Accurate prediction of mill load parameters is crucial to improving grinding efficiency and saving energy. Traditional prediction models have challenges such as poor interpretability, low prediction efficiency and differences in data distribution. This study innovatively proposed a multi-task prediction model that integrates physical information and domain adaptation. By constructing a physical-data-driven hybrid model, the physical relationship between mill load parameters is embedded into the model as prior knowledge to improve the prediction accuracy of the model. At the same time, multi-task learning is used to predict the material-to-ball volume ratio and the pulp density at the same time, which improves efficiency and reduces repetitive work. The domain adaptation method is introduced to ensure that the model maintains stable prediction performance when the data distribution changes. Laboratory ball mill data verification shows that the proposed model not only improves the prediction accuracy, but also adapts well to variable working conditions, showing significant superiority.

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Aiming to address soft sensing model degradation under changing working conditions, and to accommodate dynamic, nonlinear, and multimodal data characteristics, this paper proposes a nonlinear dynamic transfer soft sensor algorithm. The approach leverages time-delay data augmentation to capture dynamics and projects the augmented data into a latent space for constructing a nonlinear regression model. Two regular terms, distribution alignment regularity and first-order difference regularity, are introduced during data projection to address data distribution disparities. Laplace regularity is incorporated into the nonlinear regression model to ensure geometric structure preservation. The final optimization objective is formulated within the framework of partial least squares, and hyperparameters are determined using Bayesian optimization. The effectiveness of the proposed algorithm is demonstrated through experiments on three public datasets.

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The industrially collected process data usually exhibit non-Gaussian and multi-mode characteristics. Due to sensor failures, irregular disturbances, and transmission problems, there are unavoidable outliers that make the data exhibit heavy-tailed characteristics. To this end, a variational auto-encoder regression method based on the mixture Laplacian distribution (MLVAER) is proposed, by introducing a type-II multivariate Laplacian distribution in the latent variable space for robust modeling, and further extending it to the mixture form to accommodate multi-mode processes, the corresponding reparameterization trick is finally proposed for the mixture form of this distribution for neural network gradient descent training. The model based on this distribution assumption has higher degrees of freedom than the model based on the traditional multivariate Laplace distribution assumption when the network structure is the same. Numerical simulation and experiments on two industrial examples demonstrate that the proposed algorithm reduces the root mean square error by over 15% compared to other algorithms.

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Accurately predicting runoff is crucial for managing water resources, preventing and mitigating floods, scheduling hydropower plant operations, and protecting the environment. The hydrological dynamic composite system that forms runoff is complex and random, and seemingly random behavior may be caused by nonlinear variables in a simple deterministic system, which poses a challenge to runoff prediction. In this paper, we construct parallel and multi-timescale reservoirs from a chaotic theory perspective to simulate the stochasticity of chaotic systems. We propose a multi-task-based "Decomposition-Integration-Prediction" (Multi-SDIPC) model for runoff prediction. To validate our research results, we use the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset and compare our proposed model with 10 baseline models. The results show that our model has an average NSE metric of 0.83 and exhibits higher accuracy, better generalization, and greater stability than the other models in multi-step forecasting. Based on our findings, we recommend wider application of the Multi-SDIPC model in different regions of the world for medium or long-term runoff prediction.

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Aiming at the problem of mismatch between real-time data distribution and modeling data distribution caused by the change of working conditions in industrial process, which leads to the performance deterioration of the soft sensor model, a multi-source unsupervised soft sensor method based on joint distribution alignment and mapping structure preservation is proposed. Firstly, the method uses the hypergraph to establish the complex structure of feature and label, and clusters the hypergraph matrix in multiple views to completely construct the class pseudo label; then dynamic distribution alignment is used to adapt marginal distribution and conditional distribution between the data of historical working conditions and the current working conditions, and the hypergraph Laplacian operator is introduced for manifold regularization to prevent the mapping relationship between feature and label from being destroyed; finally, similar working conditions are introduced to further enhance the robustness of the model. The experimental results show that compared with the traditional unsupervised soft sensor methods, the method used in this paper can effectively improve the prediction accuracy of the model.

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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.

