Control system based loop and process monitoring

For the sake of both economy and safety, the ability to diagnose a fault or disturbance is of great interest for an operator/engineer in process industries. To be practicable an on-line system with this capability must contain a suite of methods because no single method is likely to diagnose all possible faults. This thesis aims to contribute one novel component to this suite. This thesis envisages the situation where the detection and diagnosis of faults and disturbances would be distributed to separate modules, each associated with the individual control systems located throughout a plant. In particular the thesis addresses those plants whose control systems inherently eliminate steady state error. Thus it seeks to address the large proportion of process plants that have proportional plus integral action as standard. By reasoning about changes in steady state an approach is proposed that requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. Because the approach can be implemented as modules that are largely based on standard control systems, the implementation can be configured and commissioned using various generic programmes and hence has the potential to be commercialised. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type number of zero or not and so on. It is founded on the application of both signed directed graph (SDG) and control systems theory to single and cascade control systems with integral action. This results in the derivation of cause-effect knowledge and fault isolation procedures that take into account factors like interactions between control systems, and the availability of non-control-loop- based measurements. Following on from a survey of the more relevant methods published in the literature, a theoretical analysis is carried out of what happens to control systems when they are subjected to various faults and disturbances. The main purpose is to derive equations to describe how control systems respond in the steady state to these occurrences. Although providing a foundation, these equations are unlikely to be suitable for direct use and a cause-effect analysis of the faults/disturbances involving signed-directed- graph (SDG) representation is then pursued. This leads to a search and test strategy for fault isolation involving interacting control systems, minimal knowledge acquisition and knowledge evolution. Since the approach is based on steady state deviations, a steady state change detection algorithm is proposed. The approach is tested by applying it to a continuous stirred tank reactor (CSTR) and to the Tennessee Eastman (T-E) process benchmark. Some recommendations are made for integrating the approach into a commercial software tool. In principle, the approach can form the basis for the diagnosis of faults/disturbances in both control systems and in the process itself. One of the key features is that the approach can work at different levels of detail. Diagnosis is based on knowledge of the signs of steady state interactions (gains) between individual control loops, non- control-system-related measurements and on the steady state effects of disturbances. Both faults and disturbances (e.g. a load change) can be diagnosed, although diagnostic detail, i.e. degree of isolation, is clearly dependent on the measurements and knowledge that is available. The concept of a distributed, control system based approach to the diagnosis of faults and disturbances, its development and application to various processes are all original, as are the integration aspects

Modeling and Intelligent Control for Spatial Processes and Spatially Distributed Systems

Dynamical systems are often characterized by their time-dependent evolution, named temporal dynamics. The space-dependent evolution of dynamical systems, named spatial dynamics, is another important domain of interest for many engineering applications. By studying both the spatial and temporal evolution, novel modeling and control applications may be developed for many industrial processes. One process of special interest is additive manufacturing, where a three-dimensional object is manufactured in a layer-wise fashion via a numerically controlled process. The material is printed over a spatial domain in each layer and subsequent layers are printed on top of each other. The spatial dynamics of the printing process over the layers is named the layer-to-layer spatial dynamics. Additive manufacturing provides great flexibility in terms of material selection and design geometry for modern manufacturing applications, and has been hailed as a cornerstone technology for smart manufacturing, or Industry 4.0, applications in industry. However, due to the issues in reliability and repeatability, the applicability of additive manufacturing in industry has been limited. Layer-to-layer spatial dynamics represent the dynamics of the printed part. Through the layer-to-layer spatial dynamics, it is possible to represent the physical properties of the part such as dimensional properties of each layer in the form of a heightmap over a spatial domain. Thus, by considering the spatial dynamics, it is possible to develop models and controllers for the physical properties of a printed part. This dissertation develops control-oriented models to characterize the spatial dynamics and layer-to-layer closed-loop controllers to improve the performance of the printed parts in the layer-to-layer spatial domain. In practice, additive manufacturing resources are often utilized as a fleet to improve the throughput and yield of a manufacturing system. An additive manufacturing fleet poses additional challenges in modeling, analysis, and control at a system-level. An additive manufacturing fleet is an instance of the more general class of spatially distributed systems, where the resources in the system (e.g., additive manufacturing machines, robots) are spatially distributed within the system. The goal is to efficiently model, analyze, and control spatially distributed systems by considering the system-level interactions of the resources. This dissertation develops a centralized system-level modeling and control framework for additive manufacturing fleets. Many monitoring and control applications rely on the availability of run-time, up-to-date representations of the physical resources (e.g., the spatial state of a process, connectivity and availability of resources in a fleet). Purpose-driven digital representations of the physical resources, known as digital twins, provide up-to-date digital representations of resources in run-time for analysis and control. This dissertation develops an extensible digital twin framework for cyber-physical manufacturing systems. The proposed digital twin framework is demonstrated through experimental case studies on abnormality detection, cyber-security, and spatial monitoring for additive manufacturing processes. The results and the contributions presented in this dissertation improve the performance and reliability of additive manufacturing processes and fleets for industrial applications, which in turn enables next-generation manufacturing systems with enhanced control and analysis capabilities through intelligent controllers and digital twins.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169635/1/baltaefe_1.pd

Fault detection, identification and economic impact assessment for a pressure leaching process

