied in most existing literature, which is not sufficient for potential applications since the occurrence and the disappearance of multipath are always changeable along the movement of receiver. To address these problems, the maximum correntropy criterion(MCC) and the generalized maximum correntropy criterion(GMCC) are integrated into the traditional adaptive multipath estimation(AME) algorithm, named as MCC-AME and GMCC-AME, to handle the dynamic multipath estimation problem in non-Gaussian noises. Furthermore, MCC-AME and GMCC-AME are further improved by adopting forgetting factor, named as RMCC-AME and RGMCC-AME, to improve estimation accuracy and reduce time consumption in a recursive way. The four proposed algorithms also address the problems that the formerly proposed entropy-based multipath estimation algorithms are sensitive to the initial estimation and that the assumption of fixed number of multipath is required. The performance of the four proposed algorithms are analyzed and compared. The analytical results show that GMCC-AME outperforms MCC-AME regarding convergent speed, estimation accuracy and robustness, and RGMCC-AME performs even better than RMCC-AME in the same regard. [ABSTRACT FROM AUTHOR]

Byline: Liyun Yang, Mifeng Ren, Dongbo Hao, Liqin Yang In this paper, the problem of delay-dependent robust stability for uncertain stochastic neural networks with time-varying delay is considered. Based on Lyapunov stability theory combined with linear matrix inequalities (LMI) techniques, some new delay-dependent stability criteria in terms of LMI are derived by introducing some free weighing matrices and using Leibniz-Newton formula which can be selected properly to lead to less conservative results. Finally, two examples are given. One is given to illustrate, the other is an extended model for prevenient systems.

In this paper, a data-driven superheating control strategy is developed for organic Rankine cycle (ORC) processes. Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy (QMEE) is adopted to construct the performance index of superheating control systems. Furthermore, particle swarm optimization (PSO) algorithm is applied to obtain optimal control law by minimizing the performance index. The implementation procedures of the presented superheating control system in an ORC-based waste heat recovery process are presented. The simulation results testify the effectiveness of the presented control algorithm. [ABSTRACT FROM AUTHOR]

In this study, an event‐triggered distributed filtering problem is investigated for non‐Gaussian systems over wireless sensor networks (WSNs). Each sensor node transmits the data to its neighbours according to predetermined send‐on‐delta (SoD) communication mechanism. Since stochastic disturbances imposed on WSNs and plants and stochastic behaviour induced by WSNs are usually non‐Gaussian, the survival information potential criterion is employed to construct a distributed filter for each sensor node subject to sporadic communications over WSNs and non‐Gaussian disturbances. In addition, the energy constraints on filter gain are also taken into account. The incremental filtering algorithm is given and the boundedness of the filter parameter is also analysed. The effectiveness of the proposed algorithm is verified by an illustrative example. [ABSTRACT FROM AUTHOR]

A neural network (NN)‐based cascade control system is developed, in which the primary proportional–integral–derivative (PID) controller is constructed by NN. A new entropy‐based measure, named the centred error entropy (CEE) index, which is a weighted combination of the error cross‐correntropy (ECC) criterion and the error entropy criterion (EEC), is proposed to tune the NN‐PID controller. The purpose of introducing CEE in controller design is to ensure that the uncertainty in the tracking error is minimised and also the peak value of the error probability density function being controlled toward zero. The NN‐controller design based on this new performance function is developed and the convergent conditions are investigated using a linearisation technique. During the control process, the CEE index is estimated by a Gaussian kernel function. Adaptive rules are developed to update the kernel size in order to achieve more accurate estimation of the CEE index. This NN cascade control approach is applied to superheated steam temperature control of a simulated power plant system, from which the effectiveness and strength of the proposed strategy are discussed by comparison with NN‐PID controllers tuned with EEC and ECC criterions. [ABSTRACT FROM AUTHOR]

In this brief, a new tracking control algorithm for a class of networked control systems (NCSs) with non-Gaussian random disturbances and delays is proposed. Due to non-Gaussian random noises involved in the systems, solely controlling the expected value of the linear quadratic performance index is insufficient to obtain a satisfactory optimal control algorithm. The proposed method in this note applies the ( h,\phi )- entropy of the quadratic performance index to characterize the randomness of the closed-loop system. In order to calculate the entropy, the formulation of the joint probability density functions (JPDFs) of the quadratic performance index is presented in terms of known JPDFs of disturbances and time-delay. By minimizing the entropy of the performance index, a new control algorithm is obtained for the considered nonlinear and non-Gaussian NCSs. In addition, the proposed control strategy is applied to a networked DC motor control system, which is subjected to non-Gaussian random disturbances and delays. The experimental results show the effectiveness of the obtained method. [ABSTRACT FROM AUTHOR]

