The ice accretion on blades is a serious problem affecting the working performance of the wind turbine. Due to the non-stationary characteristic of the wind turbine, the fault information of icing can usually be buried in the normal change of wind power generation process, which makes the icing monitoring a challenging task. In this paper, a novel data reorganization for weighted wind turbine icing monitoring method is developed to handle this problem. Specifically, a novel condition driven analysis concept to replace the traditional time-driven methods is proposed for the non-stationary wind power generation process. The data can be divided into multiple wind speed slices (WSSs), i.e., condition slices, by cutting indicator variable into multiple equal intervals, where each condition slice can characterize certain characteristics for the concerned operating condition. Subsequently, by evaluating the similarity of WSSs, different wind speed modes (WSMs) are revealed by a step-wise sequential condition partition algorithm. In this way, the process characteristics can be similar within the same WSM while quite different for different WSMs. Further, the distribution of each WSM is then evaluated by Gaussian mixture model and weighted monitoring method is designed by weighting the monitoring statistics with the posterior probabilities of each monitored sample belonging to different Gaussian components. Two real data sets are used to validate the effectiveness of the proposed method. In comparison with other methods, comprehensive results illustrate that the proposed method can transform the non-stationary process into different WSMs, and closely describe the process variations, based on which the accurate icing monitoring results can be achieved.
© 2021 Elsevier Ltd

Despite rapid advances in machine learning based fault diagnosis, their identical distribution assumption of the training (source domain) and testing data (target domain) is generally challenged in industrial applications due to the variation of working conditions. In this article, a fault-prototypical adapted network (FPAN) is proposed, which enables cross-domain industrial intelligent fault diagnosis aided by deep transfer learning. First, a similarity learning-based discrimination module is designed to learn fault prototypes (FPs) that are representative for each fault and discriminative across different faults. Then, a fault prototypical-adaptation module is developed, which adapts the multiple FPs to the target dataset and enables more precise category-wise domain invariance. The two modules are trained simultaneously to extract transferrable and discriminative FPs, by which the cross-domain intelligent diagnosis can be readily achieved. Experimental results on two industrial cases illustrate that the proposed approach learns transferable feature representations that better reduce domain discrepancy, and provides improved diagnosis performance on target data.

Deep-learning-based soft sensors have been extensively developed for predicting key quality or performance variables in industrial processes. However, most approaches assume that data are uniformly sampled while the multiple variables are often acquired at different rates in practical processes. This article designed a progressive transfer strategy, based on which a variational progressive-transfer network (VPTN) method is proposed for the soft sensor development of industrial multirate processes. In VPTN, the multirate data are first separated into multiple data chunks where the variables within each chunk are acquired at a uniform rate. Then, a variational multichunk data modeling framework is developed to model the multiple chunks in a unified fashion through deep variational structures. The base models, including the unsupervised ones with only partial process variables and the supervised soft sensor model share a similar network structure, such that the subsequent transfer strategy can be readily implemented. Finally, a progressive transfer learning strategy is designed to transfer the model parameters from the fastest sampled data chunk to the slowest one in a progressive manner. Thus, the knowledge from various data chunks can be sequentially explored and transferred to enhance the performance of the terminal soft sensor model. Case studies on both a debutanizer column dataset and a real coal mill dataset in a thermal power plant validate the performance of the proposed method.

Soft sensors have been extensively developed and applied in the process industry. One of the main challenges of the data-driven soft sensors is the lack of labeled data and the need to absorb the knowledge from a related source operating condition to enhance the soft sensing performance on the target application. This article introduces deep transfer learning to soft sensor modeling and proposes a deep probabilistic transfer regression (DPTR) framework. In DPTR, a deep generative regression model is first developed to learn Gaussian latent feature representations and model the regression relationship under the stochastic gradient variational Bayes framework. Then, a probabilistic latent space transfer strategy is designed to reduce the discrepancy between the source and target latent features such that the knowledge from the source data can be explored and transferred to enhance the target soft sensor performance. Besides, considering the missing values in the process data in the target operating condition, the DPTR is further extended to handle the missing data problem utilizing the strong generation and reconstruction capability of the deep generative model. The effectiveness of the proposed method is validated through an industrial multiphase flow process.

Missing data is quite common in the industrial field, resulting in problems in downstream applications, as most data driven methods used in these applications rely on complete and high-quality dataset to build a high-quality model. Existing methods deal with missing data individually regardless of its downstream application, treating all variables equally without considering their different roles in the downstream application. This would affect imputation performance for key variables, thus deteriorating the accuracy of the downstream model. A considerable challenge is how to refine the missing data imputation task. In this paper, a new method termed fine-tuned imputation GAN (FIGAN) is designed to achieve customized data imputation for industrial soft sensor. The major contribution of the paper lies in two aspects: 1) different from the original imputation GAN (GAIN) which treats all variables equally, FIGAN is guided by a soft sensor module so as to achieve customized data imputation by performing improved data imputation on quality-related variables. Enhanced accuracy for the final industrial soft sensor would be possible; 2) in addition, since labels of the soft sensor might also have missing data, a soft sensor with pseudo labeling is designed to conquer the problem with data imputation and label prediction being optimized interactively. Case studies on a converter steelmaking process and a penicillin fermentation process show the feasibility of the proposed FIGAN. It is noted that such customized imputation could be readily transferred to other downstream applications with missing data.

