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[EN] An accurate fault diagnosis of both, faults sensors and real process faults have become more and more important for process monitoring (minimize downtime, increase safety of plant operation and reduce the manufacturing cost). Quick and correct fault diagnosis is required in order to put back on track our processes or products before safety or quality can be compromised. In the study and comparison of the fault diagnosis methodologies, this thesis distinguishes between two different scenarios, methods for multivariate statistical quality control (MSQC) and methods for latent-based multivariate statistical process control: (Lb-MSPC). In the first part of the thesis the state of the art on fault diagnosis and identification (FDI) is introduced. The second part of the thesis is devoted to the fault diagnosis in multivariate statistical quality control (MSQC). The rationale of the most extended methods for fault diagnosis in supervised scenarios, the requirements for their implementation, their strong points and their drawbacks and relationships are discussed. The performance of the methods is compared using different performance indices in two different process data sets and simulations. New variants and methods to improve the diagnosis performance in MSQC are also proposed. The third part of the thesis is devoted to the fault diagnosis in latent-based multivariate statistical process control (Lb-MSPC). The rationale of the most extended methods for fault diagnosis in supervised Lb-MSPC is described and one of our proposals, the Fingerprints contribution plots (FCP) is introduced. Finally the thesis presents and compare the performance results of these diagnosis methods in Lb-MSPC. The diagnosis results in two process data sets are compared using a new strategy based in the use of the overall sensitivity and specificity / [ES] La realización de un diagnóstico preciso de los fallos, tanto si se trata de fallos de sensores como si se trata de fallos de procesos, ha llegado a ser algo de vital importancia en la monitorización de procesos (reduce las paradas de planta, incrementa la seguridad de la operación en planta y reduce los costes de producción). Se requieren diagnósticos rápidos y correctos si se quiere poder recuperar los procesos o productos antes de que la seguridad o la calidad de los mismos se pueda ver comprometida. En el estudio de las diferentes metodologías para el diagnóstico de fallos esta tesis distingue dos escenarios diferentes, métodos para el control de estadístico multivariante de la calidad (MSQC) y métodos para el control estadístico de procesos basados en el uso de variables latentes (Lb-MSPC). En la primera parte de esta tesis se introduce el estado del arte sobre el diagnóstico e identificación de fallos (FDI). La segunda parte de la tesis está centrada en el estudio del diagnóstico de fallos en control estadístico multivariante de la calidad. Se describen los fundamentos de los métodos más extendidos para el diagnóstico en escenarios supervisados, sus requerimientos para su implementación sus puntos fuertes y débiles y sus posibles relaciones. Los resultados de diagnóstico de los métodos es comparado usando diferentes índices sobre los datos procedentes de dos procesos reales y de diferentes simulaciones. En la tesis se proponen nuevas variantes que tratan de mejorar los resultados obtenidos en MSQC. La tercera parte de la tesis está dedicada al diagnóstico de fallos en control estadístico multivariante de procesos basados en el uso de modelos de variables latentes (Lb-MSPC). Se describe los fundamentos de los métodos mas extendidos en el diagnóstico de fallos en Lb-MSPC supervisado y se introduce una de nuestras propuestas, el fingerprint contribution plot (FCP). Finalmente la tesis presenta y compara los resultados de diagnóstico de los métodos propuestos en Lb-MSPC. Los resultados son comparados sobre los datos de dos procesos usando una nueva estrategia basada en el uso de la sensitividad y especificidad promedia. / [CA] La realització d'un diagnòstic precís de les fallades, tant si es tracta de fallades de sensors com si es tracta de fallades de processos, ha arribat a ser de vital importància en la monitorització de processos (reduïx les parades de planta, incrementa la seguretat de l'operació en planta i reduïx els costos de producció) . Es requerixen diagnòstics ràpids i correctes si es vol poder recuperar els processos o productes abans de que la seguretat o la qualitat dels mateixos es puga veure compromesa. En l'estudi de les diferents metodologies per al diagnòstic de fallades esta tesi distingix dos escenaris diferents, mètodes per al control estadístic multivariant de la qualitat (MSQC) i l mètodes per al control estadístic de processos basats en l'ús de variables latents (Lb-MSPC). En la primera part d'esta tesi s'introduïx l'estat de l'art sobre el diagnòstic i identificació de fallades (FDI). La segona part de la tesi està centrada en l'estudi del diagnòstic de fallades en control estadístic multivariant de la qualitat. Es descriuen els fonaments dels mètodes més estesos per al diagnòstic en escenaris supervisats, els seus requeriments per a la seua implementació els seus punts forts i febles i les seues possibles relacions. Els resultats de diagnòstic dels mètodes és comparat utilitzant diferents índexs sobre les dades procedents de dos processos reals i de diferents simulacions. En la tesi es proposen noves variants que tracten de millorar els resultats obtinguts en MSQC. La tercera part de la tesi està dedicada al diagnòstic de fallades en control estadístic multivariant de processos basat en l'ús de models de variables latents (Lb-MSPC). Es descriu els fonaments dels mètodes més estesos en el diagnòstic de fallades en MSPC supervisat i s'introdueix una nova proposta, el fingerprint contribution plot (FCP). Finalment la tesi presenta i compara els resultats de diagnòstic dels mètodes proposats en MSPC. Els resultats són comparats sobre les dades de dos processos utilitzant una nova estratègia basada en l'ús de la sensibilitat i especificitat mitjana. / Vidal Puig, S. (2016). FAULT DIAGNOSIS TOOLS IN MULTIVARIATE STATISTICAL PROCESS AND QUALITY CONTROL [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61292

