On extending process monitoring and diagnosis to the electrical and mechanical utilities: an advanced signal analysis approach

This thesis is concerned with extending process monitoring and diagnosis to electrical and mechanical utilities. The motivation is that the reliability, safety and energy efficiency of industrial processes increasingly depend on the condition of the electrical supply and the electrical and mechanical equipment in the process. To enable the integration of electrical and mechanical measurements in the analysis of process disturbances, this thesis develops four new signal analysis methods for transient disturbances, and for measurements with different sampling rates. Transient disturbances are considered because the electrical utility is mostly affected by events of a transient nature. Different sampling rates are considered because process measurements are commonly sampled at intervals in the order of seconds, while electrical and mechanical measurements are commonly sampled with millisecond intervals. Three of the methods detect transient disturbances. Each method progressively improves or extends the applicability of the previous method. Specifically, the first detection method does univariate analysis, the second method extends the analysis to a multivariate data set, and the third method extends the multivariate analysis to measurements with different sampling rates. The fourth method developed removes the transient disturbances from the time series of oscillatory measurements. The motivation is that the analysis of oscillatory disturbances can be affected by transient disturbances. The methods were developed and tested on experimental and industrial data sets obtained during industrial placements with ABB Corporate Research Center, Kraków, Poland and ABB Oil, Gas and Petrochemicals, Oslo, Norway. The concluding chapters of the thesis discuss the merits and limitations of each method, and present three directions for future research. The ideas should contribute further to the extension of process monitoring and diagnosis to the electrical and mechanical utilities. The ideas are exemplified on the case studies and shown to be promising directions for future research.Open Acces

Characterization of Closed-loop Process Variable Data

Business analysis methods for controller performance assessment are implemented using statistical process control (SPC) and "six-sigma" principles. This work focuses on the characterization closed-loop archived data primarily for use in SPC-based analysis for controller performance assessment. Closed-loop data sets for the advanced process control manipulated variables (APC-MVs) exhibit different levels of variability when considered over a one year period. These periods of variability are termed as "error variability bands." This thesis presents four error variability band identification techniques using general purpose statistical tools including histograms, normal probability plots, quantile-quantile plots and the sample autocorrelation function. The performance of these methods is presented using archived refinery data reconstructed on a one-minute sample period for flow, pressure, and temperature loops. The impact of set-point variability on APC manipulated variables is also illustrated.School of Chemical Engineerin

Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information

Production plants used in modern process industry must produce products that meet stringent environmental, quality and profitability constraints. In such integrated plants, non-linearity and strong process dynamic interactions among process units complicate root-cause diagnosis of plant-wide disturbances because disturbances may propagate to units at some distance away from the primary source of the upset. Similarly, implemented advanced process control strategies, backup and recovery systems, use of recycle streams and heat integration may hamper detection and diagnostic efforts. It is important to track down the root-cause of a plant-wide disturbance because once corrective action is taken at the source, secondary propagated effects can be quickly eliminated with minimum effort and reduced down time with the resultant positive impact on process efficiency, productivity and profitability. In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to incorporate and utilize knowledge about the overall process topology or interrelated physical structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs). Traditionally, process control engineers have intuitively referred to the physical structure of the plant by visual inspection and manual tracing of fault propagation paths within the process structures, such as the process drawings on printed P&IDs, in order to make logical conclusions based on the results from data-driven analysis. This manual approach, however, is prone to various sources of errors and can quickly become complicated in real processes. The aim of this thesis, therefore, is to establish innovative techniques for the electronic capture and manipulation of process schematic information from large plants such as refineries in order to provide an automated means of diagnosing plant-wide performance problems. This report also describes the design and implementation of a computer application program that integrates: (i) process connectivity and directionality information from intelligent P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii) process know-how to aid process control engineers and plant operators gain process insight. This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor independent text-based XML description of the plant. The XML output was processed by a software tool developed in Microsoft® .NET environment in this research project to computationally generate connectivity matrix that shows plant items and their connections. The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis for other application and has served as precursor to other research work. The final version of the developed software tool links statistical results of cause-and-effect analysis of process data with the connectivity matrix to simplify and gain insights into the cause and effect analysis using the connectivity information. Process knowhow and understanding is incorporated to generate logical conclusions. The thesis presents a case study in an atmospheric crude heating unit as an illustrative example to drive home key concepts and also describes an industrial case study involving refinery operations. In the industrial case study, in addition to confirming the root-cause candidate, the developed software tool was set the task to determine the physical sequence of fault propagation path within the plant. This was then compared with the hypothesis about disturbance propagation sequence generated by pure data-driven method. The results show a high degree of overlap which helps to validate statistical data-driven technique and easily identify any spurious results from the data-driven multivariable analysis. This significantly increase control engineers confidence in data-driven method being used for root-cause diagnosis. The thesis concludes with a discussion of the approach and presents ideas for further development of the methods