Process dynamic behaviors resulting from closed-loop control and the inherence of processes are ubiquitous in industrial processes and bring a considerable challenge for process monitoring. Many methods have been developed for dynamic process monitoring, of which the dynamic latent variables (DLV) model is one of the most practical and promising branches. This paper provides a timely retrospective study of typical methods to fill the void in the systematic analysis of DLV methods for dynamic process monitoring. First, several classical DLV methods are briefly reviewed from three aspects, including original ideas, the determination of parameters, and offline statistics design. Second, a discussion on the relationships of the discussed methods has been established to make a clear understanding of process dynamics explained by each method. Third, five cases of a three-phase flow process are provided to illustrate the effectiveness of the methods from the application viewpoint. Finally, future research directions on dynamic process monitoring have also been provided. The primary objective of this paper is to summarize the prevalent DLV methods for dynamic process monitoring and thus highlight a valuable reference for further improvement on DLV models and the selection of algorithms in practical applications. (C) 2021 Elsevier Ltd. All rights reserved.

Unsupervised cross-domain fault diagnosis has been actively researched in recent years. It learns transferable features that reduce distribution inconsistency between source and target domains without target supervision. Most of the existing cross-domain fault diagnosis approaches are developed based on the consistency assumption of the source and target fault category sets. This assumption, however, is generally challenged in practice, as different working conditions can have different fault category sets. To solve the fault diagnosis problem under both domain and category inconsistencies, a multisource-refined transfer network is proposed in this article. First, a multisource-domain-refined adversarial adaptation strategy is designed to reduce the refined categorywise distribution inconsistency within each source-target domain pair. It avoids the negative transfer trap caused by conventional global-domainwise-forced alignments. Then, a multiple classifier complementation module is developed by complementing and transferring the source classifiers to the target domain to leverage different diagnostic knowledge existing in various sources. Different classifiers are complemented by the similarity scores produced by the adaptation module, and the complemented smooth predictions are used to guide the refined adaptation. Thus, the refined adversarial adaptation and the classifier complementation can benefit from each other in the training stage, yielding target-faults-discriminative and domain-refined-indistinguishable feature representations. Extensive experiments on two cases demonstrate the superiority of the proposed method when domain and category inconsistencies coexist.

When training samples are scarce, the semantic embedding technique, i. e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, i. e., zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning; e. g., we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available.(1)

迭代学习模型预测控制（Iterative learning model predictive control, ILMPC）具备较强的批次学习能力及突出的时域跟踪性能,在批次过程控制中发挥了重要作用.然而对于具有强非线性的快动态批次过程,传统的迭代学习模型预测控制很难实现计算效率与跟踪精度之间的平衡,这给其应用带来了挑战.对此本文提出一种高效迭代学习预测函数控制策略,将原非线性系统沿参考轨迹线性化得到二维跟踪误差预测模型,并在控制器设计中补偿所产生的线性化误差,构造优化目标函数为真实跟踪误差的上界.为加强优化计算效率,在时域上结合预测函数控制以降低待优化变量维数,从而有效降低计算负担.结合终端约束集理论,分析了迭代学习预测函数控制的时域稳定性及迭代收敛性.通过对无人车和典型快速间歇反应器的仿真实验验证所提出算法的有效性.

近年来，建模传感器之间的空间依赖性已经成为实现准确交通流量预测的关键步骤。然而，交通流状态的动态演化对空间依赖性的捕捉造成挑战。具体而言，受各种外部因素的影响，交通流量随着时间的推移会在不同的状态之间动态演化和转换，并可能处于某些重叠状态，从而导致时空依赖性的不确定性。为此，提出了一种时空依赖性自适应模糊嵌入的方法，该方法不通过确定性的状态，而是通过包含多个模糊状态的隶属度向量来刻画交通流量，从而充分表征交通流量的状态演化以及潜在的重叠状态。具体来说，为交通流量构建多个模糊状态，并生成相应的隶属度值来描述交通流量隶属于各种模糊状态的程度，从而可以通过隶属度向量来有效刻画交通流量的动态演化和可能存在的重叠状态，进而准确地感知空间依赖性。此外，为了防止模糊嵌入数据出现模式坍塌问题，设计了一种状态多样性约束来保证所有模糊状态之间的差异性，以提升其表征能力。最后，设计了一种全局-局部时间平滑性约束，以促进模糊状态的演化连续性。在真实公开的交通流量数据集中，所提出的模糊嵌入方法展现出更佳的预测性能，具有较强的工程实践意义。