주성분분석법과 그레인저 인과관계를 이용한 실시간 공정 모니터링 및 이상 전파 경로 계산

학위논문 (박사)-- 서울대학교 대학원 공과대학 화학생물공학부, 2017. 8. 한종훈.Modern industrial process is a complex device industry consisting of a combination of numerous unit processes. Numerous process parameters such as flow rate, temperature, pressure, concentration and composition have strong linear or nonlinear correlation. Since improvement of computing power and process control systems in industrial processes, several board operator and field operator can manage huge amounts of data and whole process information from industrial plant. However, the number of processes and devices to be handled by a single operator will increase, and operators meets a limitation of cognitive ability due to flood of information, causing problems such as process malfunction or instrumental failure. To solve this problem, we propose a PCA modeling procedures that aims to improve monitoring performance by variable selection, removing noise, operation mode classification and mode change detection. Fault diagnosis and causal analysis is also introduced. We calculated the causal relationship matrix between the process variables and find out the root cause of the unexpected process changes. The proposed approach was applied and validated to LNG plant located in Incheon and plasma condition monitoring in plasma etcher. Chapter 2 discusses the application methodologies of signal processing to eliminate noises from OES signal and multivariate statistical techniques to improve monitoring sensitivity. Among the plasma sensors, optical emission spectroscopy (OES) has been widely utilized and its high dimensionality has required multivariate analysis (MVA) techniques such as principal component analysis (PCA). PCA, however, might devaluate physical meaning of target process during its statistical calculation. In addition, inherent noise from charge coupled devices (CCD) array in OES might deteriorate PCA model performance. Therefore, it is desirable to pre-select physically important variables and to filter out noisy signals before modeling OES based plasma data. For these purposes, this chapter introduces a peak wavelength selection algorithm for selecting physically meaningful wavelength in plasma and discrete wavelet transform (DWT) for filtering out noisy signals from a CCD array. The effectiveness of the PCA model introduced in this paper is verified by comparing fault detection capabilities of conventional PCA model under the various source power or pressure faulty situations in a capacitively coupled plasma etcher. The PCA model introduced in this chapter successively detect even extremely small variation such as 0.67% of source power change even though the conventional PCA model fails to detect all of the faulty situations under the tests. Chapter 3 discusses the application methodology of operation mode identification and multimode PCA to improve the performance of LNG mixed refrigeration (MR) process and prevent process shutdown. LNG MR process is usually used for liquefying natural gas. The compressors for refrigerant compression are operated with the high-speed rotating parts to create a high-pressure. However, any malfunction in the compressors can lead to significant process downtime, catastrophic damage to equipment and potential safety consequences. The existing methodology assumes that the process has a single mode of operation, which makes it difficult to distinguish between a malfunction of the process and a change in mode of operation. Therefore, k-nearest neighbor algorithm (k-NN) is employed to classify the operation modes, which is integrated into multi-mode principal component analysis (MPCA) for process monitoring and fault detection. When the fault detection performance is evaluated with real LNG MR operation data, the proposed methodology shows more accurate and early detection capability than conventional PCA. Chapter 4 discusses PCA based fault amplification algorithm to detect both the root cause of fault and the fault propagation path in the system. The developed algorithm project the samples on the residual subspace (RS) to determine the disturbance propagation path. Usually, the RS of the fault data is superimposed with the normal process variations which should be minimized to amplify the fault magnitude. The RS containing amplified fault is then converted into the co-variance matrix followed by singular value decomposition (SVD) analysis which in turn generates the fault direction matrix corresponding to the largest eigenvalue. The fault variables are then re-arranged according to their magnitude of contribution towards a fault which in turn represents the fault propagation path using an absolute descending order functions. Moreover, the multivariate granger causality (MVGC) algorithm is used to analyze the causal relationship among the variables obtained from the developed algorithm. Both the methodologies are tested on the LNG fractionation process train and distillation column operation where some fault case scenarios are assumed to estimate the fault directions. It is observed that the hierarchy of variables obtained from fault propagation path algorithm are in good agreement with the MVGC algorithm. Therefore, fault amplification methodology can be used in industrial systems for identifying the root cause of fault as well as the fault propagation path. The application results show that the proposed multivariate statistical method can improve productivity and safety by providing useful information for process monitoring and fault diagnosis in various processes with distributed control system.CHAPTER 1 Introduction 1 1.1 Research motivation 1 1.2 Research objectives 4 1.3 Outline of the thesis 5 CHAPTER 2 : Multivariate monitoring, variable selection and OES signal filter design of plasma process 6 2.1 Introduction 6 2.2 Issues in PCA Modeling of OES based Plasma Data 8 2.3 Theoretical Background 11 2.3.1 Peak Wavelength Selection Algorithm 11 2.3.2 Discrete Wavelet Transform 14 2.4 Experimental Set-up 19 2.5 Results and Discussion 21 2.5.1 Pre-selected variables in OES data 21 2.5.2 Decomposition of OES signal by DWT 23 2.5.3 Comparison of Fault Detection Performance in OES based PCA Models 25 2.6 Conclusion 35 CHAPTER 3 : Multimode PCA and k-nearest neighbor algorithm for LNG mixed refrigeration process monitoring 36 3.1 Introduction 36 3.2 Target process and data description 38 3.3 Theoretical Background 45 3.3.1 Principal component analysis based fault detection 45 3.3.2 k-Nearest Neighbor classifier 48 3.4 Mode identification and fault detection 49 3.4.1 Operation mode identification and fault detection 49 3.5 Results and Conclusion 55 3.5.1 Consideration in LNG MR process monitoring 55 3.5.2 Global and local PCA modeling 59 3.5.3 Detection of operation mode 61 3.5.4 Comparison of fault detection performance 66 3.6 Conclusion 70 CHAPTER 4 : Estimation of disturbance propagation path using PCA and multivariate Granger Causality 71 4.1 Introduction 71 4.2 Theoretical Background 77 4.2.1 Fault propagation path detection 77 4.2.2 Causal analysis based on Granger Causality (GC) 82 4.3 Application to the Liquefied Natural Gas (LNG) Process 87 4.3.1 Process Description 87 4.3.2 Development of fault case scenarios 90 4.4 Conclusion 116 CHAPTER 5 Concluding Remarks 118 Nomenclature and Abbreviations 121 Literature cited 122 Abstract in Korean (요 약) 133Docto

