30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Novel techniques for acoustic emission monitoring of fatigue fractures in landing gear.

Acoustic Emission (AE) is a technique for performing Non-Destructive Evaluation (NDE) of structures, whereby ultrasonic transducers are placed upon the surface of the structure in order to detect ultrasound resulting from damage-related events within the structure. Unlike many other NDE techniques, AE is an entirely passive process: the transducers operate in receive mode only. Advances in the sophistication of AE equipment and computer hardware in general mean that it is now possible to perform AE tests involving many sensors over a large structure whilst recording and storing every waveform received at every sensor. There is much interest in using this data to perform detailed analysis of structural integrity, particularly because AE testing is entirely non-invasive and can be largely automated. There are many hurdles to be overcome before AE can be routinely used in such a situation: the quantity of data is huge, and it is of a format which is unusual to most engineers - AE signals comprise short, transient, 'burst' like signals. A significant portion of this thesis is given to considering what to do with all the data, and how it may be understood. The thesis focusses on the development of a data processing method, and its application to landing gear certification tests. The methodology is designed to be generic, in that. it could find application in the on-line monitoring of a variety of engineering structures in the aerospace, automotive and civil-engineering sectors

The use of mechanical redundancy for fault detection in non-stationary machinery

The classical approach to machinery fault detection is one where a machinery’s condition is constantly compared to an established baseline with deviations indicating the occurrence of a fault. With the absence of a well-established baseline, fault detection for variable duty machinery requires the use of complex machine learning and signal processing tools. These tools require extensive data collection and expert knowledge which limits their use for industrial applications. The thesis at hand investigates the problem of fault detection for a specific class of variable duty machinery; parallel machines with simultaneously loaded subsystems. As an industrial case study, the parallel drive stations of a novel material haulage system have been instrumented to confirm the mechanical response similarity between simultaneously loaded machines. Using a table-top fault simulator, a preliminary statistical algorithm was then developed for fault detection in bearings under non-stationary operation. Unlike other state of the art fault detection techniques used in monitoring variable duty machinery, the proposed algorithm avoided the need for complex machine learning tools and required no previous training. The limitations of the initial experimental setup necessitated the development of a new machinery fault simulator to expand the investigation to include transmission systems. The design, manufacturing and setup of the various subsystems within the new simulator are covered in this manuscript including the mechanical, hydraulic and control subsystems. To ensure that the new simulator has successfully met its design objectives, extensive data collection and analysis has been completed and is presented in this thesis. The results confirmed that the developed machine truly represents the operation of a simultaneously loaded machine and as such would serve as a research tool for investigating the application of classical fault detection techniques to parallel machines in non-stationary operation.Master's These

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

Mantenimiento Predictivo: Historia, una guía de implementación y enfoques actuales

Debido al aumento del número de sensores utilizados en las plantas de producción, la posibilidad de obtener datos de estas ha incrementado considerablemente. Esto conlleva la posibilidad de detectar fallos antes de que estos ocurran y futuras paradas que afecten a las plantas de producción. Las tecnologías de mantenimiento predictivo permiten predecir eventos futuros, convirtiéndolas en herramientas para afrontar los retos que surjan en los mercados competitivos. Esta tesis está dividida en cinco partes. La primera, describe el mantenimiento a lo largo de la historia, mientras que la segunda está enfocada en el mantenimiento predictivo. El tercer punto es una guía de implementación de un programa de mantenimiento predictivo para cualquier organización interesada en el tema. Finalmente, las dos últimas partes hacen referencia a los enfoques más comunes en inteligencia artificial donde se explican técnicas importantes como “Artificial Neural Networks” y “Machine Learning”, describiendo algunos ejemplos donde fueron usadas para realizar mantenimiento predictivo.Departamento de Organización de Empresas y Comercialización e Investigación de MercadosHochschule Albstadt-SigmaringenGrado en Ingeniería en Organización Industria

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described