Journal of Process Control最新文献_第8页

Advances in modeling and control of nonlinear distributed parameter systems and their applications: A review 非线性分布参数系统的建模与控制及其应用研究进展

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-20 DOI: 10.1016/j.jprocont.2025.103549

Bowen Xu , Weiqi Yang , Xinjiang Lu , Yunxu Bai , Yajun Wang

Numerous processes in various fields including engineering, physics, and chemistry, etc., belong to distributed parameter systems (DPSs). These systems are strongly spatiotemporal coupled, possessing complex time-varying dynamics and infinite-dimensional spatial distribution characteristics. Additionally, there are unknown initial/ boundary conditions and parameter variation during the interaction of information or energy exchange, especially in complex application scenarios (i.e., large operation range, large spatial region, etc.). These factors make the modeling, prediction and control of spatiotemporal dynamics extremely difficult and challenging. With the enrichment of computational resources and data-driven/ intelligent methods, many new frameworks and strategies are designed and applied for nonlinear DPSs, which promotes the research diversity and maturity of DPS theory. Meanwhile, the development also gives rise to new problems. From the perspective of review, this paper starts from the practical modeling and control problems in combination with several application cases of nonlinear DPSs, and summarizes the research and application progress, including traditional methods, data-driven methods, intelligent modeling methods etc., and looks forward to the future development trends, providing guidance for related research and practical problem-solving of nonlinear DPSs.

工程、物理、化学等领域的许多过程都属于分布式参数系统（dps）。这些系统具有强烈的时空耦合性，具有复杂的时变动力学和无限维空间分布特征。此外，在信息或能量交换的交互过程中，存在未知的初始/边界条件和参数变化，特别是在复杂的应用场景中（如大操作范围、大空间区域等）。这些因素使得时空动态的建模、预测和控制变得极其困难和具有挑战性。随着计算资源的丰富和数据驱动/智能方法的发展，许多新的DPS框架和策略被设计和应用，促进了DPS理论研究的多样性和成熟度。与此同时，发展也带来了新的问题。本文从综述的角度出发，结合非线性dps的几个应用案例，从实际建模和控制问题出发，总结了包括传统方法、数据驱动方法、智能建模方法等方面的研究和应用进展，并展望了未来的发展趋势，为非线性dps的相关研究和实际解决提供指导。

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引用次数: 0

Maximum extrem biogas yield prediction based tracking control for two-stage anaerobic digestion using CKF robust observer feedback 基于CKF鲁棒观测器反馈的两级厌氧消化最大极值沼气产量预测跟踪控制

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-19 DOI: 10.1016/j.jprocont.2025.103558

Hongxuan Li , Haoping Wang , Yang Tian , Nicolai Christov

Two-stage anaerobic digestion process, recognized as a promising microbiological technology, can effectively converts organic pollutants into renewable energy gases. However, practical implementation faces two fundamental challenges: the critical process states (for example, concentrations of anaerobic microorganisms) are not directly measurable through conventional sensors, and the gas production efficiency remains suboptimal under current operational paradigms. To address these challenges, this study proposed a robust observer-based biogas yield extremum prediction tracking controller (RO-EPTC). The proposed RO-EPTC controller integrates a cubature Kalman filter robust observer and an artificial neural network-based prediction tracking controller. The RO-EPTC enables dynamic extremum prediction of biogas yield while ensuring real-time convergence of actual gas production to the identified optimal trajectory. Additionally, the proposed scheme provides accurate estimation of unmeasurable system states. Finally, through simulation comparison experiments, the effects of proposed RO-EPTC method were verified.

两级厌氧消化技术可以有效地将有机污染物转化为可再生能源气体，是一种很有前途的微生物技术。然而，实际实施面临两个基本挑战：关键过程状态（例如，厌氧微生物的浓度）不能通过传统传感器直接测量，并且在当前的操作范式下，产气效率仍然不是最佳的。为了解决这些挑战，本研究提出了一种鲁棒的基于观测器的沼气产量极值预测跟踪控制器（RO-EPTC）。提出的RO-EPTC控制器集成了培养卡尔曼滤波鲁棒观测器和基于人工神经网络的预测跟踪控制器。RO-EPTC能够对生物气产量进行动态极值预测，同时确保将实际产气量实时收敛到确定的最佳轨迹。此外，该方案提供了对不可测系统状态的准确估计。最后，通过仿真对比实验，验证了所提RO-EPTC方法的效果。

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引用次数: 0

Output consensus for interconnected systems via the internal model principle and a model predictive control based strategy 基于内模原理和模型预测控制策略的互联系统输出一致性

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-18 DOI: 10.1016/j.jprocont.2025.103551

Ye Zhang , Fei Li , Dongya Zhao , Xing-Gang Yan , Sarah K. Spurgeon

Interconnected systems are commonly found in process networks. In this paper, an output consensus framework is proposed for a class of continuous interconnected linear heterogeneous systems subject to constraints. A distributed output consensus control strategy is developed by combining the internal model principle (IMP) with model predictive control (MPC). A distributed iterative algorithm is designed to solve the IMP conditions for interconnected systems. The IMP based control plays two main roles: On the one hand, it helps to deal with the interconnection effects existing between the subsystems; on the other hand, it drives the subsystems to track the reference dynamics in order to achieve output consensus. The MPC determines an optimized control gain while being able to handle constraints. Simulation examples and experimental trials are presented to validate the effectiveness and superiority of the proposed method.

相互连接的系统通常出现在过程网络中。本文提出了一类具有约束的连续互联线性异构系统的输出一致性框架。将内模原理（IMP）与模型预测控制（MPC）相结合，提出了一种分布式输出一致性控制策略。设计了一种分布式迭代算法来求解互联系统的IMP条件。基于IMP的控制主要有两个作用：一方面，它有助于处理子系统之间存在的互连效应；另一方面，它驱动子系统跟踪参考动态，以达到输出一致性。MPC在能够处理约束条件的同时确定了优化的控制增益。通过仿真算例和实验验证了该方法的有效性和优越性。

引用次数: 0

Deep learning-based model predictive control with exponential weighting strategy and its application in energy management systems 基于深度学习的指数加权模型预测控制及其在能源管理系统中的应用

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-17 DOI: 10.1016/j.jprocont.2025.103542

Dan Cui , Yanfang Mo , Xiaofeng Yuan , Lingjian Ye , Kai Wang , Feifan Shen , Yalin Wang , Chunhua Yang , Weihua Gui

Building energy management plays an important role in improving the overall system efficiency and reducing energy consumption. To achieve this goal, it is significant and challenging for the optimization of energy consumption and the utilization of renewable energy sources. This work presents a deep learning-based model predictive control with exponential weighting (DLEMPC) strategy to control and optimize Energy Management Systems (EMS). First, an exponential weighting technique with decreasing characteristic is introduced to the cost function over the timeslots in the receding horizon of the MPC to improve the control performance of the system, which aims to obtain the control actions by paying more importance on recent timeslots in the finite time-horizon. Second, a controller based on the deep belief network (DBN) model is proposed to reduce computational complexity of the rolling horizon optimization in practical applications. The deep learning controller is obtained by training it with a large number of input and output data pairs that are generated from a well-defined MPC designed with the new cost function. Finally, the DLEMPC strategy is used to control and optimize an EMS, connected to a grid, battery, HVAC, and solar panel. The results demonstrate that DLEMPC strategy can significantly improve the energy efficiency of buildings and reduce energy consumption compared to the traditional MPC formula.

建筑能源管理对提高系统整体效率、降低能耗具有重要作用。实现这一目标，对能源消耗的优化和可再生能源的利用具有重要的意义和挑战性。本文提出了一种基于深度学习的指数加权模型预测控制（DLEMPC）策略来控制和优化能源管理系统。首先，为了提高系统的控制性能，在MPC的后退水平时隙的代价函数中引入了具有递减特征的指数加权技术，其目的是在有限的时间范围内更重视最近时隙的控制动作。其次，提出了一种基于深度信念网络（DBN）模型的控制器，以降低实际应用中滚动地平线优化的计算复杂度。深度学习控制器是通过训练大量的输入输出数据对得到的，这些数据对是由一个定义良好的MPC生成的，该MPC设计了新的成本函数。最后，将该策略用于控制和优化与电网、电池、暖通空调和太阳能电池板相连的EMS。结果表明，与传统的MPC公式相比，DLEMPC策略可以显著提高建筑的能源效率，降低能耗。

