FAULT DIAGNOSIS TOOLS IN MULTIVARIATE STATISTICAL PROCESS AND QUALITY CONTROL

[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 no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61292TESI

Observability and Economic aspects of Fault Detection and Diagnosis Using CUSUM based Multivariate Statistics

This project focuses on the fault observability problem and its impact on plant performance and profitability. The study has been conducted along two main directions. First, a technique has been developed to detect and diagnose faulty situations that could not be observed by previously reported methods. The technique is demonstrated through a subset of faults typically considered for the Tennessee Eastman Process (TEP); which have been found unobservable in all previous studies. The proposed strategy combines the cumulative sum (CUSUM) of the process measurements with Principal Component Analysis (PCA). The CUSUM is used to enhance faults under conditions of small fault/signal to noise ratio while the use of PCA facilitates the filtering of noise in the presence of highly correlated data. Multivariate indices, namely, T2 and Q statistics based on the cumulative sums of all available measurements were used for observing these faults. The ARLo.c was proposed as a statistical metric to quantify fault observability. Following the faults detection, the problem of fault isolation is treated. It is shown that for the particular faults considered in the TEP problem, the contribution plots are not able to properly isolate the faults under consideration. This motivates the use of the CUSUM based PCA technique previously used for detection, for unambiguously diagnose the faults. The diagnosis scheme is performed by constructing a family of CUSUM based PCA models corresponding to each fault and then testing whether the statistical thresholds related to a particular faulty model is exceeded or not, hence, indicating occurrence or absence of the corresponding fault. Although the CUSUM based techniques were found successful in detecting abnormal situations as well as isolating the faults, long time intervals were required for both detection and diagnosis. The potential economic impact of these resulting delays motivates the second main objective of this project. More specifically, a methodology to quantify the potential economical loss due to unobserved faults when standard statistical monitoring charts are used is developed. Since most of the chemical and petrochemical plants are operated under closed loop scheme, the interaction of the control is also explicitly considered. An optimization problem is formulated to search for the optimal tradeoff between fault observability and closed loop performance. This optimization problem is solved in the frequency domain by using approximate closed loop transfer function models and in the time domain using a simulation based approach. The optimization in the time domain is applied to the TEP to solve for the optimal tuning parameters of the controllers that minimize an economic cost of the process

A study of new and advanced control charts for two categories of time related processes

Ph.DDOCTOR OF PHILOSOPH

UNION INTERSECTION TEST IN INTERPRETING SIGNAL FROM MULTIVARIATE CONTROL CHART

Statistical Process Control (SPC) has been a very important discipline in quality control study since pioneered by Walter A. Shewhart in 1920s. Control charting is one of the important tools in SPC and has received wide attention from researchers as well as practitioners. The complexity and the impracticality in monitoring several univariate control charts for a multivariate process has made many practitioners use a multivariate control chart instead. Its usage gives a better control of the overall Type I error and the interdependency among variables is retained. Unfortunately, a multivariate control chart is not able to pinpoint the responsible variable(s) once an out-of-control (OOC) signal is triggered. Many diagnostic methods have been proposed to overcome this problem but all of them have their own limitations and drawbacks. The applicability of a diagnostic method for a limited number of variables, lack of physical interpretation, the complexity of the computation procedure and lack of location invariance are among the factors that have inhibited the implementation of multivariate charts. Lack of comparative studies for various diagnostic methods also makes it difficult for practitioners to choose an appropriate diagnostic method. This study highlights some problems that might arise in a comparison of diagnostic methods and makes suggestions to overcome them, hence, making the results of a comparative study more relevant and reliable. The effects of several factors such as the size of the deviation in a mean vector, the combination of various sizes of shifts in a mean vector and the inter-correlation among the variables on the performance of diagnostic methods are studied and a summary of the suitability of certain diagnostic methods for certain situations is given. This study presents a new comparison involving two diagnostic methods adapted from the methods proposed by Doganaksoy, Faltin and Tucker (1991) and Maravelakis et al. (2000). A problem related to the usage of eigenvectors with similar eigenvalues is revealed in this study and suggestions from previous studies regarding this matter are presented. Due to lack of multivariate approaches in dealing with the interpretation of a multivariate control chart signal, this study proposes a new method which embraces the principles of Union Intersection Test (UIT) in diagnosing an OOC signal. A thorough discussion of the UIT principle, the hypotheses, the test statistic and the application of the union intersection technique in the diagnosis problem is presented. An extension of the first comparison study is which includes the proposed method is carried out. The performance of the new diagnostic method is studied and its strengths and weaknesses are discussed. A simplified version for the new method, involving application of spectral decomposition, is also proposed. By using this simplified approach, the common practice of considering multiple types of covariance matrices in a comparison study of diagnostic methods can be avoided to some extent. This study is concluded with a few suggestions of potential further work

Monitoring wine fermentation using ATR-MIR spectroscopy and chemometric techniques.