Statistical process monitoring and hierarchical Bayesian modeling are two ways to learn more about processes of interest. In this work, we consider two main components: risk-adjusted monitoring and Bayesian hierarchical models for spatial data. Usually, if prior information about a process is known, it is important to incorporate this into the monitoring scheme. For example, when monitoring 30-day mortality rates after surgery, the pre-operative risk of patients based on health characteristics is often an indicator of how likely the surgery is to succeed. In these cases, risk-adjusted monitoring techniques are used. In this work, the practical limitations of the traditional implementation of risk-adjusted monitoring methods are discussed and an improved implementation is proposed. A method to perform spatial risk-adjustment based on exact locations of concurrent observations to account for spatial dependence is also described. Furthermore, the development of objective priors for fully Bayesian hierarchical models for areal data is explored for Gaussian responses. Collectively, these statistical methods serve as analytic tools to better monitor and model spatial processes. / Ph. D. / The purpose of this research was to advance understanding of help-seeking behaviors of lowincome older adults who were deemed ineligible to receive state-funded assistance. I used health services data from two independent state agencies to assess factors associated with service use and health status; follow-up interviews were conducted to explore self-management strategies of rural older adults with unmet needs. Older adults who did not receive help were at increased risk for hospitalization and mortality compared to individuals who received helped. Rural older adults were significantly more likely to not receive help and were at increased risk for mortality, placing them in a vulnerable position. Interviews with rural-dwelling older adults that were not receiving help highlighted the challenges associated with living with unmet needs but demonstrated resilience through their use of physical and psychological coping mechanisms to navigate daily challenges and maintain health and well-being. They had to deal with numerous difficulties performing instrumental activities of daily living (IADL); mobility was an underlying problem that led to subsequent IADL limitations, such as difficulty with household chores and meal preparation. Policymakers need to advocate for services that allow older adults to address preemptively their care needs before they become unmanageable. Ensuring the availability of services for near-risk older adults who are proactive in addressing their functional care needs would benefit individuals and caregivers on whom they rely. Such services not only support older adults’ health, functioning, and well-being but may be cost-effective for public programs. Policies should reduce unmet needs among older adults by increasing service access in rural communities because even if services exist, they may not be available to this near-risk population of older adults.Many current scientific applications involve data collection that has some type of spatial component. Within these applications, the objective could be to monitor incoming data in order to quickly detect any changes in real time. Another objective could be to use statistical models to characterize and understand the underlying features of the data. In this work, we develop statistical methodology to monitor and model data that include a spatial component. Specifically, we develop a monitoring scheme that adjusts for spatial risk and present an objective way to quantify and model spatial dependence when data is measured for areal units. Collectively, the statistical methods developed in this work serve as analytic tools to better monitor and model spatial data.

As social networks become more prevalent, there is significant interest in studying these network data, the focus often being on detecting anomalous events. This area of research is referred to as social network surveillance or social network change detection. While there are a variety of proposed methods suitable for different monitoring situations, two important issues have yet to be completely addressed in network surveillance literature. First, performance assessments using simulated data to evaluate the statistical performance of a particular method. Second, the study of aggregated data in social network surveillance. The research presented tackle these issues in two parts, evaluation of a popular anomaly detection method and investigation of the effects of different aggregation levels on network anomaly detection. / Ph. D. / Social networks are increasingly becoming a part of our normal lives. These networks contain a wealth of information that can be immensely useful in a variety of areas, from targeting a specific audience for advertisement, to apprehending criminals, to detecting terrorist activities. The research presented focus evaluating popular methods on monitoring these social networks, and the potential information loss one might encounter when only limited information can be collected over a specific time period, we present our commendations on social network monitoring that are applicable to a wide range of scenarios as well.