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引用次数: 0

Excitation-free closed-loop identification based on adaptive hysteresis loop width adjustment strategy 基于自适应磁滞环宽度调整策略的无激励闭环辨识

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-13 DOI: 10.1016/j.jprocont.2025.103552

Chonggao Hu , Ridong Zhang , Furong Gao

Aiming at the problem that the traditional system identification methods are not adaptive enough when the system model parameters change significantly, this paper proposes an excitation-free closed-loop identification method based on an adaptive hysteresis loop width adjustment (AHLWA) strategy. Firstly, the AHLWA strategy is proposed according to the direction of change of the mean value of the power spectrum (MVPS) of the input signal, which can respond to the trend of the system's dynamic characteristics and dynamically adjust the hysteresis loop width parameters in real time. Secondly, an excitation-free closed-loop identification method based on the AHLWA strategy was developed by integrating the AHLWA strategy with the prediction error method. In addition, to accurately quantify the model error and detect model parameter variations, an improved model error detection method is proposed to quantify the model error by using the unexcited closed-loop identification technique. The numerical example simulation results indicate that the MVPS of the proposed identification method increases from 0.01 to 0.25 compared to the relay feedback identification method, which ensures the continuous excitation of the input signals and significantly improves the identification accuracy when the system model parameters change significantly. Meanwhile, the proposed identification method is further validated by applying it to the temperature control system of industrial coking furnaces. In addition, the proposed identification method can update the benchmark model on time, which makes the system model error significantly lower than 30%, providing an effective solution for model error detection in industrial closed-loop systems.

针对传统系统辨识方法在系统模型参数发生显著变化时适应性不足的问题，提出了一种基于自适应滞回环宽度调整（AHLWA）策略的无激励闭环辨识方法。首先，根据输入信号功率谱均值（MVPS）的变化方向，提出了AHLWA策略，该策略能够实时响应系统动态特性的变化趋势，动态调整滞回环宽度参数；其次，将AHLWA策略与预测误差法相结合，提出了一种基于AHLWA策略的无激励闭环辨识方法；此外，为了准确量化模型误差和检测模型参数变化，提出了一种改进的模型误差检测方法，利用非激励闭环辨识技术对模型误差进行量化。数值算例仿真结果表明，与继电器反馈辨识方法相比，所提辨识方法的MVPS从0.01提高到0.25，保证了输入信号的持续激励，在系统模型参数发生显著变化时显著提高了辨识精度。同时，将该辨识方法应用于工业焦化炉温度控制系统，进一步验证了辨识方法的有效性。此外，所提出的识别方法能够及时更新基准模型，使系统模型误差显著低于30%，为工业闭环系统的模型误差检测提供了有效的解决方案。

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引用次数: 0

A cross-layer cooperative optimization framework for optimal scheduling of multi-grade PET fiber production 多级聚酯纤维生产优化调度的跨层协同优化框架

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-11 DOI: 10.1016/j.jprocont.2025.103540

Jiale Zhang, Wenli Du, Xin Dai

The fluctuations in the supply chain market of polyethylene terephthalate (PET) fibers have been intensifying in recent years. Existing research on the production scheduling of PET plants is usually based on the assumption of a stationary supply chain market. However, these works ignore supply chain fluctuations and market competition, and the schedule obtained may become sub-optimal or infeasible in the real market. This paper considers using the game to represent the competition and cooperation relationships in the market among enterprises with limited supply capacity to obtain equilibrium supplies. Meanwhile, changes in the market prices will cause changes in the equilibrium supplies of the game. In addition, price prediction and supply decisions support the production schedule to achieve high economic efficiency. Therefore, we propose a cross-layer cooperative optimization framework between the supply chain layer and production chain layer for production scheduling optimization. In the supply chain layer, price trends are predicted by synchronous spatio-temporal relationship network, and equilibrium supplies are obtained through a multi-firm multi-product game. In the production chain layer, a production scheduling optimization model that integrates predicted prices and equilibrium supplies from the supply chain layer is established. The effectiveness of the proposed method is verified on a real-world PET plant.