Thesis (MEng)--Stellenbosch University, 2017.ENGLISH SUMMARY: Modern chemical and metallurgical processes consist of numerous process units with several complex interactions existing between them. The increased process complexity has in turn amplified the effect of faulty process conditions on the overall process performance. Fault diagnosis forms a critical part of a process monitoring strategy and is crucial for improved process performance. The increased amount of process measurements readily available in modern process plants allows for more complex data-driven fault diagnosis methods. Linear and nonlinear feature extraction methods are popular multivariate fault diagnosis procedures employed in literature. However, these methods are yet to find wide spread industrial application. The multivariate fault diagnosis methods are not often evaluated on real-world modern chemical processes. The lack of real world application has in turn led to the absence of economic performance assessments evaluating the potential profitability of these fault diagnosis methods. The aim of this study is to design and investigate the performance of a fault diagnosis strategy with both traditional fault diagnosis performance metrics and an economic impact assessment (EIA). A complex dynamic process model of the pressure leach at a base metal refinery (BMR) was developed by Dorfling (2012). The model was recently updated by Miskin (2015), who included the actual process control layers present at the BMR. A fault library was developed, through consultation of expert knowledge from the BMR, and incorporated into the dynamic model by Miskin (2015). The pressure leach dynamic model will form the basis for the investigation. Principal component analysis (PCA) and kernel PCA (KPCA) were employed as feature extraction methods. Traditional and reconstruction based contributions were employed as fault identification methods. Economic Performance Functions (EPFs) were developed from expert knowledge from the plant. The fault diagnosis performance was evaluated through the traditional performance metrics and the EPFs. Both PCA and KPCA provided improved fault detection results when compared to a simple univariate method. PCA provided significantly improved detection results for five of the eight faults evaluated, when compared to univariate detection. Fault identification results suffered from significant fault smearing. The significant fault detection results did not translate into a significant economic benefit. The EIA proved the process to be robust against faults, when implementing a basic univariate fault detection approach. Recommendations were made for possible industrial application and future work focusing on EIAs, training data selection and fault smearing.AFRIKAANS OPSOMMING: Moderne chemiese- en metallurgiese-prosesse bestaan uit ʼn verskeidenheid proseseenhede met talle komplekse interaksies wat tussen die proseseenhede bestaan. Die toename in die komplekse interaksies versterk die effek van foutiewe prosesomstandighede op die algehele prosesverrigting. Die toename in die beskikbaarbaarheid van prosesmetings in moderne prosesse, laat meer komplekse datagedrewe fout-diagnostiese metodes toe. Lineêre en nie-lineêre kenmerk-ekstraksie metodes is gewilde meerveranderlike fout-diagnostiese prosedures wat in literatuur gebruik word. Dié metodes het egter nog nie ʼn algemene toepassing in die industrie gevind nie. Die meerveranderlike fout-diagnostiese metodes word egter nie gereeld op die werklik moderne chemiese-prosesse toegepas nie; die gebrek aan dié toepassings veroorsaak die afwesigheid van ekonomiese impakstudies wat die winsgewendheid van hierdie fout-diagnostiese metodes evalueer. Die doel van hierdie studie is om ‘n fout-diagnostiese strategie te ontwerp en om die werkverrigting te ondersoek met beide tradisionele fout-diagnostiese werkverrigtingstatistieke en ekonomiese impak assessering (EIA). ‘n Komplekse dinamiese prosesmodel van die drukloogproses by ‘n basismetaalraffinadery (BMR) is ontwikkel deur Dorfling (2012). Die model is onlangs deur Miskin (2015) opdateer wat die werklike BMR prosesbeheerstrategie geïmplementeer het. ‘n Biblioteek van foute is ontwikkel d.m.v. die konsultering met kundiges by die BMR en is suksesvol opgeneem in die dinamiese model deur Miskin (2015). Die dinamiese drukloogmodel vorm die basis van hierdie projek. Hoofkomponentanalise (HKA) en Kern-HKA (KHKA) is gebruik as metodes vir kenmerk-ekstraksie. Tradisionele- en rekonstruksie-gebaseerde bydraberekeninge is gebruik as fout-identifikasie metodes. Ekonomiese-verrigtingfunksies (EVF’s) is ontwikkel met die hulp van kundiges by die BMR. Die fout-diagnose werkverrigting is geëvalueer met beide tradisionele fout-diagnostiese werkverrigtingstatistieke en die EVF’s. Beide HKA en KHKA het verbeterde foutopsporings resultate gelewer in vergelyking met ‘n eenvoudige eenveranderlike metode. HKA het beduidende verbeterde foutopsporingsresultate vir vyf van die agt foute gelewer, in vergelyking met eenveranderlike foutopsporing. Fout-identifikasie resultate het aan beduidende fout smeer-effekte gely. Dié beduidende foutopsporings resultate het nie tot ‘n beduidende ekonomiese voordeel gelei nie. Die EIA het bewys dat die proses wel robuus is teen foute, wanneer ‘n basiese eenveranderlike foutopspring strategie gevolg word. Aanbevelings is gemaak vir moontlike industriële aanwending en toekomstige werk wat fokus op EIA’s, opleidingsdata-seleksie en foutsmeer-effek

多変量時系列データの変分オートエンコーダによるロバストな教示なし異常検知

九州工業大学博士学位論文 学位記番号：情工博甲第370号 学位授与年月日：令和4年9月26日1: Introduction|2: Background & Theory|3: Methodology|4: Experiments and Discussion|5: Conclusions九州工業大学令和4年