Plant safety is the most important concern of chemical industries. Process faults can cause economic loses as well as human and environmental damages. Most of the operational faults are normally considered in the process design phase by applying methodologies such as Hazard and Operability Analysis (HAZOP). However, it should be expected that failures may occur in an operating plant. For this reason, it is of paramount importance that plant operators can promptly detect and diagnose such faults in order to take the appropriate corrective actions. In addition, preventive maintenance needs to be considered in order to increase plant safety. Fault diagnosis has been faced with both analytic and data-based models and using several techniques and algorithms. However, there is not yet a general fault diagnosis framework that joins detection and diagnosis of faults, either registered or non-registered in records. Even more, less efforts have been focused to automate and implement the reported approaches in real practice. According to this background, this thesis proposes a general framework for data-driven Fault Detection and Diagnosis (FDD), applicable and susceptible to be automated in any industrial scenario in order to hold the plant safety. Thus, the main requirement for constructing this system is the existence of historical process data. In this sense, promising methods imported from the Machine Learning field are introduced as fault diagnosis methods. The learning algorithms, used as diagnosis methods, have proved to be capable to diagnose not only the modeled faults, but also novel faults. Furthermore, Risk-Based Maintenance (RBM) techniques, widely used in petrochemical industry, are proposed to be applied as part of the preventive maintenance in all industry sectors. The proposed FDD system together with an appropriate preventive maintenance program would represent a potential plant safety program to be implemented. Thus, chapter one presents a general introduction to the thesis topic, as well as the motivation and scope. Then, chapter two reviews the state of the art of the related fields. Fault detection and diagnosis methods found in literature are reviewed. In this sense a taxonomy that joins both Artificial Intelligence (AI) and Process Systems Engineering (PSE) classifications is proposed. The fault diagnosis assessment with performance indices is also reviewed. Moreover, it is exposed the state of the art corresponding to Risk Analysis (RA) as a tool for taking corrective actions to faults and the Maintenance Management for the preventive actions. Finally, the benchmark case studies against which FDD research is commonly validated are examined in this chapter. The second part of the thesis, integrated by chapters three to six, addresses the methods applied during the research work. Chapter three deals with the data pre-processing, chapter four with the feature processing stage and chapter five with the diagnosis algorithms. On the other hand, chapter six introduces the Risk-Based Maintenance techniques for addressing the plant preventive maintenance. The third part includes chapter seven, which constitutes the core of the thesis. In this chapter the proposed general FD system is outlined, divided in three steps: diagnosis model construction, model validation and on-line application. This scheme includes a fault detection module and an Anomaly Detection (AD) methodology for the detection of novel faults. Furthermore, several approaches are derived from this general scheme for continuous and batch processes. The fourth part of the thesis presents the validation of the approaches. Specifically, chapter eight presents the validation of the proposed approaches in continuous processes and chapter nine the validation of batch process approaches. Chapter ten raises the AD methodology in real scaled batch processes. First, the methodology is applied to a lab heat exchanger and then it is applied to a Photo-Fenton pilot plant, which corroborates its potential and success in real practice. Finally, the fifth part, including chapter eleven, is dedicated to stress the final conclusions and the main contributions of the thesis. Also, the scientific production achieved during the research period is listed and prospects on further work are envisaged. / La seguridad de planta es el problema más inquietante para las industrias químicas. Un fallo en planta puede causar pérdidas económicas y daños humanos y al medio ambiente. La mayoría de los fallos operacionales son previstos en la etapa de diseño de un proceso mediante la aplicación de técnicas de Análisis de Riesgos y de Operabilidad (HAZOP). Sin embargo, existe la probabilidad de que pueda originarse un fallo en una planta en operación. Por esta razón, es de suma importancia que una planta pueda detectar y diagnosticar fallos en el proceso y tomar las medidas correctoras adecuadas para mitigar los efectos del fallo y evitar lamentables consecuencias. Es entonces también importante el mantenimiento preventivo para aumentar la seguridad y prevenir la ocurrencia de fallos. La diagnosis de fallos ha sido abordada tanto con modelos analíticos como con modelos basados en datos y usando varios tipos de técnicas y algoritmos. Sin embargo, hasta ahora no existe la propuesta de un sistema general de seguridad en planta que combine detección y diagnosis de fallos ya sea registrados o no registrados anteriormente. Menos aún se han reportado metodologías que puedan ser automatizadas e implementadas en la práctica real. Con la finalidad de abordar el problema de la seguridad en plantas químicas, esta tesis propone un sistema general para la detección y diagnosis de fallos capaz de implementarse de forma automatizada en cualquier