近年来，聚对苯二甲酸乙二醇酯（PET）纤维供应链市场的波动不断加剧。现有的PET工厂生产调度研究通常是基于固定供应链市场的假设。然而，这些工作忽略了供应链的波动和市场竞争，得到的计划在实际市场中可能是次优的或不可行的。本文考虑用博弈来表示供应能力有限的企业之间为获得均衡供给的竞争与合作关系。同时，市场价格的变化会引起博弈均衡供给的变化。此外，价格预测和供应决策支持生产计划，以实现较高的经济效益。为此，我们提出了供应链层与生产链层之间的跨层协同优化框架，用于生产调度优化。在供应链层，通过同步时空关系网络预测价格趋势，并通过多企业多产品博弈获得均衡供给。在生产链层，建立了整合供应链层预测价格和均衡供给的生产调度优化模型。在实际的PET装置上验证了该方法的有效性。

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引用次数: 0

Novel observer-based fault-tolerant tracking control of input-constrained polymerization reactor with statistical analysis 基于观测器的统计分析输入约束聚合反应器容错跟踪控制

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-10 DOI: 10.1016/j.jprocont.2025.103541

Zahra Ahangari Sisi , Mehdi Mirzaei , Sadra Rafatnia , Somayeh Jamshidi , Maryam Farbodi

The polymerization reaction within a continuous stirred tank reactor is modeled as a multivariable, nonlinear control process with input constraints. This study proposes a novel optimization-based approach for fault diagnosis and compensation, despite the uncertainties and disturbances present in the dynamic model of the polymerization reactor. This approach facilitates the design of a reliable model-based controller through the estimation of system perturbations. The proposed strategy mitigates external disturbances, time-varying uncertainties, and faults by incorporating complementary terms, calculated in real-time from output measurements, into the initial process model. To ensure robust performance of the fault detection mechanism, the threshold bounds for external disturbances and other uncertainties are determined stochastically using the Monte Carlo simulation approach. A continuous predictive controller is designed in closed form based on the updated reactor model, accounting for the presence of control input limitations. The constrained controller is formulated by solving an optimization problem using the Karush–Kuhn–Tucker (KKT) conditions. The boundedness of the tracking errors is established under the constrained multivariable controller. The results demonstrate that the proposed method exhibits high sensitivity, accuracy, and robustness in fault detection and isolation for a nonlinear uncertain reactor. Simulations confirm the superior performance of the proposed observer-based fault-tolerant control system over existing passive and active actuator fault-tolerant control methods.

将连续搅拌釜反应器内的聚合反应建模为具有输入约束的多变量非线性控制过程。本研究提出了一种新的基于优化的故障诊断和补偿方法，尽管聚合反应器的动态模型中存在不确定性和干扰。该方法通过对系统扰动的估计，便于设计可靠的基于模型的控制器。该策略通过将从输出测量实时计算的互补项纳入初始过程模型，减轻了外部干扰、时变不确定性和故障。为了保证故障检测机制的鲁棒性，采用蒙特卡罗模拟方法随机确定外部干扰和其他不确定性的阈值边界。考虑到控制输入的限制，在更新的反应器模型的基础上，以封闭形式设计了连续预测控制器。利用Karush-Kuhn-Tucker （KKT）条件求解优化问题，建立了约束控制器。在约束多变量控制下，建立了跟踪误差的有界性。结果表明，该方法对非线性不确定电抗器的故障检测和隔离具有较高的灵敏度、准确性和鲁棒性。仿真结果表明，基于观测器的容错控制系统优于现有的被动和主动执行器容错控制方法。

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引用次数: 0

Observer based dual-layer sliding mode fault-tolerant control for dissolved oxygen concentration in wastewater treatment process 基于观测器的废水处理过程溶解氧浓度双层滑模容错控制

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-09 DOI: 10.1016/j.jprocont.2025.103538

