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Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 365
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Book Description
This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring , and predictive maintenance solutions in process industry . The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists. Upon completion, readers will be able to confidently navigate the Prognostics and Health Management literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into seven parts. Part 1 lays down the basic foundations of ML-assisted process and equipment condition monitoring, and predictive maintenance. Part 2 provides in-detail presentation of classical ML techniques for univariate signal monitoring. Different types of control charts and time-series pattern matching methodologies are discussed. Part 3 is focused on the widely popular multivariate statistical process monitoring (MSPM) techniques. Emphasis is paid to both the fault detection and fault isolation/diagnosis aspects. Part 4 covers the process monitoring applications of classical machine learning techniques such as k-NN, isolation forests, support vector machines, etc. These techniques come in handy for processes that cannot be satisfactorily handled via MSPM techniques. Part 5 navigates the world of artificial neural networks (ANN) and studies the different ANN structures that are commonly employed for fault detection and diagnosis in process industry. Part 6 focusses on vibration-based monitoring of rotating machinery and Part 7 deals with prognostic techniques for predictive maintenance applications. Broadly, the book covers the following: Exploratory analysis of process data Best practices for process monitoring and predictive maintenance solutions Univariate monitoring via control charts and time series data mining Multivariate statistical process monitoring techniques (PCA, PLS, FDA, etc.) Machine learning and deep learning techniques to handle dynamic, nonlinear, and multimodal processes Fault detection and diagnosis of rotating machinery using vibration data Remaining useful life predictions for predictive maintenance
Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 365
Get Book Here
Book Description
This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring , and predictive maintenance solutions in process industry . The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists. Upon completion, readers will be able to confidently navigate the Prognostics and Health Management literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into seven parts. Part 1 lays down the basic foundations of ML-assisted process and equipment condition monitoring, and predictive maintenance. Part 2 provides in-detail presentation of classical ML techniques for univariate signal monitoring. Different types of control charts and time-series pattern matching methodologies are discussed. Part 3 is focused on the widely popular multivariate statistical process monitoring (MSPM) techniques. Emphasis is paid to both the fault detection and fault isolation/diagnosis aspects. Part 4 covers the process monitoring applications of classical machine learning techniques such as k-NN, isolation forests, support vector machines, etc. These techniques come in handy for processes that cannot be satisfactorily handled via MSPM techniques. Part 5 navigates the world of artificial neural networks (ANN) and studies the different ANN structures that are commonly employed for fault detection and diagnosis in process industry. Part 6 focusses on vibration-based monitoring of rotating machinery and Part 7 deals with prognostic techniques for predictive maintenance applications. Broadly, the book covers the following: Exploratory analysis of process data Best practices for process monitoring and predictive maintenance solutions Univariate monitoring via control charts and time series data mining Multivariate statistical process monitoring techniques (PCA, PLS, FDA, etc.) Machine learning and deep learning techniques to handle dynamic, nonlinear, and multimodal processes Fault detection and diagnosis of rotating machinery using vibration data Remaining useful life predictions for predictive maintenance
Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 354
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Book Description
This book provides an application-focused exposition of modern ML tools that have proven useful in process industry and hands-on illustrations on how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, inferential modeling, dimensionality reduction, and process control. This book considers unique characteristics of industrial process data and uses real data from industrial systems for illustrations. With the focus on practical implementation and minimal programming or ML prerequisites, the book covers the gap in available ML resources for industrial practitioners. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning. The readers will find all the resources they need to deal with high-dimensional, correlated, noisy, corrupted, multimode, and nonlinear process data. The book has been divided into four parts. Part 1 provides a perspective on the importance of ML in process systems engineering and lays down the basic foundations of ML. Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the various characteristics of industrial process systems. Part 3 is focused on artificial neural networks and deep learning. Part 4 covers the important topic of deploying ML solutions over web and shows how to build a production-ready process monitoring web application. Broadly, the book covers the following: Varied applications of ML in process industry Fundamentals of machine learning workflow Practical methodologies for pre-processing industrial data Classical ML methods and their application for process monitoring, fault diagnosis, and soft sensing Deep learning and its application for predictive maintenance Reinforcement learning and its application for process control Deployment of ML solution over web
Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 69
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Book Description
This book is designed to help readers gain quick familiarity with deep learning-based computer vision and abnormal equipment sound detection techniques. The book helps you take your first step towards learning how to use convolutional neural networks (the ANN architecture that is behind the modern revolution in computer vision) and build image sensor-based manufacturing defect detection solutions. A quick introduction is also provided to how modern predictive maintenance solutions can be built for process critical equipment by analyzing the sound generated by the equipment. Overall, this short eBook sets the foundation with which budding process data scientists can confidently navigate the world of modern computer vision and acoustic monitoring.