Jätevedenpuhdistamojen prosessinohjauksen ja operoinnin kehittäminen data-analytiikan avulla: esimerkkejä teollisuudesta ja kansainvälisiltä puhdistamoilta

Instrumentation, control and automation are central for operation of municipal wastewater treatment plants. Treatment performance can be further improved and secured by processing and analyzing the collected process and equipment data. New challenges from resource efficiency, climate change and aging infrastructure increase the demand for understanding and controlling plant-wide interactions. This study aims to review what needs, barriers, incentives and opportunities Finnish wastewater treatment plants have for developing current process control and operation systems with data analytics. The study is conducted through interviews, thematic analysis and case studies of real-life applications in process industries and international utilities. Results indicate that for many utilities, additional measures for quality assurance of instruments, equipment and controllers are necessary before advanced control strategies can be applied. Readily available data could be used to improve the operational reliability of the process. 14 case studies of advanced data processing, analysis and visualization methods used in Finnish and international wastewater treatment plants as well as Finnish process industries are reviewed. Examples include process optimization and quality assurance solutions that have proven benefits in operational use. Applicability of these solutions for identified development needs is initially evaluated. Some of the examples are estimated to have direct potential for application in Finnish WWTPs. For other case studies, further piloting or research efforts to assess the feasibility and cost-benefits for WWTPs are suggested. As plant operation becomes more centralized and outsourced in the future, need for applying data analytics is expected to increase.Prosessinohjaus- ja automaatiojärjestelmillä on keskeinen rooli modernien jätevedenpuhdistamojen operoinnissa. Prosessi- ja laitetietoa paremmin hyödyntämällä prosessia voidaan ohjata entistä tehokkaammin ja luotettavammin. Kiertotalous, ilmastonmuutos ja infrastruktuurin ikääntyminen korostavat entisestään tarvetta ymmärtää ja ohjata myös eri osaprosessien välisiä vuorovaikutuksia. Tässä työssä tarkastellaan tarpeita, esteitä, kannustimia ja mahdollisuuksia kehittää jätevedenpuhdistamojen ohjausta ja operointia data-analytiikan avulla. Eri sidosryhmien näkemyksiä kartoitetaan haastatteluilla, joiden tuloksia käsitellään temaattisen analyysin kautta. Löydösten perusteella potentiaalisia ratkaisuja kartoitetaan suomalaisten ja kansainvälisten puhdistamojen sekä prosessiteollisuuden jo käyttämistä sovelluksista. Löydökset osoittavat, että monilla puhdistamoilla tarvitaan nykyistä merkittävästi kattavampia menetelmiä instrumentoinnin, laitteiston ja ohjauksen laadunvarmistukseen, ennen kuin edistyneempien prosessinohjausmenetelmien käyttöönotto on mahdollista. Operoinnin toimintavarmuutta ja luotettavuutta voitaisiin kehittää monin tavoin hyödyntämällä jo kerättyä prosessi- ja laitetietoa. Työssä esitellään yhteensä 14 esimerkkiä puhdistamoilla ja prosessiteollisuudessa käytössä olevista prosessinohjaus- ja laadunvarmistusmenetelmistä. Osalla ratkaisuista arvioidaan sellaisenaan olevan laajaa sovelluspotentiaalia suomalaisilla jätevedenpuhdistamoilla. Useiden ratkaisujen käyttöönottoa voitaisiin edistää pilotoinnilla tai jatkotutkimuksella potentiaalisten hyötyjen ja kustannusten arvioimiseksi. Jo kerättyä prosessi- ja laitetietoa hyödyntävien ratkaisujen kysynnän odotetaan tulevaisuudessa lisääntyvän, kun puhdistamojen operointi keskittyy ja paineet kustannus- ja energiatehokkuudelle kasvavat