Deep Learning for Decision Making and Autonomous Complex Systems

Deep learning consists of various machine learning algorithms that aim to learn multiple levels of abstraction from data in a hierarchical manner. It is a tool to construct models using the data that mimics a real world process without an exceedingly tedious modelling of the actual process. We show that deep learning is a viable solution to decision making in mechanical engineering problems and complex physical systems. In this work, we demonstrated the application of this data-driven method in the design of microfluidic devices to serve as a map between the user-defined cross-sectional shape of the flow and the corresponding arrangement of micropillars in the flow channel that contributed to the flow deformation. We also present how deep learning can be used in the early detection of combustion instability for prognostics and health monitoring of a combustion engine, such that appropriate measures can be taken to prevent detrimental effects as a result of unstable combustion. One of the applications in complex systems concerns robotic path planning via the systematic learning of policies and associated rewards. In this context, a deep architecture is implemented to infer the expected value of information gained by performing an action based on the states of the environment. We also applied deep learning-based methods to enhance natural low-light images in the context of a surveillance framework and autonomous robots. Further, we looked at how machine learning methods can be used to perform root-cause analysis in cyber-physical systems subjected to a wide variety of operation anomalies. In all studies, the proposed frameworks have been shown to demonstrate promising feasibility and provided credible results for large-scale implementation in the industry

