A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challenging.Most multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical application.This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring.This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data.This process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode overlap.Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

Local component based principal component analysis model for multimode process monitoring

For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component based principal component analysis(LCPCA)approach for monitoring the status of a multimode process.In LCPCA,the process prior knowledge of mode division is not required and it purely based on the process data.Firstly,LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture(FGMM).Then,calculating the posterior probability is applied to determine each sample belonging to which local component.After that,the local component information(such as mean and standard deviation)is used to standardize each sample of local component.Finally,the standardized samples of each local component are combined to train PCA monitoring model.Based on the PCA monitoring model,two monitoring statistics T^(2) and SPE are used for monitoring multimode processes.Through a numerical example and the Tennessee Eastman(TE)process,the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.

A Review for Model Plant Mismatch Measures in Process Monitoring

Model is usually necessary for the design of a control loop. Due to simplification and unknown dynamics, model plant mismatch is inevitable in the control loop. In process monitoring, detection of mismatch and evaluation of its influences are demanded. In this paper several mismatch measures are presented based on different model descriptions. They are categorized into different groups from different perspectives and their potential in detection and diagnosis is evaluated. Two case studies on mixing process and distillation process demonstrate the efficacy of the framework of mismatch monitoring.

Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry

Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.

A novel multimode process monitoring method integrating LCGMM with modified LFDA

Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Integration Between Enterprise Process Monitoring and Controlling System and Enterprise Application

The relationships and the features of integration between Enterprise ProcessMonitoring and Controlling System (EPMCS) and Enterprise Process Related Applications (EPRA) wereanalyzed. An integration architecture centered on EPMCS was presented, in which there were fourlayers to connect from EPMCS to EPRA: EPMCS, application integration layer, transport layer andEPRA, and there were four layers used to etstablish integration: presentation layer, function layer,data layer and system layer. The frameworks to connect EPMCS and EPRA were designed, thatEnterprise-Independent Model (EIM), Enterprise-Specific Model (ESM) and meta-model to describe thesetwo models were defined. The method to integrate data based on XML was designed to exchange datafrom EPMCS to EPRA according to the mapping between EIM and ESM. The approches are suitable forintegrating EPMCS and systems in Product Data Management (PDM), project management and enterprisebusiness management.

A New Approach to Software Development Fusion Process Model

There are several software process models that have been proposed and are based on task involved in developing and maintaining software product. The large number of software projects not meeting their expectation in terms of functionality, cost, delivery schedule and effective project management appears to be lacking. In this paper, we present a new software fusion process model, which depicts the essential phases of a software project from initiate stage until the product is retired. Fusion is component based software process model, where each component implements a problem solving model. This approach reduces the risk associated with cost and time, as these risks will be limited to a component only and ensure the overall quality of software system by considering the changing requirements of customer, risk assessment, identification, evaluation and composition of relative concerns at each phase of development process.

Multimode Process Monitoring Based on the Density-Based Support Vector Data Description

Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

Establishment of Nanchang Honggu Tunnel health monitoring and assessment system

Based on the background of the Nanchang Honggu Tunnel,which is the largest inland river immersed tunnel to date in China,some research work on the construction of the Nanchang Honggu Tunnel health monitoring and assessment system is introduced.The platform of the health monitoring and assessment system is established with a relatively mature structure,including the sensor subsystem,data acquisition subsystem,data transmission subsystem,database subsystem,data processing and control subsystem,and health assessment and pre-warning subsystem.Monitoring index selection,sensor selection and sensor layout are fully considered in the health monitoring design and technology scheme.The fuzzy-AHP(analytic hierarchy process)evaluation method is employed to establish the Honggu Tunnel health assessment model,and the index weight determination method and grading control criterion are investigated and given.Then,the functions of the software system are achieved with advanced cloud platform technology,and the running costs of the system is reduced greatly.In summary,the establishment of the Nanchang Honggu Tunnel health monitoring and assessment system is described and such a system can realize real-time monitoring of the Honggu Tunnel and make it more convenient for implementing health assessment,pre-warning,and some other functions.Managers can operate the system using PCs and cell phones.At the same time,the relevant work on this system can provide a good reference for other immersed tunnels.

