Automatic control techniques have been widely employed in industry to increase efficiency and profitability of the processes. However, reliability on automation increases the susceptibility of the system to faults in major control equipment such as actuators and sensors. This realization has motivated design of frameworks for fault detection and isolation (FDI) and fault tolerant control (FTC). The success of these FDI and FTC mechanisms is contingent on their ability to handle complexities associated with process systems such as nonlinearity, uncertainty, high dimensionality and the resulting effects of the existence of complexity in system structure such as faults that cannot be isolated. Motivated by the above considerations, this thesis considers the problem of fault diagnosis and fault tolerant control for complex process systems. First, an FDI framework is designed that can detect and confine possible locations for faults that cannot be isolated. Next, the problem of simultaneous actuator and sensor fault diagnosis for nonlinear uncertain systems. The key idea is to design FDI filters in a way they account for the impact of uncertainty explicitly. This work then considers the problem of simultaneous fault diagnosis in nonlinear uncertain networked systems. FDI is achieved using a distributed architecture, comprised of a bank of local FDI (LFDI) schemes that communicate with each other. The efficacy of the proposed FDI methodologies is shown via application to a number of chemical process examples. Finally, an integrated framework is proposed for fault diagnosis and fault tolerant control of variable air volume (VAV) boxes, a common component of heating, ventilation and air conditioning (HVAC) systems as an industrial case study of complex systems. The advantages of the proposed framework are diagnosing multiple faults and handling faults in stuck dampers using a safe parking strategy with energy saving capability. / Thesis / Doctor of Philosophy (PhD) / Automation is the key to increase efficiency and profitability of the processes. However, as the level of automation increases, major control equipment are more prone to faults. Thus, fault detection and isolation (FDI) and fault tolerant control (FTC) frameworks are required for fault handling. Fault handling, however, can only be efficiently achieved if the designed FDI and FTC frameworks are able to deal with complexities arising in process systems such as nonlinearity, uncertainty, high dimensionality and the resulting effects of the existence of complexity in system structure such as faults that cannot be isolated. This motivates design of FDI and FTC frameworks for complex process systems. First, FDI frameworks are presented that can diagnose faults in the presence of complexities mentioned above. Then, an integrated framework is designed for diagnosing and handling faults of heating, ventilation and air conditioning (HVAC) systems as an industrial case study of complex process systems.

Companies are under constant pressure to adapt and improve their processes to staycompetitive. Since most of their processes are handled by software, it also needs toconstantly change. Those improvements and changes add up over time and increase thecomplexity of the system, which in turn prevents the company from further adaption.In order to change and improve existing business processes and their implementation withinsoftware, several stakeholders have to go through a long process. Current IT methodologies arenot suitable for such a dynamic environment. The analysis of this change process shows thatfour software characteristics are important to speed it up. They are: transparency, adaptability,testability and reparability. Transparency refers to the users capability to understand what thesystem is doing, where and why. Adaptability is a mainly technical characteristic that indicatesthe capability of the system to evolve or change. Testability allows automated testing andvalidation for correctness without requiring manual checks. The last characteristic is reparability,which describes the possibility to bring the system back into a consistent and correct state, evenif erroneous software was deployed.An architecture and software development patterns are evaluated to build an overall programmingmodel that provides the software characteristics. The overall architecture is basedon microservices, which facilitates decoupling and maintainability for the software as well asorganizations. Command Query Responsibility Segregation decouples read from write operationsand makes data changes explicit. With Event Sourcing, the system stores not only the currentstate, but all historic events. It provides a built-in audit trail and is able to reproduce differentscenarios for troubleshooting and testing.A demo process is defined and implemented within multiple prototypes. The design of theprototype is based on the programming model. It is built in Javascript and implements Microservices,CQRS and Event Sourcing. The prototypes show and validate how the programmingmodel provides the software characteristics. Software built with the programming model allowscompanies to iterate faster at scale. Since the programming model is suited for complex processes,the main limitation is that the validation is based on a demo process that is simpler and thebenefits are hard to quantify. / ör att fortsatt vara konkurrenskraftiga är företag under konstant press att anpassa ochförbättra sina processer. Eftersom de flesta processer hanteras av programvara, behöveräven de ständigt förändras. Övertiden leder dessa förbättringar och förändringar till ökadsystemkomplexitet, vilket i sin tur hindrar företaget från ytterligare anpassningar. För attförändra och förbättra befintliga affärsprocesser och dess programvara, måste idag typiskt fleraaktörer vara en del av en lång och tidskrävande process. Nuvarande metoder är inte lämpade fören sådan dynamisk miljö. Detta arbete har fokuserat på fyra programvaruegenskaper som ärviktiga för att underlätta förändringsprocesser. Dessa fyra egenskaper är: öppenhet, anpassningsförmåga,testbarhet och reparerbarhet. Öppenhet, hänvisar till förmågan att förstå varför, var ochvad systemet gör. Anpassningsbarhet är huvudsakligen en teknisk egenskap som fokuserar påsystemets förmåga att utvecklas och förändras. Testbarhet strävar efter automatisk testning ochvalidering av korrekthet som kräver ingen eller lite manuell kontroll. Den sista egenskapen ärreparerbarhet, som beskriver möjligheten att återhämta systemet till ett konsekvent och korrekttillstånd, även om felaktig programvara har använts. En programmeringsmodell som rustarprogramvara med de ovan beskrivna programegenskaperna är utvecklad i detta examensarbete.Programmeringsmodellens arkitektur är baserad på diverse micro-tjänster, vilka ger brafrånkopplings- och underhållsförmåga för en programvara, samt användarorganisationerna.Command Query Responsibility Segregation (CQRS) frånkopplar läsoperationer från skrivoperationeroch gör ändringar i data explicita. Med Event Sourcing lagrar systemet inte endastdet nuvarande tillståndet, utan alla historiska händelser. Modellen förser användarna medett inbyggt revisionsspår och kan reproducera olika scenarion för felsökning och testning. Endemoprocess är definierad och implementerad i tre olika prototyper. Designen av prototypernaär baserad på den föreslagna programmeringsmodellen. Vilken är byggd i Javascript och implementerarmicro-tjänster, CQRS och Event Sourcing. Prototyperna visar och validerar hurprogrammeringsmodellen ger programvaran rätt egenskaper. Programvara byggd med dennaprogrammeringsmodell tillåter företag att iterera snabbare. De huvudsakliga begränsningarna iarbetet är att valideringen är baserad på en enklare demoprocess och att dess fördelar är svåraatt kvantifiera.