New methods for control loop performance monitoring and assesment

Tato disertační práce se zabývá problematikou monitorování a ohodnocování kvality řízení v jednoduchých regulačních smyčkách s PID regulátory. Cílem bylo vyvinout spolehlivé a efektivní algoritmy, které komplexně pokrývají tuto problematiku především v oblasti řízení průmyslových procesů a jsou schopné praktického nasazení. Dosažené teoretické výsledky jsou popsány ve třech kapitolách, které se věnují metodám automatického ladění PID regulátorů, ohodnocování regulačních smyček a identifikace řízeného systému v uzavřené smyčce. Představené metody byly otestovány jak v simulačním prostředí, tak na reálných laboratorních modelech.NeobhájenoThis thesis deals with assessment and monitoring of simple PID loop control quality. The goal was to develop reliable and effective algorithms for complex solution of selected problem in process control industry. Achieved theoretical results are described in three chapters dedicated to automatic PID tuning, control loop performance assessment and closed-loop system identification methods. The introduced methods were extensively tested both in simulation and real hardware environment

Subspace based data-driven designs of fault detection systems

The thesis focuses on advanced methods of fault detection and diagnosis suitable for application in large-scale processes. The theory of fault diagnosis mainly comprises development of mathematical models for observing critical changes in the process under consideration. The so-called residual signal is used for the purpose of detecting abnormal events and diagnosing their nature. For large-scale processes, it is difficult to build their models mathematically. Therefore, very often historical data from regular sensor measurements, event-logs and records are used to directly identify relationship between plant's input and output. On these lines, the thesis presents a data-driven design of fault detection systems which reduces the computation burden by identifying only the key components and not the entire process model itself. The novel design method is also studied within the context of parameter varying systems. Since many processes undergo temporary fluctuation of their crucial parameters, which can not be ruled out as faults, the fault detection system must be able to adapt to these changes. This is realized in the thesis with two efficient algorithms, which are based on recursive identification techniques. The theoretical contribution in this thesis also revolves around improvising the novel data-drive design of fault detection systems. In other words, the identification procedure is optimized by reformulating it as “closed-loop” identification or identification of Kalman filter. Also, the algorithm is numerically optimized by using QR based decomposition technique. The thesis also presents application results of different algorithms derived in this work. As benchmarks, the Tennessee Eastman chemical plant and the continuously stirred tank heater are considered. The novel algorithms are compared with the existing popular techniques from the literature.Die Arbeit konzentriert sich auf fortgeschrittene Methoden zur Fehlererkennung und Diagnose für den Einsatz in Mehrgrößen Systemen. Üblicherweise umfasst die Fehlerdiagnose Entwicklung von mathematischen Modellen zur Beobachtung der Veränderungen in den ursprünglichen Prozessen. Dabei wird ein so genanntes Residuensignal zur von Fehlern benutzt, welches im Fehlerfall einen Ausschlag zeigt. Für Mehrgrößen Systeme, ist es im Allgemeinen schwierig, mathematische Modelle zu erstellen, die mathematisch abgeleitet werden können. Deshalb werden Daten aus dem Prozess, z.B. aus regelmäßigen Messungen, Event-Logs oder Records verwendet, um Beziehungen zwischen Prozess-Eingang und Ausgang abzubilden. Davon ausgehend werden in der vorliegenden Arbeit Verfahren entwickelt um ein Datenbasiertes Fehlererkennungssystem zu generieren, welches ohne Modelidentifikation arbeitet. In dieser Arbeit wird das Problem der Datenbasierten Fehlererkennung weiter im Rahmen der so genannten Parameter Varianten Systeme untersucht. Da viele Prozesse vorübergehenden Parameterschwankungen unterliegen, die nicht als Fehler ausgeschlossen werden können, muss das Fehlererkennung System in der Lage sein, die Veränderungen zu adaptieren. Ein solches lernendes Fehlererkennungssystem ist hier an Hand von zwei effizienten Algorithmen und mit rekursiver Identifikation realisiert. Der Beitrag in dieser Arbeit ist auch ein modifiziertes, optimales Subraum Identifikation basiertes Entwurf. Darüber hinaus wird das Identifikationsverfahren auf die Hauptkomponenten beschränkt und das ursprüngliche Problem wird für die optimale Parameterschätzung als „Closed-Loop“ Identifikation oder Identifikation des Kalman Filters umformuliert. Die gesamte Konstruktion ist numerisch über eine QR Zerlegung numerisch optimiert. Die Arbeit stellt auch Ergebnisse der Applikation verschiedener Algorithmen vor. Als Versuchstand wurden das Tennessee Eastman Prozess und eine kontinuierlich gerührte Tankheizung verwendet. Die Algorithmen dieser Arbeit werden mit dem ursprünglichen und anderen Identifikationsverfahren verglichen