Analysis of Fusion Process Model—Case Study

Fusion Process Model is a software process model to enhance the software development process. Fusion process model have five fundamental phases and one fusion process controller to control and co-ordinate the overall development process. Fusion Process Model uses 3C-Model to generalize the process of solving the problem in each phase. 3C-Model, which helps in implementing component based development approach and provides firmer control over the software development process. Because of the component driven approach, the risk associated with cost and time is limited to component only and ensure the overall quality of software system, reduce the development cost and time by considering the changing requirements of customer, risk assessment, identification, evaluation and composition of relative concerns at each phase of development process. We have implemented Fusion Process Model to the design of a real world information system and evaluated this implementation with the initial project estimation.

Modeling of Business Processes of Project Financing

Need of transformation of means of support of project financing for commercial banks is proved.The analysis and modeling of business processes of project management by the contextual chart and the chart of decomposition is carried out that allowed to describe the main stages of project financing.With use of tools of programming the business application of project management which will promote operational assessment on selection of introduced drafts is created.

One-Sample Bayesian Predictive Analyses for a Nonhomogeneous Poisson Process with Delayed S-Shaped Intensity Function Using Non-Informative Priors

The delayed S-shaped software reliability growth model (SRGM) is one of the non-homogeneous Poisson process (NHPP) models which have been proposed for software reliability assessment. The model is distinctive because it has a mean value function that reflects the delay in failure reporting: there is a delay between failure detection and reporting time. The model captures error detection, isolation, and removal processes, thus is appropriate for software reliability analysis. Predictive analysis in software testing is useful in modifying, debugging, and determining when to terminate software development testing processes. However, Bayesian predictive analyses on the delayed S-shaped model have not been extensively explored. This paper uses the delayed S-shaped SRGM to address four issues in one-sample prediction associated with the software development testing process. Bayesian approach based on non-informative priors was used to derive explicit solutions for the four issues, and the developed methodologies were illustrated using real data.

Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

基于OPC UA的纤维缠绕机信息模型开发和应用

为实现对复合材料纤维缠绕机生产过程的监测,以缠绕机及其辅助设备为研究对象,针对实际加工设备的多样性造成的加工数据采集的异构性,基于OPC UA建立缠绕机的信息模型架构。根据OPC基金会发布的OPC UA规范和相关的行业标准,建立纤维缠绕机系统的信息模型和实例化。基于开源项目open62541,导入编译后的缠绕机的信息模型代码,自定义开发出符合使用规范的OPC UA服务器,然后利用UA客户端实现对服务器的连接,客户端能够通过服务器查询系统的加工数据,解决加工数据的异构性问题以达到监测缠绕机生产加工过程的目的;最后验证了建立的纤维缠绕机信息模型能够正确导入到开源项目open62541中,实现了加工数据监测。

产业共性技术跨组织合作研发激励模型研究

本文研究了一个由高校院所进行基础研究和制造企业负责后续商业开发的共性技术跨组织合作研发激励模型及其选择问题。本文将政府支持与合同激励放在同一框架,运用博弈论方法为制造企业设计产出导向单一激励模型、产出导向+过程监控激励模型及“政府支持+”综合激励模型以激发高校院所基础研究的积极性,同时解析了激励模型选择、影响因素及机理。研究表明:“政府支持+”综合激励模型是三种激励模型中的占优模型、产出导向+过程监控激励模型优于产出导向单一激励模型;双方收益分享系数具有促进和抑制高校院所认真履责行为的双重作用,且对其机会主义行为具有抑制作用,对制造企业后续商业开发行为及预期收益有“倒U型”影响;高校院所基础研究的影响系数在双方收益分享系数对制造企业后续商业开发努力的影响中发挥调节作用;政府补贴对高校院所和制造企业的合作研发行为具有明显的正向激励作用;惩罚系数可有效抑制高校院所的机会主义行为。

平行药物系统:基于大语言模型和三类人的框架与方法

近年来,随着物联网、大语言模型、多模态交互等新一代人工智能技术的迅猛发展,传统的药物研发、生产加工等过程面临着智能化转型的挑战。为此,以平行智能为理念,基于ACP方法和大语言模型提出了虚实交互的平行药物系统,并将三类人(数字人、机器人和生物人)的概念引入系统中,详细阐述了该系统的理论框架与构建方法。平行药物系统涵盖医药产业的全流程,药物研发阶段考虑了药物发现、实验室研究、临床试验等过程;药物生产加工阶段考虑了制药工厂运行、系统分析预测等方面;药物医疗保健阶段考虑了个性化用药咨询、增强现实用药指导、隐私安全等内容。平行药物系统打造了一个数字化的“药物空间”,旨在建立药物系统的新范式,推动智能化药物的革命。