Machine Learning In Python For Process And Equipment Condition Monitoring And Predictive Maintenance
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| Author | : Ankur Kumar |
| Publisher | : MLforPSE |
| Total Pages | : 365 |
| Release | : 2024-01-12 |
| Genre | : Computers |
| ISBN | : |
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 |
| Total Pages | : 69 |
| Release | : 2024-04-24 |
| Genre | : Computers |
| ISBN | : |
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 | : Dipanjan Sarkar |
| Publisher | : Apress |
| Total Pages | : 545 |
| Release | : 2017-12-20 |
| Genre | : Computers |
| ISBN | : 1484232070 |
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
| Author | : Ankur Kumar |
| Publisher | : MLforPSE |
| Total Pages | : 208 |
| Release | : 2023-06-01 |
| Genre | : Computers |
| ISBN | : |
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 | : Ankur Kumar |
| Publisher | : MLforPSE |
| Total Pages | : 354 |
| Release | : 2022-02-25 |
| Genre | : Computers |
| ISBN | : |
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 | : Dr Deepti Chopra |
| Publisher | : BPB Publications |
| Total Pages | : 134 |
| Release | : 2021-05-07 |
| Genre | : Computers |
| ISBN | : 9389423619 |
Explore Machine Learning Techniques, Different Predictive Models, and its Applications Ê KEY FEATURESÊÊ _ Extensive coverage of real examples on implementation and working of ML models. _ Includes different strategies used in Machine Learning by leading data scientists. _ Focuses on Machine Learning concepts and their evolution to algorithms. DESCRIPTIONÊ This book covers basic concepts of Machine Learning, various learning paradigms, different architectures and algorithms used in these paradigms. You will learn the power of ML models by exploring different predictive modeling techniques such as Regression, Clustering, and Classification. You will also get hands-on experience on methods and techniques such as Overfitting, Underfitting, Random Forest, Decision Trees, PCA, and Support Vector Machines. In this book real life examples with fully working of Python implementations are discussed in detail. At the end of the book you will learn about the unsupervised learning covering Hierarchical Clustering, K-means Clustering, Dimensionality Reduction, Anomaly detection, Principal Component Analysis.Ê WHAT YOU WILL LEARN _ Learn to perform data engineering and analysis. _ Build prototype ML models and production ML models from scratch. _ Develop strong proficiency in using scikit-learn and Python. _ Get hands-on experience with Random Forest, Logistic Regression, SVM, PCA, and Neural Networks. WHO THIS BOOK IS FORÊÊ This book is meant for beginners who want to gain knowledge about Machine Learning in detail. This book can also be used by Machine Learning users for a quick reference for fundamentals in Machine Learning. Readers should have basic knowledge of Python and Scikit-Learn before reading the book. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Linear Regression 3. Classification Using Logistic Regression 4. Overfitting and Regularization 5. Feasibility of Learning 6. Support Vector Machine 7. Neural Network 8. Decision Trees 9. Unsupervised Learning 10. Theory of Generalization 11. Bias and Fairness in ML
| Author | : Chen Ye (S.M.) |
| Publisher | : |
| Total Pages | : 91 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Digital technology is changing the industrial sector, yet how to make rational use of some technologies and create considerable value in a variety of industrial scenarios is an issue. Many digital industrial companies have stated that they have helped clients with their digital transformation, create much value, but the real effects have not been shown in public. Venture capitals firms have made huge investment in potential digital industrial startups. Numerous industrial IoT platforms are emerging in the market, but a number of them fade soon after. Many people have heard about industrial maintenance technology, but they have difficulty in differentiate concepts such as reactive maintenance, planned maintenance, proactive maintenance, and predictive maintenance. Many people know that big data and Al are essential in industrial sector, but they do not know how to process, analyze, and extract value from industrial data and how to use Al algorithms and tools to implement a research project. This thesis analyzes the entire digital industrial ecosystem in various dimensions such as initiatives, technologies in related domains, stakeholders, markets, and strategies. This work also analyzes of the predictive maintenance solution in various dimensions such as background, importance, suitable scenarios, market, business model, and technology. The author plans an experiment for the predictive maintenance solution, including goal, data source and description, methods and steps, and flow and tools. Then author uses a baseline approach and an optimal approach to implement the experiment, including data preparation, selection and evaluation of both regression and classification models, and deep learning practice through neural network building and optimization. Finally, contributions and expectations, and limitations and future research are discussed. This work uses a system approach, including system architecting, system engineering, and project management, to complete the process of analysis, design, and implementation.