Key Performance Monitoring and Diagnosis in Industrial Automation Processes

With ever increasing global competition, monitoring and diagnosis methods based on key performance indicator (KPI) are increasingly receiving attention in the process industry. Primarily due to the scale and complexity of modern automation processes, application of signal processing and model-based monitoring methods is too costly and time-consuming. On the other hand, due to the availability of cheap measurement and storage systems, a large amount of process and KPI data is obtained. As a result, developing data-driven KPI monitoring methods has become an area of great interest in both academics and industry. Therefore, this thesis is focused on the data-driven design of systematic KPI monitoring and diagnosis systems for industrial automation processes. Depending on the relationship between the low-level process variables and the high-level KPIs, industrial processes can be classified into three groups: 1. Static processes (SPs) are those described by algebraic equations; 2. Lumped-parameter processes (LPPs) are those described by ordinary differential equations; and 3. Distributed-parameter processes (DPPs) are those described by partial differential equations. For each of these groups of processes, analytical redundancy plays a very important role when developing efficient process monitoring tools. For SPs, multivariate-statistics-based methods have been used. However, their applicability is restricted by high mathematical complexity, high design costs and low diagnostic performance. For this reason, an alternative improved method has been proposed in this thesis. For LPPs, complex model-based methods have been implemented. Therefore, to reduce the design costs required for monitoring LPPs, efficient Subspace identification based approaches are presented. Finally, since there are very few available model-based methods for DPPs, this thesis presents novel approaches for KPI monitoring in DPPs. For all these methods, the design procedures are based on the process I/O data and do not require advanced mathematical knowledge. After performance degradation has been detected, it is important to identify the root causes to prevent further losses. In industrial processes, performance degradation is more often caused by multiplicative faults. In this work, a new data-driven multiplicative fault diagnosis approach is proposed. This approach aims at assisting the maintenance personnel by narrowing down the investigation scope. As a result, overall equipment effectiveness (OEE) can be significantly improved. To show the effectiveness of the proposed approaches, case studies on the Tennessee Eastman benchmark process, the continuous stirred tank heater benchmark and the simulated drying section of a paper machine have been performed. The proposed methods worked successfully with these processes.Key Performance Überwachung und Diagnose in industriellen Automatisierungsprozessen Im Rahmen einer stetigen Zunahme des globalen Wettbewerbs erhalten Key Performance Indikator (KPI) basierte Überwachungs- und Diagnosetechniken zunehmend Aufmerksamkeit in der Prozessindustrie. Vor