Non-linear minimum variance estimation for fault detection systems

A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully

Feature selection and categorization to design reliable fault detection systems.

International audienceIn this work, we will develop a fault detection system which is identified as a classification task. The classes are the nominal or malfunctioning state. To develop a decision system it is important to select among the data collected by the supervision system, only those carrying relevant information related to the decision task. There are two objectives presented in this paper, the first one is to use data mining techniques to improve fault detection tasks. For this purpose, feature selection algorithms are applied before a classifier to select which measures are needed for a fault detection system. The second objective is to use STRASS (STrong Relevant Algorithm of Subset Selection), which gives a useful feature categorization: strong relevant features, weak relevant and/or redundant ones. This feature categorization permits to design reliable fault detection system. The algorithm is tested on real benchmarks in medical diagnosis and fault detection. Our results indicate that a small number of measures can accomplish and perform the classification task and shown our algorithm ability to detect the correlated features. Furthermore, the proposed feature selection and categorization permits to design reliable and efficient fault detection system

A quick fault detection system applied to pitch actuators of wind turbines

The design of fast respond fault detection systems to wind turbines results an important subject and represents a notable challenger too. This paper presents a recent approach on a quick response fault detection system to pitch actuators in controlled wind turbines. The obtained time detection is about 10 seconds. Our scheme was possible by manipulating an adaptive parametric estimation block by varying the time scales among the actuator and the identification process dynamics. Additionally, numerical experiments are realized to support the main contribution.Postprint (published version

A quick fault detection system applied to pitch actuators of wind turbines

The design of fast respond fault detection systems to wind turbines results an important subject and represents a notable challenger too. This paper presents a recent approach on a quick response fault detection system to pitch actuators in controlled wind turbines. The obtained time detection is about 10 seconds. Our scheme was possible by manipulating an adaptive parametric estimation block by varying the time scales among the actuator and the identification process dynamics. Additionally, numerical experiments are realized to support the main contribution.Postprint (published version

Variability of indicators used in motor fault detection based on electrical measurements

Online condition assessment, product quality assurance and improved operational efficiency of engineering systems, such as induction motors, has increased in significance due to the advantages it offers in terms of productivity. Early detection of faults would not only allow for extensive trending but also provide advanced warnings regarding the health of the machinery. The implementation of on-line fault detection systems must not only exhibit high level of detection accuracy, but also discriminate between actual incipient faults and false alarms caused by temporal variations in operating conditions. The objective of this research is to develop the elements of a fault detection system suitable for continuous, on-line condition monitoring and assessment of 3

Robust Fault Detection of Switched Linear Systems with State Delays

This correspondence deals with the problem of robust fault detection for discrete-time switched systems with state delays under an arbitrary switching signal. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the robust fault detection filter such that, for unknown inputs, control inputs, and model uncertainties, the estimation error between the residuals and faults is minimized. The problem of robust fault detection is converted into an H infin-filtering problem. By a switched Lyapunov functional approach, a sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed method

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

Investigation of Motor Current Signature Analysis in Detecting Unbalanced Motor Windings of an Induction Motor with Sensorless Vector Control Drive

Maintaining the efficiency of AC motors in site equipment is important, given the increasing cost of energy. Reduction of motor efficiency from baseline manufacturer data can go undetected until total failure of the equipment is experienced. This paper introduces motor current signature analysis methods used to detect the early onset of motor efficiency reduction in AC motors controlled by modern Sensorless-Vector Variable Speed Control inverters. A step increase in the resistance of one stator winding is simulated in stages. Off-line processing of motor current data signals using data analysis methods developed for the MATLAB platform is used to identify imbalances caused by subtle stator resistance increases. Initial results indicate that small increases in stator resistances can be observed in the motor current signals received after data processing techniques have been used on the measured signals. The test results are presented herein along with details on the research work to be continued

Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions

Online fault detection based on typicality and eccentricity data analytics

Fault detection is a task of major importance in industry nowadays, since that it can considerably reduce the risk of accidents involving human lives, in addition to production and, consequently, financial losses. Therefore, fault detection systems have been largely studied in the past few years, resulting in many different methods and approaches to solve such problem. This paper presents a detailed study on fault detection on industrial processes based on the recently introduced eccentricity and typicality data analytics (TEDA) approach. TEDA is a recursive and non-parametric method, firstly proposed to the general problem of anomaly detection on data streams. It is based on the measures of data density and proximity from each read data point to the analyzed data set. TEDA is an online autonomous learning algorithm that does not require a priori knowledge about the process, is completely free of user- and problem-defined parameters, requires very low computational effort and, thus, is very suitable for real-time applications. The results further presented were generated by the application of TEDA to a pilot plant for industrial process