| Author | : Jessica Madison Wu |
| Publisher | : |
| Total Pages | : 69 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
SHAPE Technologies is the world leader in ultra high pressure industrial waterjet systems for cutting and cleaning with applications from metal to food. Although SHAPE is the technological leader in this space, SHAPE must continuously look toward developing new capabilities to differentiate its products. SHAPE has historically outfitted its machines with a suite of sensors, however these systems in the field do not store the data, thereby losing the time series relationships and historical log of machine health. One opportunity is to create a connected platform that leverages this data to help SHAPE's customers move away from a break fix model to a predictive maintenance program. This project seeks to expand on a sensor connectivity proof of concept ("POC"), which the team successfully built on a prototype grade Raspberry Pi, and make the platform ready for customer beta trial. First, this project explores important infrastructure, legal, and supply chain challenges that impact the commercial business when connecting industrial equipment to the internet as well as the technological considerations to make the platform both backwards and forwards compatible. Second, this project helps define the minimum viable product requirements for industrial infrastructure and devices configuration. Third, this project merges the POC captured data and lab data to train and validate supervised machine learning models to predict failures several days in advance and demonstrates how such a system can help customers mitigate unplanned downtime.
| Author | : Axel Ross |
| Publisher | : |
| Total Pages | : 164 |
| Release | : 2021-02-15 |
| Genre | : |
| ISBN | : 9781801824996 |
55% DISCOUNT FOR BOOKSTORES! Attract new customers with this book. They will love it! Geared mainly toward beginners readers, the topic of Python Data Science is getting more and more discussed today as companies increasingly require professionals who can manage Python, Machine Learning and Artificial Intelligence. "Many people think that python, data science, machine learning and artificial intelligence are difficult concepts to understand. Data science uses scientific strategies and Science to process data and to separate information from it. It chips away at a similar idea as Big Data and Data Mining. It requires ground-breaking equipment alongside a useful calculation and programming to take care of the data issues or to process the data for acquiring meaningful learning from it. The present information patterns are giving us 80% of data in unstructured mannered while rest 20% organized in organization for snappy dissecting. The unorganized or semi-organized details require processing to make it valuable for the present-day business person condition. For the most part, this information or details are produced from the wide assortments of sources, for example, content records, money related logs, instruments and sensors, and sight and sound structures. Drawing important and profitable experiences from this information require propelled calculations and tools. This Science is proposing an offer for this purpose, and this is making it a useful science for the present-day mechanical world. The improvement and exceedingly useful inquire about in the world of Computer Science and Technology has made the importance of its most basic and essential of concepts ascend by a thousand-crease. This principle concept is the thing that we have been everlastingly alluding to as data, and it is this data that solitary holds the way to everything in the world. The greatest of organizations and firms of the world have fabricated their establishment and philosophies and determine a unique piece of their pay totally through data. Fundamentally, the value and importance of data can be comprehended by the straightforward certainty that a legitimate store/distribution center of data is a million times more profitable than mine of pure gold in the advanced world. Like this, the vast spread and escalated examines in the field of data has genuinely opened up a lot of potential outcomes and doors (as far as a calling) wherein curating such vast amounts of data are the absolute most lucrative employments a specialized individual can discover today. This guide will focus on the following: Applications and role of data science Data science and applications GUI programming with Tkinter. Working with raw data Build your own sentiment analysis tool Exploration of NLTK K-means clustering Operations on data Variable scope and lifetime in python functions Machine learning & neural networks Principal components analysis Setting up your TensorFlow environment And more! Don't miss the opportunity to learn more about these topics. The future has never been closer and the opportunities it offers are endless. Even if you are a beginner, if you are starting from scratch, this book will allow you to understand topics that you have already heard about and that fascinate you, but that you probably never had the courage to go into." This book is a real gold mine. It has already sold hundreds of thousands of copies and received rave reviews from readers all over the world.Don't pass up the chance to have this book in your store!
| Author | : ML & AI ACADEMY |
| Publisher | : Giale Limited |
| Total Pages | : 186 |
| Release | : 2020-11-21 |
| Genre | : |
| ISBN | : 9781801255318 |
Are you looking for a complete guide of machine learning and artificial intelligence? Then you have found just the book you need to understand and master the fundamentals of machine learning and artificial intelligence technology. With the rise of the modern-day smart customer, a competitive race has been ignited among the businesses that are starting to rely upon innovative technologies. The most important technologies are machine-learning, data mining technology, and artificial intelligence technology to gain an edge over the competition; resulting in high paying and rewarding jobs for people like you who have the in-demand machine learning technical skillset. It is essentials to master the basics of these technologies as well as the basic concepts of Python coding and how you can utilize your coding skills to analyze a large volume of data and uncover valuable information. Python programming language increases the speed of operation while allowing for higher efficiency in creating system integrations. The power of programming languages in our digital world cannot be underestimated. Some of the highlights of this book include: - Deep dive into the data mining process - Gain an in-depth understanding of various machine-learning algorithms - Dig deep into the development and application of some of the most popular supervised and unsupervised machine learning algorithms. - Step by step instructions on how to install Python on your operating systems (Windows, Mac, and Linux). - Basic concepts of writing efficient and effective Python codes - Learn everything you need to know of the most popular machine learning library called "TensorFlow." - Deep dive into the concept of personalized marketing, predictive analytics, customer analytics and exploratory data analysis Remember knowledge is power, and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices. Your understanding of Python, Artificial Intelligence and machine learning will improve drastically, and you will be poised to develop your very own machine-learning model. Even if you have never studied Python language before, you can learn it quickly. So what are you waiting for? Go to the top of the page and click Buy Now!