Author: Ankur Kumar
Publisher: MLforPSE
ISBN:
Category : Computers
Languages : en
Pages : 208
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Book Description
This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning for dynamic process modeling. Upon completion, readers will be able to confidently navigate the system identification literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into three parts. Part 1 of the book provides perspectives on the importance of ML for dynamic process modeling and lays down the basic foundations of ML-DPM (machine learning for dynamic process modeling). Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the different modeling requirements and process characteristics that determine a model’s suitability for a problem at hand. These include, amongst others, presence of multiple correlated outputs, process nonlinearity, need for low model bias, need to model disturbance signal accurately, etc. Part 3 is focused on artificial neural networks and deep learning. The following topics are broadly covered: · Exploratory analysis of dynamic dataset · Best practices for dynamic modeling · Linear and discrete-time classical parametric and non-parametric models · State-space models for MIMO systems · Nonlinear system identification and closed-loop identification · Neural networks-based dynamic process modeling
Author: Jerker Delsing
Publisher: CRC Press
ISBN: 1315350866
Category : Computers
Languages : en
Pages : 404
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Book Description
This book presents an in-depth description of the Arrowhead Framework and how it fosters interoperability between IoT devices at service level, specifically addressing application. The Arrowhead Framework utilizes SOA technology and the concepts of local clouds to provide required automation capabilities such as: real time control, security, scalability, and engineering simplicity. Arrowhead Framework supports the realization of collaborative automation; it is the only IoT Framework that addresses global interoperability across multiplet SOA technologies. With these features, the Arrowhead Framework enables the design, engineering, and operation of large automation systems for a wide range of applications utilizing IoT and CPS technologies. The book provides application examples from a wide number of industrial fields e.g. airline maintenance, mining maintenance, smart production, electro-mobility, automative test, smart cities—all in response to EU societal challenges. Features Covers the design and implementation of IoT based automation systems. Industrial usage of Internet of Things and Cyber Physical Systems made feasible through Arrowhead Framework. Functions as a design cookbook for building automation systems using IoT/CPS and Arrowhead Framework. Tools, templates, code etc. described in the book will be accessible through open sources project Arrowhead Framework Wiki at forge.soa4d.org/ Written by the leading experts in the European Union and around the globe.
Author: Carlos Toro
Publisher: Springer Nature
ISBN: 3030672700
Category : Technology & Engineering
Languages : en
Pages : 418
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Book Description
This book relates research being implemented in three main research areas: secure connectivity and intelligent systems, real-time analytics and manufacturing knowledge and virtual manufacturing. Manufacturing SMEs and MNCs want to see how Industry 4.0 is implemented. On the other hand, groundbreaking research on this topic is constantly growing. For the aforesaid reason, the Singapore Agency for Science, Technology and Research (A*STAR), has created the model factory initiative. In the model factory, manufacturers, technology providers and the broader industry can (i) learn how I4.0 technologies are implemented on real-world manufacturing use-cases, (ii) test process improvements enabled by such technologies at the model factory facility, without disrupting their own operations, (iii) co-develop technology solutions and (iv) support the adoption of solutions at their everyday industrial operation. The book constitutes a clear base ground not only for inspiration of researchers, but also for companies who will want to adopt smart manufacturing approaches coming from Industry 4.0 in their pathway to digitization.
Author: M S Hariharan
Publisher: BPB Publications
ISBN: 9355515758
Category : Computers
Languages : en
Pages : 303
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Book Description
Harness the power of Python to analyze your IoT data KEY FEATURES ● Learn how to build an IoT Data Analytics infrastructure. ● Explore advanced techniques for IoT Data Analysis with Python. ● Gain hands-on experience applying IoT Data Analytics to real-world situations. DESCRIPTION Python is a popular programming language for data analytics, and it is also well-suited for IoT Data Analytics. By leveraging Python's versatility and its rich ecosystem of libraries and tools, Data Analytics for IoT can unlock valuable insights, enable predictive capabilities, and optimize decision-making in various IoT applications and domains. The book begins with a foundation in IoT fundamentals, its role in digital transformation, and why Python is the preferred language for IoT Data Analytics. It then covers essential data analytics concepts, how to establish an IoT Data Analytics environment, and how to design and manage real-time IoT data flows. Next, the book discusses how to implement Descriptive Analytics with Pandas, Time Series Forecasting with Python libraries, and Monitoring, Preventive Maintenance, Optimization, Text Mining, and Automation strategies. It also introduces Edge Computing and Analytics, discusses Continuous and Adaptive Learning concepts, and explores data flow and use cases for Edge Analytics. Finally, the book concludes with a chapter on IoT Data Analytics for self-driving cars, using the CRISP-DM framework for data collection, modeling, and deployment. By the end of the book, you will be equipped with the skills and knowledge needed to extract valuable insights from IoT data and build real-world applications. WHAT YOU WILL LEARN ● Explore the essentials of IoT Data Analytics and the Industry 4.0 revolution. ● Learn how to set up the IoT Data Analytics environment. ● Equip Python developers with data analysis foundations. ● Learn to build data lakes for real-time IoT data streaming. ● Learn to deploy machine learning models on edge devices. ● Understand Edge Computing with MicroPython for efficient IoT Data Analytics. WHO THIS BOOK IS FOR If you are an experienced Python developer who wants to master IoT Data Analytics, or a newcomer who wants to learn Python and its applications in IoT, this book will give you a thorough understanding of IoT Data Analytics and practical skills for real-world use cases. TABLE OF CONTENTS 1. Necessity of Analytics Across IoT 2. Up and Running with Data Analytics Fundamentals 3. Setting Up IoT Analytics Environment 4. Managing Data Pipeline and Cleaning 5. Designing Data Lake and Executing Data Transformation 6. Implementing Descriptive Analytics Using Pandas 7. Time Series Forecasting and Predictions 8. Monitoring and Preventive Maintenance 9. Model Deployment on Edge Devices 10. Understanding Edge Computing with MicroPython 11. IoT Analytics for Self-driving Vehicles
Author: R. Keith Mobley
Publisher: Elsevier
ISBN: 0080478697
Category : Technology & Engineering
Languages : en
Pages : 451
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Book Description
This second edition of An Introduction to Predictive Maintenance helps plant, process, maintenance and reliability managers and engineers to develop and implement a comprehensive maintenance management program, providing proven strategies for regularly monitoring critical process equipment and systems, predicting machine failures, and scheduling maintenance accordingly. Since the publication of the first edition in 1990, there have been many changes in both technology and methodology, including financial implications, the role of a maintenance organization, predictive maintenance techniques, various analyses, and maintenance of the program itself. This revision includes a complete update of the applicable chapters from the first edition as well as six additional chapters outlining the most recent information available. Having already been implemented and maintained successfully in hundreds of manufacturing and process plants worldwide, the practices detailed in this second edition of An Introduction to Predictive Maintenance will save plants and corporations, as well as U.S. industry as a whole, billions of dollars by minimizing unexpected equipment failures and its resultant high maintenance cost while increasing productivity. - A comprehensive introduction to a system of monitoring critical industrial equipment - Optimize the availability of process machinery and greatly reduce the cost of maintenance - Provides the means to improve product quality, productivity and profitability of manufacturing and production plants
Author: Pedro M. Santos
Publisher: Packt Publishing Ltd
ISBN: 1838986626
Category : Computers
Languages : en
Pages : 443
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Book Description
Delve deep into the various technical practices, principles, and values of Agile. Key FeaturesDiscover the essence of Agile software development and the key principles of software designExplore the fundamental practices of Agile working, including test-driven development (TDD), refactoring, pair programming, and continuous integrationLearn and apply the four elements of simple designBook Description The number of popular technical practices has grown exponentially in the last few years. Learning the common fundamental software development practices can help you become a better programmer. This book uses the term Agile as a wide umbrella and covers Agile principles and practices, as well as most methodologies associated with it. You’ll begin by discovering how driver-navigator, chess clock, and other techniques used in the pair programming approach introduce discipline while writing code. You’ll then learn to safely change the design of your code using refactoring. While learning these techniques, you’ll also explore various best practices to write efficient tests. The concluding chapters of the book delve deep into the SOLID principles - the five design principles that you can use to make your software more understandable, flexible and maintainable. By the end of the book, you will have discovered new ideas for improving your software design skills, the relationship within your team, and the way your business works. What you will learnLearn the red, green, refactor cycle of classic TDD and practice the best habits such as the rule of 3, triangulation, object calisthenics, and moreRefactor using parallel change and improve legacy code with characterization tests, approval tests, and Golden MasterUse code smells as feedback to improve your designLearn the double cycle of ATDD and the outside-in mindset using mocks and stubs correctly in your testsUnderstand how Coupling, Cohesion, Connascence, SOLID principles, and code smells are all relatedImprove the understanding of your business domain using BDD and other principles for "doing the right thing, not only the thing right"Who this book is for This book is designed for software developers looking to improve their technical practices. Software coaches may also find it helpful as a teaching reference manual. This is not a beginner's book on how to program. You must be comfortable with at least one programming language and must be able to write unit tests using any unit testing framework.
Author: Valentine Fontama
Publisher: Apress
ISBN: 148420445X
Category : Computers
Languages : en
Pages : 178
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Book Description
Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter. The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.