allem vor dem Hintergrund von Umfang und Komplexität moderner Automatisierungsprozesse ist die Anwendung von Signalverarbeitung und modellbasierten Überwachungstechniken zu teuer und zu zeitaufwendig. Andererseits ist häufig auf Grund der Verfügbarkeit von günstigen Mess- und Speichersystemen, eine große Menge von Prozess- und KPI-Daten vorhanden. Daher ist die Entwicklung von datenbasierten Verfahren ein Forschungsfeld, welches sowohl im akademischen als auch im industriellen Bereich mit großem Interesse verfolgt wird. Dementsprechend liegt der Fokus der vorliegenden Arbeit auf einem systematischen und datenbasierten Entwurf von KPI-Überwachungs- und -Diagnosesystemen für industrielle Automatisierungsprozesse. Anhand der Beziehung zwischen den low-level Prozessgrößen und den high-level KPIs können industrielle Prozesse in drei Gruppen eingeteilt werden: 1. Statische Prozesse (SP) sind Prozesse, die sich durch algebraische Gleichungen beschrieben lassen; 2. Konzentrierte-Parameter Prozesse (KPP) sind Prozesse, welche durch gewöhnliche Differentialgleichungen beschrieben werden; und 3. Verteilte-Parameter Prozesse (VPP) sind Prozesse, welche durch partielle Differentialgleichungen beschrieben werden. Für jede dieser Gruppen spielt das Konzept der analytischen Redundanz eine sehr wichtige Rolle bei der Entwicklung von effizienten Prozessüberwachungs-Tools. Für SP, sind multivariate statistische Verfahren verwendet worden. Allerdings ist deren Anwendbarkeit durch hohe mathematische Komplexität, einen hohen Entwurfsaufwand und eine geringen Diagnoseleistung beschränkt. Aus diesem Grund wird ein alternatives, verbessertes Verfahren in dieser Arbeit vorgeschlagen. Für KPP, sind komplexe modellbasierte Methoden implementiert worden. Um die Entwicklungskosten für die Überwachung der KPP zu reduzieren, wird eine effiziente Methode, basierend auf Subspace-Identifikation, vorgestellt. Da es nur sehr wenige modellbasierte Methoden für VPP gibt, präsentiert diese Arbeit schließlich neue Verfahren für die KPI- Überwachung in VPP. Alle vorgestellten Verfahren basieren auf den Prozess E/A Daten und erfordern daher keine tiefergehenden mathematischen Kenntnisse über den Prozess. Nach erfolgreicher Erkennung des Leistungsabfalls eines KPI, ist es in einem nächsten Schritt erforderlich die Ursache zu identifizieren, um weitere ökonomische Verluste zu verhindern. In industriellen Prozessen wird ein Leistungsabfall häufig durch multiplikative Fehler verursacht. In dieser Arbeit wird ein neues datenbasiertes, multiplikatives Fehlerdiagnoseverfahren vorgeschlagen. Dieses Verfahren soll der Unterstützung des Wartungspersonals dienen, indem eine Eingrenzung der Problemursache vorgenommen wird. Als Ergebnis kann somit die OEE (Overall Equipment Effectiveness) deutlich verbessert werden. Um die Wirksamkeit der vorgeschlagenen Verfahren zu demonstrieren, wurden verschiedene Fallstudien an Hand des „Tennessee Eastman“ Benchmark, des „continuous stirred tank heater“ Benchmark und einer simulierten Trockenpartie einer Papiermaschine durchgeführt. Die Effektivität der vorgeschlagenen Methoden konnte an Hand der aufgeführten Benchmark Prozesse erfolgreich gezeigt werden