Hongyan Yang, Qi Zou, Honggui Han

Fault-tolerant control (FTC) of dissolved oxygen concentration is the core technology to ensure the robustness of wastewater treatment process (WWTP). However, the dynamic characteristics of microbial community are difficult to be modeled accurately, and external disturbances such as fluctuations in influent water quality and equipment failures further increase the control difficulty. Therefore, how to effectively compensate for the unmodeled dynamics and improve the system robustness is still a key problem to be solved in the field of WWTP control. In order to address this problem, this paper proposes an FTC method for dissolved oxygen concentration that integrates a dual sliding mode observation mechanism and an intelligent optimization strategy. Firstly, a state observer with an adaptive compensation mechanism is constructed based on the sliding mode control (SMC) method to realize the simultaneous estimation of dissolved oxygen concentration and unmodeled dynamics. Secondly, an adaptive robust fault-tolerant controller is designed by combining the Lyapunov stability theory. Then, a double sliding mode surface containing observation error and control error is established. Thirdly, a differential evolutionary algorithm is introduced to perform a global optimization of the robust gain parameters, which transforms the complex robustness problem into an optimal gain solving problem. Simulation experiments are conducted to compare the fault-tolerant control effect of different control methods with the proposed method, and the results verify the superiority of the method proposed in this paper.

溶解氧浓度容错控制是保证污水处理过程鲁棒性的核心技术。然而，微生物群落的动态特征难以准确建模，而进水水质波动和设备故障等外部干扰进一步增加了控制难度。因此，如何有效地补偿未建模的动力学，提高系统的鲁棒性仍然是污水处理控制领域需要解决的关键问题。为了解决这一问题，本文提出了一种溶解氧浓度的FTC方法，该方法集成了双滑模观测机制和智能优化策略。首先，基于滑模控制（SMC）方法构造了带自适应补偿机制的状态观测器，实现了溶解氧浓度和未建模动力学的同步估计；其次，结合李雅普诺夫稳定性理论，设计了自适应鲁棒容错控制器。然后，建立了包含观测误差和控制误差的双滑模曲面。第三，引入差分进化算法对鲁棒增益参数进行全局优化，将复杂的鲁棒性问题转化为最优增益求解问题。通过仿真实验比较了不同控制方法与本文方法的容错控制效果，结果验证了本文方法的优越性。

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引用次数: 0

Align knowledge with time-series: Cross-modal domain knowledge activation for LLM-enabled zero-shot fault diagnosis 将知识与时间序列对齐：跨模态领域知识激活用于llm支持的零故障诊断

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-09 DOI: 10.1016/j.jprocont.2025.103534

Jiancheng Zhao , Chunhui Zhao , Jiaqi Yue

The existing zero-shot fault diagnosis methods identify unseen categories that have no training samples by predicting fault attributes from samples. Although these methods alleviated the data scarcity issue, they raised a new challenge, i.e., professional fault attributes annotation. We recognize that the essence of fault attributes lies in describing the connections and differences between categories. Therefore, we propose a novel LLM-enabled zero-shot fault diagnosis paradigm, and the large language models (LLMs) fine-tuned based on domain-specific knowledge can capture similar information to replace manual annotation. It unlocks the potential of LLMs to handle zero-shot tasks related to industrial time-series data in a cross-modal manner. It aims to address the burden of semantic knowledge annotation posed by the existing attribute-enabled paradigm. Moreover, the domain shift problem (DSP) arising from the shortage of training samples for unseen faults is also tackled by leveraging the cross-modal activation of relevant knowledge that has been learned from domain-specific documents. Firstly, to address the issue that LLMs pretrained on general knowledge are lacking in the knowledge of the industrial field, we design prompts for industrial faults and fine-tune the LLM with domain knowledge from diagnosis reports. Subsequently, considering that LLMs lack the ability to process time-series data, we design a cross-modal transformation module to align the time-series modality with the text modality. Moreover, we propose a knowledge distillation strategy to further align these two modalities, so the unseen fault text descriptions can serve as substitutes for the unavailable samples to address the DSP. We conduct experiments on a real thermal power plant, and the proposed method achieves an average improvement of 9.83% in terms of the diagnosis accuracy of unseen faults.