El vi és un dels productes amb valor afegit més apreciats al món i és per això que el control de la producció vinícola ha sigut sempre un tema prioritari per a la majoria dels cellers. La implementació d’anàlisis at-line com són les Tècniques Analítiques de Processos (PAT), no només permet un control del vi acabat si no que també dóna la possibilitat de prendre mesures correctives al llarg del procés evitant així obtenir un producte final defectuós. En aquesta tesi doctoral, es va investigar la possibilitat d’implementar diferents estratègies per controlar i detectar desviacions durant la fermentació alcohòlica utilitzant un equip portable i de resposta ràpida: un equip d’ espectroscòpia en l’infraroig mitjà, en el mode de reflectància total atenuada (ATR-MIR) el qual permet obtenir, en pocs segons, una gran quantitat d’informació sobre el procés de fermentació que es va tractar amb diferents tècniques quimiomètriques. Primer, utilitzant les dades espectrals i la regressió de mínims quadrats parcials, es van predir diferents paràmetres químics durant la fermentació alcohòlica. En segon lloc, es van comparar els espectres de fermentacions control amb fermentacions desviades utilitzant l’anàlisi discriminant per mínims quadrats parcialsEl vino es uno de los productos con valor añadido más apreciados del mundo y por ello, el control de la producción vinícola ha sido siempre un tema prioritario para la mayoría de bodegas. La implementación de análisis at-line como son las Técnicas Analíticas de Procesos (PAT), no sólo permite un control del vino acabado si no que también brinda la posibilidad de tomar medidas correctivas a lo largo del proceso evitando así obtener un producto final defectuoso. En esta tesis doctoral, se investigó la posibilidad de implementar diferentes estrategias para controlar y detectar desviaciones durante la fermentación alcohólica utilizando un equipo portátil y de respuesta rápida: un equipo de espectroscopia en el infrarrojo medio, en el modo de reflectancia total atenuada (ATR-MIR) el cual permite obtener, en pocos segundos, una gran cantidad de información sobre el proceso de fermentación que se trató con diferentes técnicas quimiométricas. Primero, usando los datos espectrales y la regresión de mínimos cuadrados parciales, se predijeron distintos parámetros químicos durante la fermentación alcohólica. En segundo lugar, se compararon los espectros de fermentaciones control con fermentaciones desviadas utilizando el análisis discriminante por mínimos cuadrados parcialesWine is one of the most appreciated high added-value products in the world and therefore, controlling wine production has always been a priority for most wineries. Implementing at-line analyses such as Process Analytical Technologies (PAT) guidelines, not only enables a control of the final wine but also gives the possibility to apply correcting measures throughout the process, thus avoiding a defective final product. In this doctoral thesis, we investigated the possibility of implementing different strategies to control and detect deviations during wine alcoholic fermentation using a fast and portable equipment: an Attenuated Total Reflectance Mid-Infrared (ATR-MIR) spectrometer which allows obtaining, in a few seconds, a large amount of information about the fermentation process, which was processed with different chemometric techniques. First, using the spectral data and Partial Least Square Regression, different chemical parameters were predicted during alcoholic fermentation. Secondly, we compared the spectra from both Normal Operation Conditions and deviated fermentations using Partial Least Squares Discriminant Analysis. ANOVA–simultaneous component analysis was applied to study the influence of several factors into the variance of the spectra. Multivariate Curve Resolution Alternating Least Squares was used to model both alcoholic and malolactic fermentations. Finally, a PAT methodolog

Modeling the oriented strandboard manufacturing process and the oriented strandboard continuous rotary drying system

Oriented Strand Board (OSB) is the leading structural panel product used in residential building construction. This dissertation describes three models and a statistical process control technique all designed to aid manufacturers to cost effectively manufacture OSB. The first model is an OSB Mill Process Flow Model that defines the processing steps and the desired outcomes. The second model is an OSB Mill Model, an ExcelRTM based computer program, designed to answer operational what if and trade-off questions. The model is a spreadsheet representation of the OSB production process. The third model is an OSB Dryer Model that predicts the dryer outlet moisture content derived using a multivariate data analysis technique called projection to latent structures by means of Partial Least Squares (PLS). PLS was instrumental in identifying outlet temperature and heat source temperatures as the most influential dryer system variables in predicting dryer outlet moisture content. The SPC technique is Multivariate Statistical Process Control (MSPC) that uses multivariate scores or Hotelling T2 to determine the state of the drying process; and if the drying process is out of control, what process variables influenced the process shift

A study on improving the performance of control charts under non-normal distributions

Master'sMASTER OF ENGINEERIN

Processing and inferential methods to improve shaft-voltage-based condition monitoring of synchronous generators

This thesis focuses on improving shaft-voltage-based condition monitoring of synchronous generators. The work presents theory for describing and modelling shaft voltages using fundamental electromagnetic principles. A modern framework is adopted in developing an online, automated and intelligent fault-diagnosis system. Novel processing and inferential methods are used by the system to provide accurate and reliable incipient-fault detection and diagnosis. The literature shows that shaft-voltage analysis is recognised as a technique with potential for use in condition monitoring. However, deficiencies in the fundamental theory and the inadequacy of methods for extracting useful information has limited its widespread application. This work extends the knowledge of shaft voltages, validates the merits of its use for fault diagnosis, and provides methods for practical application. Validation of the model is completed using an experimental synchronous generator, and results indicate that simulated shaft voltages compare well with the measurements - i.e. total average error of the model combined with experimental uncertainty is below 16%. The fault detection and diagnosis components are tested separately and together as a complete shaft-voltage-based conditionmonitoring system in an experimental setting. Results indicate that the system can accurately diagnose faults and it represents a unique and valuable contribution to shaft-voltage-based condition monitoring. Additionally, techniques such as optimal measurement selection, multivariate model monitoring, and fault inference developed for the investigations and system presented in this thesis, will assist engineers and researchers working in the field of condition monitoring of electrical rotating machines

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes