Download or read book Data driven Approaches for Condition Monitoring and Predictive Analytics written by Abdallah Adnan Chehade and published by . This book was released on 2017 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid development of sensing and communication technologies has enabled an unprecedented opportunity for condition monitoring, making multiple data streams a commonplace to simultaneously monitor the health status of an operating unit. Such a big data environment poses essential challenges in determining (i) which data streams to use; and (ii) how to fuse/combine those multiple and relevant data streams for better failure diagnosis and prognostics as these multiple data streams are often correlated and each data stream may only contain partial information about the degraded unit. However, it is often hard to physically interpret the dependencies and relations between these data streams due to the complexity of the system. Given the massive amount of data have become available, nowadays many research companies are looking for effective tools to improve failure monitoring and predictive capabilities. As a consequence, my research focuses on developing effective data-driven methodologies to better monitor and infer the condition of an operating unit in real time. Such inference would be very useful for profitable managerial decision-making such as condition-based maintenance scheduling, work in progress distribution, shipment scheduling, and customer satisfaction. This thesis contributes to the field of System Informatics and Data Analytics (SIDA) by developing systematic data-driven methodologies for better condition monitoring and prognostic analysis in complex systems. These developed methodologies enable (i) real time modeling and characterization of the health status of a system, (ii) predicting future measurements, trends and behaviors of the system, and (iii) further diagnosing the reasons for degradation and failure of the system. This research combines advanced statistical methods, data analytics tools, engineering knowledge, and decision science and operations research. The research is highly applicable in many applications such as health care, manufacturing, after sales and services.

Download or read book Data Driven Technology for Engineering Systems Health Management written by Gang Niu and published by Springer. This book was released on 2016-07-27 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

Download or read book Predictive Maintenance in Smart Factories written by Tania Cerquitelli and published by Springer Nature. This book was released on 2021-08-26 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the outcome of the European project "SERENA", involving fourteen partners as international academics, technological companies, and industrial factories, addressing the design and development of a plug-n-play end-to-end cloud architecture, and enabling predictive maintenance of industrial equipment to be easily exploitable by small and medium manufacturing companies with a very limited data analytics experience. Perspectives and new opportunities to address open issues on predictive maintenance conclude the book with some interesting suggestions of future research directions to continue the growth of the manufacturing intelligence.

Download or read book Data Driven Remaining Useful Life Prognosis Techniques written by Xiao-Sheng Si and published by Springer. This book was released on 2017-01-20 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.

Download or read book Data driven Prognosis and Diagnosis of Event Occurrences with Applications in Manufacturing and Healthcare Systems written by and published by . This book was released on 2016 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information technology revolution is turning modern engineering systems into smart and connected systems and such systems have become increasingly available in practice. Due to the advances in implementation of smart and connected systems, we now have massive data with rich condition monitoring signals of in-situ systems and detailed records of critical events. This unprecedented data availability realized by the smart and connected systems provides significant opportunities for sophisticated data-driven prognosis and diagnosis for the underlying health status of a system in various fields. Successful prognosis and diagnosis can prevent catastrophic consequences in advance and provide meaningful information about the underlying health status of a system. However, at the same time, it also creates new challenges for research in data analytics as to how this vast and complex data could be utilized to retrieve accurate prognosis and meaningful diagnosis. Many existing techniques fall short of addressing this issue because most of them are for the cases where the data were collected in a well-controlled experimental setting. The critical event records and condition monitoring data obtained from the complex smart and connected systems often involve many factors that are uncontrollable and inevitably exhibit severe heterogeneity. This thesis addresses multiple challenges for prognosis and diagnosis based on such data by establishing a series of data-driven methodologies. (a) To build a joint model framework for both time-to-failure data and condition monitoring signals by integrating Cox regression and mixed-effects model. (b) To extend the joint model framework to address various issues in the prognosis based on the monitoring data. (c) Establishing a joint prognostic model for recurrent events by hierarchically integrating logistic regression and mixed-effects models. (d) To establish a diagnostic model based on recurrent event data using correlated Gamma-based hidden Markov model. The proposed methods can be applied to a broad range of data analytics applications, and the emerging challenges in monitoring data obtained from smart and connected systems can be effectively addressed.

Download or read book IoT Streams for Data Driven Predictive Maintenance and IoT Edge and Mobile for Embedded Machine Learning written by Joao Gama and published by Springer Nature. This book was released on 2021-01-09 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.

Download or read book Structural Health Monitoring Based on Data Science Techniques written by Alexandre Cury and published by Springer Nature. This book was released on 2021-10-23 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.

Download or read book Data driven Prognosis and Diagnosis of Event Occurrences with Applications in Manufacturing and Healthcare Systems written by Junbo Son and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information technology revolution is turning modern engineering systems into smart and connected systems and such systems have become increasingly available in practice. Due to the advances in implementation of smart and connected systems, we now have massive data with rich condition monitoring signals of in-situ systems and detailed records of critical events. This unprecedented data availability realized by the smart and connected systems provides significant opportunities for sophisticated data-driven prognosis and diagnosis for the underlying health status of a system in various fields. Successful prognosis and diagnosis can prevent catastrophic consequences in advance and provide meaningful information about the underlying health status of a system. However, at the same time, it also creates new challenges for research in data analytics as to how this vast and complex data could be utilized to retrieve accurate prognosis and meaningful diagnosis. Many existing techniques fall short of addressing this issue because most of them are for the cases where the data were collected in a well-controlled experimental setting. The critical event records and condition monitoring data obtained from the complex smart and connected systems often involve many factors that are uncontrollable and inevitably exhibit severe heterogeneity. This thesis addresses multiple challenges for prognosis and diagnosis based on such data by establishing a series of data-driven methodologies. (a) To build a joint model framework for both time-to-failure data and condition monitoring signals by integrating Cox regression and mixed-effects model. (b) To extend the joint model framework to address various issues in the prognosis based on the monitoring data. (c) Establishing a joint prognostic model for recurrent events by hierarchically integrating logistic regression and mixed-effects models. (d) To establish a diagnostic model based on recurrent event data using correlated Gamma-based hidden Markov model. The proposed methods can be applied to a broad range of data analytics applications, and the emerging challenges in monitoring data obtained from smart and connected systems can be effectively addressed.

Download or read book Intelligent Quality Assessment of Railway Switches and Crossings written by Roberto Galeazzi and published by Springer Nature. This book was released on 2021-03-04 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the latest scientific and technological advancements in the field of railway turnout engineering. It offers a holistic approach to the scientific investigation of the factors and mechanisms determining performance degradation of railway switches and crossings (S&Cs), and the consequent development of condition monitoring systems that will enable infrastructure managers to transition towards the implementation of predictive maintenance. The book is divided into three distinct parts. Part I discusses the modelling of railway infrastructure, including switch and crossing systems, while Part II focuses on metallurgical characterization. This includes the microstructure of in-field loaded railway steel and an analysis of rail screw failures. In turn, the third and final part discusses condition monitoring and asset management. Given its scope, the book is of interest to both academics and industrial practitioners, helping them learn about the various challenges characterizing this engineering domain and the latest solutions to properly address them.

Download or read book Data Driven Methods for Civil Structural Health Monitoring and Resilience written by Mohammad Noori and published by CRC Press. This book was released on 2023-10-26 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.

Download or read book Deep Learning Approaches for Condition Monitoring and Prognostic Analysis written by and published by . This book was released on 2021 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive analytics is a technique to make predictions about future unknown events by analyzing current and historical data. The market for predictive analysis is expected to reach $12.4 billion in 2022. Prognostic analysis is one dimension of predictive analytics in engineering discipline and focuses on predicting the time when a system or a component will no longer perform its intended function. A robust and accurate prognostic analysis makes for the increment of production reliability and safety as well as maintenance cost reduction, thus becoming one key task of condition-based maintenance (CBM). Given multiple sensors to simultaneously monitor the health condition during the age of Internet of Things (IoT) and Industry 4.0, traditional prognostic technologies have the limited ability of handling large-scale dataset embedded with complex structures. Therefore, this dissertation mainly focuses on developing high-performance Deep Learning (DL) technologies for condition monitoring and prognostic analysis under the data-rich environment. Besides the CBM, the proposed DL technologies can be also successfully extended and applied into other fields, like battery power estimation, Alzheimer's Disease (AD) prediction, etc., which have been validated in the dissertation with real case studies. In addition, this dissertation also studied and answered research questions like how to evaluate the quality of features for prognostic analysis, how to select and extract high-level features from original dataset, how to capture the interactions between different features, and so forth. The answers for these questions play important roles in model development for prognostic analysis.

Download or read book Nuclear Power Plant Equipment Prognostics and Health Management Based on Data driven methods written by Jun Wang and published by Frontiers Media SA. This book was released on 2021-09-13 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of the 5th International Conference on Maintenance Condition Monitoring and Diagnostics 2021 written by Esko Juuso and published by Springer Nature. This book was released on 2023-07-18 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains selected papers from the Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, in Oulu, Finland, collected by editors with years of experiences in condition monitoring, signal processing, advanced reasoning and diagnostics, maintenance, risk assessment, and asset management. This work maximizes reader insights into the current trends in novel technologies and maintenance trends in industrial domains, energy production and energy conservation, mechatronics and robot technologies. These proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for condition monitoring and risk management professionals from industry and science exchange knowledge, experiences and strengthen multidisciplinary network those in the field. This book will be of benefit to academia, and industry alike.