现有的零射击故障诊断方法通过预测样本的故障属性来识别没有训练样本的未知类别。这些方法虽然缓解了数据稀缺性问题，但也提出了新的挑战，即专业的故障属性标注。我们认识到故障属性的本质在于描述类别之间的联系和差异。因此，我们提出了一种新的基于llm的零故障诊断范式，并且基于特定领域知识进行微调的大语言模型（llm）可以捕获类似的信息，以取代人工注释。它释放了llm以跨模态方式处理与工业时间序列数据相关的零射击任务的潜力。它旨在解决现有属性支持范式给语义知识标注带来的负担。此外，通过利用从特定领域文档中学习到的相关知识的跨模态激活，还解决了由于未见故障的训练样本不足而引起的领域转移问题。首先，为了解决一般知识预训练的LLM缺乏工业领域知识的问题，我们设计了工业故障提示，并利用诊断报告中的领域知识对LLM进行微调。随后，考虑到llm缺乏处理时间序列数据的能力，我们设计了一个跨模态转换模块，将时间序列模态与文本模态对齐。此外，我们提出了一种知识蒸馏策略来进一步协调这两种模式，因此看不见的故障文本描述可以替代不可用的样本来解决DSP问题。在实际火电厂进行了实验，该方法对未见故障的诊断准确率平均提高了9.83%。

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引用次数: 0

Operational zone-specific univariate alarm design for incipient faults 针对早期故障的特定操作区域单变量报警设计

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-09-06 DOI: 10.1016/j.jprocont.2025.103536

Mohsen Asaadi , Fan Yang , Weichi Wu

Alarm systems are essential components of industrial process monitoring, supporting both safety and operational efficiency by detecting deviations from normal conditions. Traditional alarm design methods often assume stationary, which limits their ability to reflect the evolving nature of incipient faults. These faults develop gradually and, if not properly addressed, can lead to critical failures. Timely and accurate detection is therefore vital to minimize false alarms, reduce missed detections, and improve response effectiveness. This study proposes a time-variant statistical modeling framework to characterize the behavior of process variables affected by incipient faults. A new alarm system design methodology is introduced, guided by three key performance indices: Missed Alarm Rate (MAR), False Alarm Rate (FAR), and Average Alarm Delay (AAD). The methodology uses the Narrowest Over Threshold change-point detection technique to segment the process into distinct operational zones, including the Normal Operating Zone (NOZ), Rising Zone (RZ), Fault Zone (FZ), and Return to Normal (RTN). By employing a piecewise time-variant model, the alarm system’s performance is assessed in a manner that captures local trends and transitions. The resulting indices are dynamic, offering a more detailed projection of the process variable’s behavior over time. In particular, the AAD metric reflects realistic delay patterns and avoids the misleading interpretations often associated with stationary models. The proposed method is validated through Monte Carlo simulations and demonstrated using the Tennessee Eastman Process benchmark. Results show that the time-variant model provides a more accurate and interpretable representation of process dynamics and alarm behavior than traditional approaches.

报警系统是工业过程监控的重要组成部分，通过检测正常情况的偏差来支持安全性和操作效率。传统的报警设计方法通常假设是平稳的，这限制了它们反映早期故障演变性质的能力。这些故障逐渐发展，如果处理不当，可能导致严重故障。因此，及时和准确的检测对于最大限度地减少误报、减少漏检和提高响应效率至关重要。本研究提出了一个时变统计建模框架，以表征受早期故障影响的过程变量的行为。介绍了一种新的报警系统设计方法，该方法以漏报率（MAR）、误报率（FAR）和平均报警延迟（AAD）三个关键性能指标为指导。该方法使用最窄阈值变更点检测技术将流程划分为不同的操作区域，包括正常操作区域（NOZ）、上升区域（RZ）、故障区域（FZ）和恢复正常（RTN）。通过采用分段时变模型，以捕获局部趋势和转变的方式评估报警系统的性能。所得到的指数是动态的，提供了过程变量随时间变化的更详细的预测。特别是，AAD度量反映了实际的延迟模式，并避免了通常与平稳模型相关的误导性解释。通过蒙特卡罗模拟验证了所提出的方法，并使用田纳西伊士曼过程基准进行了验证。结果表明，与传统方法相比，时变模型提供了更准确和可解释的过程动态和报警行为表示。

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引用次数: 0