Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors

Producción CientíficaThree-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.Consejo Nacional de Desarrollo Científico y Tecnológico - (processes 474290/2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0 and 405228/2016-3

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Entropy Analysis of Univariate Biomedical Signals:Review and Comparison of Methods

International audienc

Uncovering the Correlation between COVID-19 and Neurodegenerative Processes: Toward a New Approach Based on EEG Entropic Analysis

COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer’s disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigatio

Pronóstico de vida útil remanente en rodamientos con base en la estimación de la probabilidad de la degradación

La demanda de energía eléctrica por parte del sector industrial global solo crece, y se ve afectada fundamentalmente por el hecho que los rodamientos instalados en los motores eléctricos industriales se constituyen en el modo primario de fallo que afecta dicho consumo energético. Por tanto, la demanda de mantenimiento eficiente en motores eléctricos es crítica. Como solución, típicamente se ha empleado el mantenimiento preventivo como filosofía para la gestión de activos donde se busca maximizar la operación mediante inspecciones de rutina con mayor frecuencia cuando se exhiben anomalías, pero esto conlleva a un aumento en la probabilidad de falla debido a la intervención continua y el error humano inherente. Este documento de tesis doctoral presenta un marco integrado de diagnóstico y pronóstico para tratar con la vida útil remanente en rodamientos, con base en la estimación de la probabilidad de la degradación sujeta a modos de fallos definidos y severidades inducidas. Los enfoques metodológicos presentados incorporan análisis de vibración, para apoyar activamente el diagnóstico de fallos de forma no destructiva ni invasiva de máquinas rotativas en etapas tempranas, pero suponen un desafío con respecto a las propiedades de la señal, por ejemplo, su alto componente dinámico y de no estacionariedad. Se trabaja bajo una metodología que supone degradación de rodamientos evidenciada por una serie de estados discretos que representan efectivamente la dinámica y no estacionariedad del proceso de fallo. El conocimiento empírico previo también se incorpora dentro del sistema integrado para la clasificación de fallos y severidades. En definitiva, la metodología propuesta caracteriza diferentes firmas de falla en rodamientos empleando señales de vibración y varios dominios de representación de señales, con el propósito de tratar con la naturaleza estocástica y relaciones complejas en los datos concernientes a fallos y severidades. En la selección de características, se lleva a cabo un estudio sobre fusión y selección de dominios y características para la representación de señales, con el fin de discriminar la información relevante. Específicamente, aquí se presentan esquemas de fusión y selección basados en procedimientos de relevancia forward y backward, así como un enfoque estocástico de selección de características. Estas técnicas están destinadas a resaltar las características relevantes de múltiples dominios de las señales de vibración para las tareas de diagnóstico de fallos y evaluación de severidad, al mismo tiempo que se reduce la dimensionalidad de los datos. Para la etapa de entrenamiento, los enfoques se basan en sistemas estocásticos relacionados con la estimación de la probabilidad de un conjunto de estados discretos, tales como: Modelos Ocultos de Markov con observación discreta, Modelos Ocultos de Markov con observación continua y Modelos Ocultos de Markov Jerárquicos. El marco de diagnóstico y pronóstico integrado también se prueba como una herramienta de análisis de relevancia de características para discriminar múltiples condiciones de salud en rodamientos con caracterización multi-dominio. Los resultados logrados sobre una base de datos pública demuestran que los sistemas propuestos superan los algoritmos del estado-del-arte en cuanto a la cantidad de características seleccionadas y la eficiencia de la clasificación. Además, los resultados de las pruebas experimentales y los procedimientos de validación enseñan que el enfoque propuesto tiene la capacidad de proporcionar una advertencia de condiciones anormales del sistema mediante la identificación de las etapas tempranas de las condiciones de fallo. Las metodologías propuestas y técnicas analíticas desarrolladas en esta investigación para el pronóstico a largo plazo de la vida útil remanente se pueden aplicar en una gran variedad de contextos

FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

Symmetry and Complexity

Symmetry and complexity are the focus of a selection of outstanding papers, ranging from pure Mathematics and Physics to Computer Science and Engineering applications. This collection is based around fundamental problems arising from different fields, but all of them have the same task, i.e. breaking the complexity by the symmetry. In particular, in this Issue, there is an interesting paper dealing with circular multilevel systems in the frequency domain, where the analysis in the frequency domain gives a simple view of the system. Searching for symmetry in fractional oscillators or the analysis of symmetrical nanotubes are also some important contributions to this Special Issue. More papers, dealing with intelligent prognostics of degradation trajectories for rotating machinery in engineering applications or the analysis of Laplacian spectra for categorical product networks, show how this subject is interdisciplinary, i.e. ranging from theory to applications. In particular, the papers by Lee, based on the dynamics of trapped solitary waves for special differential equations, demonstrate how theory can help us to handle a practical problem. In this collection of papers, although encompassing various different fields, particular attention has been paid to the common task wherein the complexity is being broken by the search for symmetry

Improved railway vehicle inspection and monitoring through the integration of multiple monitoring technologies

The effectiveness and efficiency of railway vehicle condition monitoring is increasingly critical to railway operations as it directly affects safety, reliability, maintenance efficiency, and overall system performance. Although there are a vast number of railway vehicle condition monitoring technologies, wayside systems are becoming increasingly popular because of the reduced cost of a single monitoring point, and because they do not interfere with the existing railway line. Acoustic sensing and visual imaging are two wayside monitoring technologies that can be applied to monitor the condition of vehicle components such as roller bearing, gearboxes, couplers, and pantographs, etc. The central hypothesis of this thesis is that it is possible to integrate acoustic sensing and visual imaging technologies to achieve enhancement in condition monitoring of railway vehicles. So this thesis presents improvements in railway vehicle condition monitoring through the integration of acoustic sensing and visual imaging technologies