Improving the interpretability of causality maps for fault identification

Thesis (MEng)--Stellenbosch University, 2020.ENGLISH ABSTRACT: Worldwide competition forces modern mineral processing plants to operate at high productivity. This high productivity is achieved by implementing process monitoring to maintain the desired operating conditions. However, a fault originating in one section of a plant can propagate throughout the plant and so obscure its root cause. Causality analysis is a method that identifies the cause-effect relationships between process variables and presents these in a causality map which can be used to track the propagation path of a fault back to its root cause. A major obstacle to the wide acceptance of causality analysis as a tool for fault diagnosis in industry is the poor interpretability of causality maps. This study identified, proposed and assessed ways to improve the interpretability of causality maps for fault identification. All approaches were tested on a simulated case study and the resulting maps compared to a standard causality map or its transitive reduction. The ideal causality map was defined and all comparisons were performed based on its characteristics. Causality maps were produced using conditional Granger causality (GC), with a novel heuristic approach for selecting sampling period and time window. Conditional GC was found to be ill-suited to plant-wide causality analysis, due to large data requirements, poor model order selection using AIC, and inaccuracy in the presence of multiple different residence times and time delays. Methods to incorporate process knowledge to constrain connections and potential root causes were investigated and found to remove all spurious connections and decrease the pool of potential root cause variables respectively. Tools such as visually displaying node rankings on the causality map and incorporating sliders to manipulate connections and variables were also investigated. Furthermore, a novel hierarchical approach for plant-wide causality analysis was proposed, where causality maps were constructed in two subsequent stages. In the first stage, a less-detailed plant-wide map was constructed using representatives for groups of variables, and used to localise the fault to one of those groups of variables. Variables were grouped according to plant sections or modules identified in the data, and the first principal component (PC1) was used to represent each group (PS-PC1 and Mod-PC1 respectively). PS-PC1 was found to be the most promising approach, as its plant-wide map clearly identified the true root cause location, and the stage-wise application of conditional GC significantly reduced the required number of samples from 13 562 to 602. Lastly, a usability study in the form of a survey was performed to investigate the potential for industrial application of the tools and approaches presented in this study. Twenty responses were obtained, with participants consisting of Stellenbosch University final-year/postgraduate students, employees of an industrial IoT firm, and Anglo American Platinum employees. Main findings include that process knowledge is vital; grouping variables improves interpretability by decreasing the number of nodes; accuracy must be maintained during causality map simplification; and sliders add confusion by causing significant changes in the causality map. In addition, survey results found PS-PC1 to be the most user-friendly approach, further emphasizing its potential for application in industry.AFRIKAANSE OPSOMMING: Wêreldwye kompetisie forseer moderne mineraalprosesseringaanlegte om by hoë produktiwiteit bedryf te word. Hierdie hoë produktiwiteit word bereik deur prosesmonitering te implementeer om die gewenste bedryfskondisies te handhaaf. ’n Fout wat in een deel van ’n aanleg ontstaan kan egter regdeur die aanleg voortplant en so die grondoorsaak verberg. Oorsaaklikheidanalise is ’n metode wat die oorsaak-en-gevolg-verhouding tussen prosesveranderlikes identifiseer en hierdie in ’n oorsaaklikheidskaart toon wat gebruik kan word om die voortplantings roete van ’n fout terug na sy grondoorsaak te volg. ’n Groot hindernis vir die wye aanvaarding van oorsaaklikheidanalise as instrument vir foutdiagnose in industrie, is die swak interpreteerbaarheid van oorsaaklikheidskaarte. Hierdie studie het maniere om die interpreteerbaarheid van oorsaaklikheidskaarte vir foutidentifikasie te verbeter, geïdentifiseer, voorgestel en geassesseer. Alle benaderings is getoets op ’n gesimuleerde gevallestudie en die resulterende kaarte is vergelyk met ’n standaard oorsaaklikheidskaart of sy transitiewe inkrimping. Die ideale oorsaaklikheidskaart is gedefinieer en alle vergelykings is uitgevoer gebaseer op sy karakteristieke. Oorsaaklikheidskaarte is geproduseer deur kondisionele Granger-oorsaaklikheid (GC) te gebruik, met ’n nuwe heuristiese benadering om steekproefperiode en tydgleuf te selekteer. Kondisionele GC is gevind om nie gepas te wees vir aanlegwye oorsaaklikheidanalise nie, as gevolg van groot datavereistes, swak seleksie van modelorde as AIC gebruik word, en onakkuraatheid in die teenwoordigheid van veelvoudige, verskillende verblyftye en tydvertraging. Metodes om proseskennis te inkorporeer om konneksies en potensiële grondoorsake te bedwing, is ondersoek en gevind om alle konneksies wat vals is te verwyder en die groep van potensiële grondoorsaakveranderlikes te verminder, onderskeidelik. Instrumente soos om node-ordes op die oorsaaklikheidskaart visueel te vertoon en skuiwers te inkorporeer om konneksies en veranderlikes te manipuleer is ook ondersoek. Verder is ’n nuwe hiërargiese benadering vir aanlegwye oorsaaklikheidanalise voorgestel, waar oorsaaklikheidskaarte in twee opeenvolgende fases gebou is. In die eerste fase is ’n minder gedetaileerde aanlegwye kaart gebou deur verteenwoordigers vir groepe veranderlikes te gebruik, en is gebruik om die fout na een van daardie groepe van veranderlikes te lokaliseer. Veranderlikes is gegroepeer volgens aanlegdele of modules geïdentifiseer in die data, en die eerste hoof komponent (PC1) is gebruik om elke groep te verteenwoordig (PS-PC1 en Mod-PC1 onderskeidelik). PS-PC1 is gevind om die mees belowende benadering te wees, want sy aanlegwye kaart het duidelik die ware grondoorsaakligging geïdentifiseer, en die stap-gewyse toepassing van kondisionele GC het die vereisde aantal steekproewe beduidend verminder van 13 562 tot 602. Laastens, ’n bruikbaarheidstudie in die vorm van ’n opname is uitgevoer om die potensiaal vir industriële toepassing van die instrumente en benaderinge voorgestel in hierdie studie, te ondersoek. Twintig antwoorde is verkry, met deelnemers wat bestaan het uit Universiteit van Stellenbosch se finale jaar/nagraadse studente, werknemers van ’n industriële IoT-firma, en Anglo American Platinum werknemers. Hoofbevindinge het ingehou dat proseskennis noodsaaklik is; om veranderlikes te groepeer verbeter interpreteerbaarheid deur die aantal nodes te verminder; akkuraatheid moet gehandhaaf word gedurende vereenvoudiging van oorsaaklikheidskaarte; en skuiwers dra by tot verwarring deur beduidende veranderinge in die oorsaaklikheidskaart te maak. Daarmee saam het die opname se resultate gevind dat PS-PC1 die meer gebruiksvriendelike benadering was, wat sy potensiaal vir toepassing verder beklemtoon.Master