<p dir="ltr">Condition-based maintenance is a proactive maintenance strategy that prevents failures or diminished functionality in process systems through proper monitoring and management of process conditions. Despite being considered a mature maintenance management strategy in various industries, condition-based maintenance remains underutilized in pharmaceutical manufacturing. This situation needs to change, especially as the pharmaceutical industry continues to shift from batch to continuous manufacturing, where the implementation of CBM as a maintenance strategy assumes a greater importance.</p><p dir="ltr">This dissertation focused on addressing the challenges of implementing CBM in a continuous drug product manufacturing system. These challenges stem from the unique aspects of pharmaceutical drug product manufacturing, which includes the peculiar behavior of particulate materials and the evolutionary nature of pharmaceutical process development. The proposed solutions to address these challenges revolve around an innovative framework for the practical development of condition monitoring systems. Overall, this framework enables the incorporation of limited process knowledge in creating condition monitoring systems, which has the desired effect of empowering data-driven machine learning models.</p><p dir="ltr">A key feature of this framework is a formalized method to represent the process condition, which is usually vaguely defined in literature. This representation allows the proper mapping of preexisting condition monitoring systems, and the segmentation of the entire process condition model into smaller modules that have more manageable condition monitoring problems. Because this representation methodology is based on probabilistic graphical modelling, the smaller modules can then be holistically integrated via their probabilistic relationships, allowing the robust operation of the resulting condition monitoring system and the process it monitors.</p><p dir="ltr">Breaking down the process condition model into smaller segments is crucial for introducing novel fault detection capabilities, which enhances model prediction transparency and ensures prediction acceptance by a human operator. In this work, a methodology based on prediction probabilities was introduced for developing condition monitoring systems with novel fault detection capabilities. This approach relies on high-performing machine learning models capable of consistently classifying all the initially known conditions in the fault library with a high degree of certainty. Simplifying the condition monitoring problem through modularization facilitates this, as machine learning models tend to perform better on simpler systems. Performance indices were proposed to evaluate the novel fault detection capabilities of machine learning models, and a formal approach to managing novel faults was introduced.</p><p dir="ltr">Another benefit of modularization is the identification of condition monitoring blind spots. Applying it to the RC led to sensor development projects such as the virtual sensor for measuring granule flowability. This sensor concept was demonstrated successfully by using a data-driven model to predict granule flowability based on size and shape distribution measurements. With proper model selection and feature extraction guided by domain expertise, the resulting sensor achieved the best prediction performance reported in literature for granule flowability.</p><p dir="ltr">As a demonstration exercise in examining newly discovered faults, this work investigated a roll compaction phenomenon that is usually concealed from observation due to equipment design. This phenomenon results in the ribbon splitting along its thickness as it comes out of the rolls. In this work, important aspects of ribbon splitting were elucidated, particularly its predictability based on RC parameters and the composition of the powder blend used to form the ribbon. These findings have positive ramifications for the condition monitoring of the RC, as correspondence with industrial practitioners suggests that a split ribbon is desirable in some cases, despite being generally regarded as undesirable in the limited literature available on the subject.</p><p dir="ltr">Finally, this framework was primarily developed for the pharmaceutical dry granulation line, which consists of particle-based systems with a moderate level of complexity. However, it was also demonstrated to be feasible for the Tennessee Eastman Process (TEP), a more complex liquid-gas process system with a greater number of process faults, variables, and unit operations. Applying the framework resulted in machine learning models that yielded one of the best fault detection performances reported in literature for the TEP, while also introducing additional capabilities not yet normally reported in literature, such as fault diagnosis and novel fault detection.</p>