30 research outputs found
A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
In recent years, the digital transformation has been advancing in industrial companies,
supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence,
companies have large volumes of data and information that must be analyzed to give them competitive
advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive
Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as
Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid
Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory
Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed,
adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon
Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a
multinational company in the automotive industry sector. Two real-life datasets containing data
gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained
results show that HUEPs is a technique that supports the continuous monitoring of machines in order
to anticipate failures. This contribution to visual data analytics can help companies in decision-making,
regarding FD and PdM projects.The authors would like to thank the vehicle interiors manufacturer, Grupo Antolin, for its collaboration in this research
Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis
An efficient data reduction algorithm is designed and implemented on an industrial Internet of Things (IIoT) node for permanent magnet synchronous motor (PMSM) bearing fault diagnosis in variable speed conditions. Leakage flux and vibration signals are respectively acquired by a magnetic sensor and an accelerometer on the IIoT node in a non-invasive manner. These two signals are processed and mixed on the IIoT and transmitted to a server. The received signal is separated, the cumulative rotation angle is calculated, and the vibration signal is resampled for bearing fault identification. The proposed method can reduce about 95% of the transmission data while maintaining sufficient precision in bearing fault diagnosis in comparison with a traditional method. The proposed method based on edge computing reduces the power consumption, and hence it is suitable to use on a battery-supplied IIoT node for remote PMSM condition monitoring and fault diagnosis
Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview
Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic
Underground distribution cable incipient fault diagnosis system
This dissertation presents a methodology for an efficient, non-destructive, and online
incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they
become catastrophic. The system provides vital information to help the operator with the
decision-making process regarding the condition assessment of the underground cable. It
incorporates advanced digital signal processing and pattern recognition methods to classify
recorded data into designated classes. Additionally, the IFDS utilizes novel detection
methodologies to detect when the cable is near failure.
The classification functionality is achieved through employing an ensemble of rule-based
and supervised classifiers. The Support Vector Machines, designed and used as a supervised
classifier, was found to perform superior. In addition to the normalized energy features
computed from wavelet packet analysis, two new features, namely Horizontal Severity Index,
and Vertical Severity Index are defined and used in the classification problem.
The detection functionality of the IFDS is achieved through incorporating a temporal
severity measure and a detection method. The novel severity measure is based on the temporal
analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the
Global Severity Index (GSI). This index portrays the progressive degradation path of
underground cable as catastrophic failure time approaches. The detection approach utilizes the
numerical modeling capabilities of SOM as well as statistical change detection techniques. The
natural logarithm of the chronologically ordered minimum modeling errors, computed from
exposing feature vectors to a trained SOM, is used as the detection index. Three modified change
detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and
Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms
determine the change point or near failure time of cable from the instantaneous values of the
detection index.
Performance studies using field recorded data were conducted at three warning levels to
assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection
capability at each warning level. Specifically, it demonstrates that at least one detection
technique successfully provides an early warning that a fault is imminent
Pattern Recognition
Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition
Hybridization of machine learning for advanced manufacturing
Tesis por compendio de publicacioines[ES] En el contexto de la industria, hoy por hoy, los términos “Fabricación Avanzada”,
“Industria 4.0” y “Fábrica Inteligente” están convirtiéndose en una realidad. Las
empresas industriales buscan ser más competitivas, ya sea en costes, tiempo, consumo
de materias primas, energía, etc. Se busca ser eficiente en todos los ámbitos y además ser
sostenible. El futuro de muchas compañías depende de su grado de adaptación a los
cambios y su capacidad de innovación. Los consumidores son cada vez más exigentes,
buscando productos personalizados y específicos con alta calidad, a un bajo coste y no
contaminantes. Por todo ello, las empresas industriales implantan innovaciones
tecnológicas para conseguirlo.
Entre estas innovaciones tecnológicas están la ya mencionada Fabricación Avanzada
(Advanced Manufacturing) y el Machine Learning (ML). En estos campos se enmarca el
presente trabajo de investigación, en el que se han concebido y aplicado soluciones
inteligentes híbridas que combinan diversas técnicas de ML para resolver problemas en
el campo de la industria manufacturera. Se han aplicado técnicas inteligentes tales como
Redes Neuronales Artificiales (RNA), algoritmos genéticos multiobjetivo, métodos
proyeccionistas para la reducción de la dimensionalidad, técnicas de agrupamiento o
clustering, etc. También se han utilizado técnicas de Identificación de Sistemas con el
propósito de obtener el modelo matemático que representa mejor el sistema real bajo
estudio.
Se han hibridado diversas técnicas con el propósito de construir soluciones más robustas
y fiables. Combinando técnicas de ML específicas se crean sistemas más complejos y con
una mayor capacidad de representación/solución. Estos sistemas utilizan datos y el
conocimiento sobre estos para resolver problemas. Las soluciones propuestas buscan
solucionar problemas complejos del mundo real y de un amplio espectro, manejando
aspectos como la incertidumbre, la falta de precisión, la alta dimensionalidad, etc.
La presente tesis cubre varios casos de estudio reales, en los que se han aplicado diversas
técnicas de ML a distintas problemáticas del campo de la industria manufacturera. Los
casos de estudio reales de la industria en los que se ha trabajado, con cuatro conjuntos
de datos diferentes, se corresponden con:
• Proceso de fresado dental de alta precisión, de la empresa Estudio Previo SL.
• Análisis de datos para el mantenimiento predictivo de una empresa del sector de
la automoción, como es la multinacional Grupo Antolin.
Adicionalmente se ha colaborado con el grupo de investigación GICAP de la
Universidad de Burgos y con el centro tecnológico ITCL en los casos de estudio que
forman parte de esta tesis y otros relacionados.
Las diferentes hibridaciones de técnicas de ML desarrolladas han sido aplicadas y
validadas con conjuntos de datos reales y originales, en colaboración con empresas industriales o centros de fresado, permitiendo resolver problemas actuales y complejos.
De esta manera, el trabajo realizado no ha tenido sólo un enfoque teórico, sino que se ha
aplicado de modo práctico permitiendo que las empresas industriales puedan mejorar
sus procesos, ahorrar en costes y tiempo, contaminar menos, etc. Los satisfactorios
resultados obtenidos apuntan hacia la utilidad y aportación que las técnicas de ML
pueden realizar en el campo de la Fabricación Avanzada
Vibration Monitoring: Gearbox identification and faults detection
L'abstract è presente nell'allegato / the abstract is in the attachmen