Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches

Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

A Novel Data-Driven Fault Tree Methodology for Fault Diagnosis and Prognosis

RÉSUMÉ : La thèse développe une nouvelle méthodologie de diagnostic et de pronostic de défauts dans un système complexe, nommée Interpretable logic tree analysis (ILTA), qui combine les techniques d’extraction de connaissances à partir des bases de données « knowledge discovery in database (KDD) » et l’analyse d’arbre de défaut « fault tree analysis (FTA) ». La méthodologie capitalise les avantages des deux techniques pour appréhender la problématique de diagnostic et de pronostic de défauts. Bien que les arbres de défauts offrent des modèles interprétables pour déterminer les causes possibles à l’origine d’un défaut, leur utilisation pour le diagnostic de défauts dans un système industriel est limitée, en raison de la nécessité de faire appel à des connaissances expertes pour décrire les relations de cause-à-effet entre les processus internes du système. Cependant, il sera intéressant d’exploiter la puissance d’analyse des arbres de défaut mais construit à partir des connaissances explicites et non biaisées extraites directement des bases de données sur la causalité des fautes. Par conséquent, la méthodologie ILTA fonctionne de manière analogue à la logique du modèle d'analyse d'arbre de défaut (FTA) mais avec une implication minimale des experts. Cette approche de modélisation doit rejoindre la logique des experts pour représenter la structure hiérarchique des défauts dans un système complexe. La méthodologie ILTA est appliquée à la gestion des risques de défaillance en fournissant deux modèles d'arborescence avancés interprétables à plusieurs niveaux (MILTA) et au cours du temps (ITCA). Le modèle MILTA est conçu pour accomplir la tâche de diagnostic de défaillance dans les systèmes complexes. Il est capable de décomposer un défaut complexe et de modéliser graphiquement sa structure de causalité dans un arbre à plusieurs niveaux. Par conséquent, un expert est en mesure de visualiser l’influence des relations hiérarchiques de cause à effet menant à la défaillance principale. De plus, quantifier ces causes en attribuant des probabilités aide à comprendre leur contribution dans l’occurrence de la défaillance du système. Le modèle ITCA est conçu pour réaliser la tâche de pronostic de défaillance dans les systèmes complexes. Basé sur une répartition des données au cours du temps, le modèle ITCA capture l’effet du vieillissement du système à travers de l’évolution de la structure de causalité des fautes. Ainsi, il décrit les changements de causalité résultant de la détérioration et du vieillissement au cours de la vie du système.----------ABSTRACT : The thesis develops a new methodology for diagnosis and prognosis of faults in a complex system, called Interpretable logic tree analysis (ILTA), which combines knowledge extraction techniques from knowledge discovery in databases (KDD) and the fault tree analysis (FTA). The methodology combined the advantages of the both techniques for understanding the problem of diagnosis and prognosis of faults. Although fault trees provide interpretable models for determining the possible causes of a fault, its use for fault diagnosis in an industrial system is limited, due to the need for expert knowledge to describe cause-and-effect relationships between internal system processes. However, it will be interesting to exploit the analytical power of fault trees but built from explicit and unbiased knowledge extracted directly from databases on the causality of faults. Therefore, the ILTA methodology works analogously to the logic of the fault tree analysis model (FTA) but with minimal involvement of experts. This modeling approach joins the logic of experts to represent the hierarchical structure of faults in a complex system. The ILTA methodology is applied to failure risk management by providing two interpretable advanced logic models: a multi-level tree (MILTA) and a multilevel tree over time (ITCA). The MILTA model is designed to accomplish the task of diagnosing failure in complex systems. It is able to decompose a complex defect and graphically model its causal structure in a tree on several levels. As a result, an expert is able to visualize the influence of hierarchical cause and effect relationships leading to the main failure. In addition, quantifying these causes by assigning probabilities helps to understand their contribution to the occurrence of system failure. The second model is a logical tree interpretable in time (ITCA), designed to perform the task of prognosis of failure in complex systems. Based on a distribution of data over time, the ITCA model captures the effect of the aging of the system through the evolution of the fault causation structure. Thus, it describes the causal changes resulting from deterioration and aging over the life of the system

Cognitive alterations in Multiple Sclerosis patients: diagnostic, prognostic, and rehabilitation aspects