Download or read book Predictive Maintenance in Dynamic Systems written by Edwin Lughofer and published by Springer. This book was released on 2019-02-28 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.

Download or read book From Prognostics and Health Systems Management to Predictive Maintenance 1 written by Rafael Gouriveau and published by John Wiley & Sons. This book was released on 2016-10-14 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the steps needed to monitor health assessment systems and the anticipation of their failures: choice and location of sensors, data acquisition and processing, health assessment and prediction of the duration of residual useful life. The digital revolution and mechatronics foreshadowed the advent of the 4.0 industry where equipment has the ability to communicate. The ubiquity of sensors (300,000 sensors in the new generations of aircraft) produces a flood of data requiring us to give meaning to information and leads to the need for efficient processing and a relevant interpretation. The process of traceability and capitalization of data is a key element in the context of the evolution of the maintenance towards predictive strategies.

Download or read book Data Driven Cognitive Manufacturing Applications in Predictive Maintenance and Zero Defect Manufacturing written by Dimitris Kiritsis and published by Frontiers Media SA. This book was released on 2021-03-10 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data driven Modeling and Prognosis of Condition Monitoring Signals in Engineering Systems written by Raed Al Kontar and published by . This book was released on 2018 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Condition monitoring (CM) data, or simply monitoring data, is defined as a dataset that has been collected from individuals along the time, and it implicitly manifests the underlying unobservable system status. Due to the advances in sensory devices and information technology, prognosis and diagnosis in various fields can take enormous advantages from the rich condition monitoring data. However, at the same time, it also creates new challenges for research in data analytics as to how this vast and complex data could be utilized to retrieve accurate diagnosis and meaningful prognosis. Many existing techniques fall short of addressing this issue because most of them are developed when the data were collected in a well-controlled experimental setting. However, the monitoring data often involves many factors that are uncontrollable and, inevitably, has severe heterogeneity. This research simultaneously addresses multiple challenges that arise from the monitoring data. i. An Individualized model is crucial for effective diagnosis and prognosis based on the monitoring data. The primary focus of collecting the monitoring data is to understand the specific in-service unit rather than studying the population behavior. Therefore, the predictions or diagnostic decisions based on the monitoring data needs to be highly individualized. Collecting monitoring data happens in the on-line stage at the real time. Thus, the model should be able to update or adjust itself according to the newly-collected data points from the specific individual. ii. A non-parametric framework that can account for heterogeneity and handle high dimensionality in the data is needed. CM signals may not follow any parametric form, and if the specified form is far from the truth, the modeling and prognosis results will be misleading. For instance, parametric representations are typically based on physical or chemical theories; however, in most cases, such theories are unknown. Therefore, functional forms should be derived through empirical evaluation or visual observation, making them sensitive to model misspecification iii. A flexible modeling strategy that can handle multiple data types simultaneously is needed. Specifically incorporating qualitative data and introducing a distance measure between such data is essential for better prognosis. The monitoring data comes in various data types. In the literature, there are many statistical methods that are developed for a specific type of data which is not suitable for the monitoring data that includes various data types at the same time. Thus, a novel statistical model fusion needs to be investigated. iv. A scalable approach specifically when the number of CM signals/functional outputs is large. Further, the integrative analysis of multiple outputs implicitly assumes that these outputs share some commonalities. However, if this does not hold, negative transfer of knowledge may occur, which leads to decreased performance relative to learning tasks separately. Therefore, the model needs to possess excellent scalability when the number of outputs is large and simultaneously minimizes the negative transfer of knowledge between uncorrelated outputs. To address those issues listed above, four tasks are investigated in this report. (a) To build a mixture mixed effects model which is able to account for imbalance (early vs late failure) in the data. This technique greatly improves prognostics specifically for systems where most units are reliable and only few tend to fail at early stages of their life cycle. (b) To propose an alternative view on modeling CM data using multivariate Gaussian process. This view draws its roots from multitask learning and is based on treating each CM signal as an individual task. This technique is non-parametric, scalable and is able to account for heterogeneity in the data. (c) To incorporate qualitative features in non-parametric prognostics through a reparametrization technique called hypersphere decomposition. This technique allows incorporating external factors into prognostic models through defining a distance measure based on a unit hypercube. (d) To provide scalability for the multivariate Gaussian process when the number of outputs is large and to minimize the negative transfer of knowledge between uncorrelated outputs. This technique utilizes a distributed estimation scheme which allows scaling to arbitrarily large datasets through parallelization.