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The problem of misalignment of the original measurement model is caused by nonlinear, time-varying characteristic of the batch process. In this paper, a method based on geodesic flow kernel (GFK) for feature transfer is proposed. By mapping data into the manifold space, the feature transfer from source domain to target domain is implemented. Distribution adaptation of real-time data and modeling data is performed to reduce the distribution difference between them. The historical data through distribution adaptation is used to establish a regression model to predict the real-time data, by which the unsupervised batch process soft sensor modeling is realized. The application of predicting the concentration of penicillin between different batches during the fermentation of penicillin demonstrated that the prediction accuracy of the model can be improved more effectively than the traditional soft sensor method.

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A novel approach based on infrared spectrum analysis for early-warning of longitudinal tearing of the conveyor belt was proposed in the paper. Unlike most existing methods, the proposed method monitors the change process of the infrared radiation field of the longitudinal tearing through the infrared thermal imaging technology, and judges whether there is the risk of the longitudinal tearing of the conveyor belt through the frequency-domain characteristic coefficient T of the infrared radiation field. Experimental results exhibit that the characteristic coefficient T can quantitatively describe the change characteristics of infrared radiation field during the tearing process of conveyor belt. When the conveyor belt is complete penetration, the T value fluctuates violently from 0.6 to 1.6. This characteristic can be used as the precursor information of the tearing process, which broadens the train of thought for identification and early warning of conveyor belt longitudinal tearing.

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In various industry productions belt conveyors are widely used for continuous dry bulk material transport. A large scale belt conveying system is generally composed of cascaded single conveyors consuming considerable electric power. According to DIN22101 regulating the belt speed and thereby maximizing the load on the belt may lead to a certain reduction of the needed electrical drive power. To avoid the operational risks, which may be caused by the dynamics of the system, preview control strategy has been proposed for belt conveyor speed control. In case the loading scenarios of a primary conveyor can be measured, the material feeding rate to following conveyors can be previewed. To reduce the influence of noises during material flow measurement and control synchronization, this paper presents the research of adopting Kalman filter to preview control to improve the transient operations of controlled belt conveyors as well as the energy efficiency of the system. Compared to two speed control strategies, the non-constraint preview control and the fuzzy speed control, experimental results show that the proposed Kalman filter based preview control reduces the noise interferences towards softer and safer speed regulation process.

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Belt conveying systems require considerable energy consumption that forms a large part in material handling operations. DIN 22101 indicates that reducing the belt speed and thereby maximizing the load on the belt may lead to a certain reduction of the required electrical drive power. Previous researches have proved that the energy savings for belt conveyors can be achieved based on different speed control strategies. However, in some industrial applications, for instance in long wall mining the material to be loaded onto a belt is neither predictable nor controllable, the speed control of conveyors is not always feasible. In large scale material transport, a belt conveying system is composed of a series of individual conveyors. In case the loading scenarios of a primary belt conveyor can be measured, the material feeding rate to following conveyors can be previewed as the input for speed control in overall transport operation. This paper presents how a preview control strategy can be applied to realize the speed regulation of such conveying systems composed of series belt conveyors. After analysing the power consumption of belt conveyors and developing the preview control model, the operational risks regarding speed adjustment are taken into account as the constraints for optimal preview control. Compared to other control strategies such as fuzzy speed control, the proposed preview control system is able to reduce the operational risks during the control process of regulating the speed of belt conveyors.@en

In the operation process of wet ball mill, there are often multi-modal and multi-condition problems. In this paper, a multi-view based domain adaptive extreme learning machine (MVDAELM) was used to measure the mill load. Firstly, the correlation relationship between the load parameters and the two views (vibration and acoustic signals of the ball mill) was obtained by Canonical Correlation Analysis (CCA) respectively. Secondly, a small number of labeled data from the target domain were introduced to construct a Domain Adaptation Extreme Learning Machine (DAELM) model under manifold constraints, which solve the mismatch problem caused by the change of working conditions in the multi-condition grinding process. Finally, based on the correlation coefficient obtained before, the two views domain adaptive load parameter soft sensor model was integrated to solve the uncertainty problem in single-modal data modeling. The experimental results show that the proposed method can effectively improve the learning accuracy of the soft sensor model under multi-modal conditions.