Cognitive impairment is frequent in most patients with Multiple Sclerosis (MS) and affects several cognitive domains, having a significant impact on their quality of life and on their personal, social and work dimensions. An early and comprehensive neuropsychological assessment may provide relevant diagnostic, prognostic, and rehabilitative implications. The first chapter highlights the diagnostic and the prognostic aspects, with the description of a multicentric project, conducted in collaboration with MS centers of Bergamo, Montichiari, and Modena, in which were included newly-diagnosed MS patients and were evaluated their neurological, neuropsychological, neuroradiological and bioumoral outcomes. Results of this project have allowed the preparation of several sub-studies with important results: the first study highlighted how MS patients at the time of diagnosis, even in the absence of an evident cognitive impairment as clinically defined, are characterized by slight cognitive alterations as compared to healthy controls, both considering global cognitive functioning level and also specific cognitive domains. The second study has allowed the identification of two biomarkers present in the cerebrospinal fluid that are associated with cognitive alterations: the first (LIGHT) is associated with the inflammatory phase of the disease, while the second (parvalbumin) is associated with the neurodegenerative phase of the disease and also correlates with cortical thinning and physical disability, moreover with a stronger association compared to the one found with the level of neurofilament light chain (NF-L, a well-known biomarker of neurodegeneration). The third study has allowed to describe the predictive role of some inflammatory cytokines in the cerebrospinal fluid (CXCL13, CXCL12, IFNγ, TNF, TWEAK, LIGHT, sCD163) in discriminating, since the time of diagnosis, those MS patients that were more likely to develop neurologic and neuroradiologic worsening after 4-years follow-up. The second chapter addresses the importance of assessing MS patients not only with the classical neuropsychological tests but also with experimental paradigms. The first study, conducted in collaboration with the University of Florence and the University of Padua, investigated the phenomena of false memories, using a paradigm that induces memory distortions due to the strong connection between words associated with a same semantic category. Results showed that MS patients were not characterized by the expected memory distortions, probably due to weak association between nodes that compose semantic memory, because of neurodegenerative events. The second study, conducted in collaboration with the Kessler Foundation (West Orange, NJ, USA), focused on social cognition abilities: in a group of MS patients without evidence of cognitive impairment as traditionally defined was observed a performance significantly lower compared to healthy controls in tests of facial emotion recognition, theory of mind, and empathy. Moreover, it was demonstrated that these social cognition alterations were correlated specifically with the cortical lesions volume in both the amygdalae of MS patients, while no significant correlation was found with other measures of brain damage included in the study (cortical thickness and cortical lesion load in all the cerebral cortex). The third and last chapter focuses on the rehabilitative aspects, showing results from a study carried at the Buffalo Neuroimaging Analysis Center (Buffalo, NY, USA) on a group of MS patients that performed a cognitive training by using a telerehabilitation approach. The project aimed to identify neurological, psychological and neuroradiological variables able to characterize patients that can benefit more from the rehabilitation. Results showed that a relapsing-remitting disease phenotype (as compared with progressive patients), a higher personality trait of conscientiousness, a higher gray matter volume, a lower tract disruption in a network centered on precuneus and posterior cingulate, and a higher deviation in functional brain connectivity compared to healthy controls, play a key role to achieve a greater cognitive amelioration after the rehabilitative treatment

Design Optimization of Wind Energy Conversion Systems with Applications

Modern and larger horizontal-axis wind turbines with power capacity reaching 15 MW and rotors of more than 235-meter diameter are under continuous development for the merit of minimizing the unit cost of energy production (total annual cost/annual energy produced). Such valuable advances in this competitive source of clean energy have made numerous research contributions in developing wind industry technologies worldwide. This book provides important information on the optimum design of wind energy conversion systems (WECS) with a comprehensive and self-contained handling of design fundamentals of wind turbines. Section I deals with optimal production of energy, multi-disciplinary optimization of wind turbines, aerodynamic and structural dynamic optimization and aeroelasticity of the rotating blades. Section II considers operational monitoring, reliability and optimal control of wind turbine components

Black-Swan Type Catastrophes and Antifragility/Supra-resilience of Urban Socio-Technical Infrastructures

This paper may be one of the first attempts dealing with the problem of creating, providing and maintaining antifragility of systems of interdependent urban critical infrastructures (CI) in the wake of black-swan type technological, ecological, economic or social catastrophes occurring in a municipality. A synonym is offered to describe antifragility from a positive psychology perspective, formulating the problem as the supraresilience problem. A brief description is given of the developed innovative approach for creating a supraresilient city/region using black-swan catastrophe and the antifragility concepts. Resilience metrics are formulated as well as methods of assessing damage, interdependence of infrastructures and convergent technologies and sciences needed for practical regional resilience and risk management of the system of systems (SoS) of interdependent urban critical infrastructures). © Published under licence by IOP Publishing Ltd

Design Optimization of Wind Energy Conversion Systems with Applications

Modern and larger horizontal-axis wind turbines with power capacity reaching 15 MW and rotors of more than 235-meter diameter are under continuous development for the merit of minimizing the unit cost of energy production (total annual cost/annual energy produced). Such valuable advances in this competitive source of clean energy have made numerous research contributions in developing wind industry technologies worldwide. This book provides important information on the optimum design of wind energy conversion systems (WECS) with a comprehensive and self-contained handling of design fundamentals of wind turbines. Section I deals with optimal production of energy, multi-disciplinary optimization of wind turbines, aerodynamic and structural dynamic optimization and aeroelasticity of the rotating blades. Section II considers operational monitoring, reliability and optimal control of wind turbine components