industria. El principal requerimiento para la construcción de este sistema es la existencia de datos históricos de planta sin previo filtrado. En este sentido, diferentes métodos basados en datos son aplicados como métodos de diagnosis de fallos, principalmente aquellos importados del campo de “Aprendizaje Automático”. Estas técnicas de aprendizaje han resultado ser capaces de detectar y diagnosticar no sólo los fallos modelados o “aprendidos”, sino también nuevos fallos no incluidos en los modelos de diagnosis. Aunado a esto, algunas técnicas de mantenimiento basadas en riesgo (RBM) que son ampliamente usadas en la industria petroquímica, son también propuestas para su aplicación en el resto de sectores industriales como parte del mantenimiento preventivo. En conclusión, se propone implementar en un futuro no lejano un programa general de seguridad de planta que incluya el sistema de detección y diagnosis de fallos propuesto junto con un adecuado programa de mantenimiento preventivo. Desglosando el contenido de la tesis, el capítulo uno presenta una introducción general al tema de esta tesis, así como también la motivación generada para su desarrollo y el alcance delimitado. El capítulo dos expone el estado del arte de las áreas relacionadas al tema de tesis. De esta forma, los métodos de detección y diagnosis de fallos encontrados en la literatura son examinados en este capítulo. Asimismo, se propone una taxonomía de los métodos de diagnosis que unifica las clasificaciones propuestas en el área de Inteligencia Artificial y de Ingeniería de procesos. En consecuencia, se examina también la evaluación del performance de los métodos de diagnosis en la literatura. Además, en este capítulo se revisa y reporta el estado del arte correspondiente al “Análisis de Riesgos” y a la “Gestión del Mantenimiento” como técnicas complementarias para la toma de medidas correctoras y preventivas. Por último se abordan los casos de estudio considerados como puntos de referencia en el campo de investigación para la aplicación del sistema propuesto. La tercera parte incluye el capítulo siete, el cual constituye el corazón de la tesis. En este capítulo se presenta el esquema o sistema general de diagnosis de fallos propuesto. El sistema es dividido en tres partes: construcción de los modelos de diagnosis, validación de los modelos y aplicación on-line. Además incluye un modulo de detección de fallos previo a la diagnosis y una metodología de detección de anomalías para la detección de nuevos fallos. Por último, de este sistema se desglosan varias metodologías para procesos continuos y por lote. La cuarta parte de esta tesis presenta la validación de las metodologías propuestas. Específicamente, el capítulo ocho presenta la validación de las metodologías propuestas para su aplicación en procesos continuos y el capítulo nueve presenta la validación de las metodologías correspondientes a los procesos por lote. El capítulo diez valida la metodología de detección de anomalías en procesos por lote reales. Primero es aplicada a un intercambiador de calor escala laboratorio y después su aplicación es escalada a un proceso Foto-Fenton de planta piloto, lo cual corrobora el potencial y éxito de la metodología en la práctica real. Finalmente, la quinta parte de esta tesis, compuesta por el capítulo once, es dedicada a presentar y reafirmar las conclusiones finales y las principales contribuciones de la tesis. Además, se plantean las líneas de investigación futuras y se lista el trabajo desarrollado y presentado durante el periodo de investigación.

Double sampling procedure is adapted from a statistical branch called acceptance sampling. The first Shewhart-type double sampling monitoring scheme was introduced in the statistical process monitoring (SPM) field in 1974. The double sampling monitoring scheme has been proven to effectively decrease the sampling effort and, at the same time, to decrease the time to detect potential out-of-control situations when monitoring the location, variability, joint location and variability using univariate or multivariate techniques. Consequently, an overview is conducted to give a full account of all 76 publications on double sampling monitoring schemes that exist in the SPM literature. Moreover, in the review conducted here, these are categorized and summarized so that any research gaps in the SPM literature can easily be identified. Next, based on the knowledge gained from the literature review about the existing designs for monitoring the process mean, a new type of double sampling design is proposed. The new charting region design lead to a class of a control charts called a side-sensitive double sampling (SSDS) monitoring schemes. In this study, the SSDS scheme is implemented to monitor the process mean when the underlying process parameters are known as well as when they are unknown. A variety of run-length properties (i.e., the 5th, 25th, 50th, 75th, 95th percentiles, the average run-length (𝐴𝑅𝐿), standard deviation of the run-length (𝑆𝐷𝑅𝐿), the average sample size (𝐴𝑆𝑆) and the average extra quadratic loss (𝐴𝐸𝑄𝐿) metrics) are used to design and implement the new SSDS scheme. Comparisons with other established monitoring schemes (when parameters are known and unknown) indicate that the proposed SSDS scheme has a better overall performance. Illustrative examples are also given to facilitate the real-life implementation of the proposed SSDS schemes. Finally, a list of possible future research ideas is given with hope that this will stimulate more future research on simple as well as complex double sampling schemes (especially using the newly proposed SSDS design) for monitoring a variety of quality characteristics in the future. / Statistics / M. Sc. (Statistics)