In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method. [ABSTRACT FROM AUTHOR]

Data-driven soft sensor technology has been widely used in process monitoring, quality prediction, etc. However, there are dynamic time-varying and concept drift problems in industrial processes, making the accurate modeling of the soft sensor is still a challenging task. To solve the above problems, this paper proposes a concept drift adaptive dynamic partial least squares method. The method maps high-dimensional process data into a low-dimensional latent variable subspace. Dynamic information is introduced by establishing the dynamic regression relationship between process latent variables and quality latent variables. At the same time, transfer learning is used to align second-order statistics between latent variables of data from different distributions to solve the concept drift problem. The experiments on multiple industrial datasets show that the method proposed in this paper can effectively reduce prediction errors and improve the generalization ability of the model. [Display omitted] • Distribution alignment and dynamic system modeling are realized under NIPALS. • The objective function is organized into the form of Rayleigh quotient. • There are few hyperparameters and they have better interpretability. • Experiments show that our method can adapt to the change of data distribution well. [ABSTRACT FROM AUTHOR]

Bearings are widely used in many steam turbine generator sets and other large rotating equipment. With the rapid development of contemporary industry, there is a great number of rotating equipment in various large factories, such as nuclear power plants. As the core component of rotating machinery, the failure of rolling bearings may lead to serious accidents during the industrial production operation. In order to accurately diagnose the fault status of rolling bearings, a novel long short-term memory (LSTM) model with discrete wavelet transformation (DWT) for multi-sensor fault diagnosis is proposed in this paper. The main purpose of this paper is to use the DWT-LSTM model to diagnose the health of rolling bearings. Firstly, the DWT is used to obtain detailed fault information in both different frequency and time scales. Then, the LSTM network is employed to characterize the long-term dependencies hidden in the time series of the fault information. The proposed DWT-LSTM method makes full use of the advantages of feature extraction based on expert experience and deep network learning to discover complex patterns from a large amount of data. Finally, the feasibility and efficiency of the proposed method are illustrated by comparison with the existing methods. [ABSTRACT FROM AUTHOR]

The traditional soft sensor models are based on the independent and identical distribution assumption, which are difficult to adapt to changes in data distribution under multiple operating conditions, resulting in model performance deterioration. The domain adaptive transfer learning methods learn knowledge in different domains by means of distribution alignment, which can reduce the impact of data distribution differences, and effectively improve the generalization ability of the model. However, most of the existing models established by domain adaptation methods are static models, which cannot reflect the dynamic characteristics of the system, and have limited prediction accuracy when applied to dynamic system modeling under multiple operating conditions. The dynamic system modeling methods can effectively extract the dynamic characteristics of the data, but they cannot deal with the concept drift problem caused by the change of data distribution. This paper proposes a new dynamic transfer partial least squares method, which maps the high-dimensional process data into the low-dimensional latent variable subspace, establishes the dynamic regression relationship between the latent variables and the labels, and realizes the systematic dynamic modeling, at the same time, the model adds regular terms for distribution alignment and structure preservation, which realizes dynamic alignment of data distribution difference. The effectiveness of the proposed method is validated on three publicly available industrial process datasets. [Display omitted] • This paper proposes a novel dynamic transfer partial least squares algorithm (DTPLS). • DTPLS can solve the concept drift problem under the multiple working conditions. • Distribution alignment regularization is designed based on distribution differences. • Structure preservation regularization is designed based on smoothness assumptions. [ABSTRACT FROM AUTHOR]

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed. [ABSTRACT FROM AUTHOR]

In this paper, a novel data-driven single neuron predictive control strategy is proposed for non-Gaussian networked control systems with metrology delays in the information theory framework. Firstly, survival information potential (SIP), instead of minimum entropy, is used to formulate the performance index to characterize the randomness of the considered systems, which is calculated by oversampling method. Then the minimum values can be computed by optimizing the SIP-based performance index. Finally, the proposed strategy, minimum entropy method and mean square error (MSE) are applied to a networked motor control system, and results demonstrated the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]