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Impurity in transparent-bottled liquid is a serious production accident in the field of beverage and medicine industry. However, the existing detection systems are difficult to distinguish impurities with dynamic interference (bubbles and stains) and detect impurities located at the edge of the bottle. In order to solve the problems stated above, a new machine vision system for detecting tiny and dynamic impurities is proposed in this paper. In the system, circularity calculation, longitudinal frame-difference method, orthogonal-axis inspection and K-Nearest Neighbor (KNN) machine learning algorithm are combined together to realize the automatic and real-time detection. Experimental results demonstrate that, after completing machine learning, the weighted error of the proposed system for detecting impurities can be effectively controlled at about 0.9% even in dynamic interference environment, which is great significance to safety production in beverage and medicine industry.

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Infrared and visible light image fusion technology is a hot spot in the research of multi-sensor fusion technology in recent years. Existing infrared and visible light fusion technologies need to register before fusion because of using two cameras. However, the application effect of the registration technology has yet to be improved. Hence, a novel integrative multi-spectral sensor device is proposed for infrared and visible light fusion, and by using the beam splitter prism, the coaxial light incident from the same lens is projected to the infrared charge coupled device (CCD) and visible light CCD, respectively. In this paper, the imaging mechanism of the proposed sensor device is studied with the process of the signals acquisition and fusion. The simulation experiment, which involves the entire process of the optic system, signal acquisition, and signal fusion, is constructed based on imaging effect model. Additionally, the quality evaluation index is adopted to analyze the simulation result. The experimental results demonstrate that the proposed sensor device is effective and feasible.

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In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of similarity measurement. CMSM obtains the internal information of time series from the general perspective and local trend using the cloud model, which reduces the uncertainty of measurement. The neighbor set is selected from time series by CMSM and used to construct a prediction model based on least squares support vector machine. The proposed technique reduces the potential for overfitting and uncertainty and improves model prediction quality and generalization. Experiments were performed with four datasets selected from Time Series Data Library. The experimental results show the feasibility and effectiveness of the proposed method.

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Real-time and reliable detection of conveyor belt longitudinal tear is an important task in mining operations. This paper presents a novel method of Integrative Binocular Vision Detection (IBVD) to detect the longitudinal tears of conveyor belts. Based on infrared and visible fusion, the IBVD sensor device collects the fusion images of the belt. After extracting the tear features by projection method, the progress of potential tears can be evaluated and the tears can be identified. The IBVD method is verified by an experiment platform fulfilling the acquisition, pro-processing and analysis of fusion images for tear detection. The fusion image processing time is less than 18 ms, which satisfies the requirement of real-time online monitoring. Compared to the individual measurement technique of either infrared detection or visible light detection, the average accuracy of the IBVD method reaches up to 96%, the IBVD method is more reliable in tear detection.

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Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in which the hybrid Euclidean distance is used as the similarity measurement between two sets of time series. In order to improve the efficiency, prediction performance, as well as the ability of real-time updating of the model, in this paper, the recombination samples of the model is derived by Deep Extreme Learning Machine (DELM). The experiments show that local prediction model gets accurate results in one-step and multi-step forecasting, and the model has good generalization performance through the test on the five data sets selected from Time Series Database Library (TSDL).

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Time series prediction has been extensively used for decision-making in many areas such as economics, engineering and medicine. And the useful data can be excavated by similarity measure of time series from a mass of historical data for predicting. The collected data from the real world is often uncertain, and the cloud model can be a good solution for the problem of uncertainty. This paper proposes a method based on the similarity degree of cloud model and combines it with back propagation network for prediction. In addition to the sequence itself, the trend of the sequence is used as another index of similarity. The neighbour set of query sequence from the training set is selected by similarity measure. Based on the neighbour set, a back propagation network is trained and used for prediction. Experimental results from the six time series show that the proposed method obtains better prediction accuracies than the comparative methods, which reveal its effectiveness.

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Conveyor belt longitudinal tear is one of the most serious problems in coal mining. It is very important to detect it in real-time before the length of the tear is too long. In this study, a method of longitudinal tear detection based on visible light charge coupled device (CCD) and infrared CCD is proposed. A visible light and infrared vision real-time detection system for conveyor belt longitudinal tear is designed based on this method. In this method, the infrared CCD uses adaptive histogram equalisation coordinating with Hough transform line detection. The visible light CCD, coordinating with a laser line source, uses the Hough transform algorithm and the features from accelerated segment test algorithm. These two kinds of CCDs work together to make detection results reliable. Experimental results show that the proposed method is effective and adaptive, and meets the requirements for reliable, real-time and online longitudinal tear detection. Compared to several current methods, the proposed method has a better performance on efficiency of detection.

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