Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis

Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis
Author: Ruqiang Yan
Publisher: Elsevier
Total Pages: 314
Release: 2023-11-10
Genre: Business & Economics
ISBN: 0323914233
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Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis introduces the theory and latest applications of transfer learning on rotary machine fault diagnosis and prognosis. Transfer learning-based rotary machine fault diagnosis is a relatively new subject, and this innovative book synthesizes recent advances from academia and industry to provide systematic guidance. Basic principles are described before key questions are answered, including the applicability of transfer learning to rotary machine fault diagnosis and prognosis, technical details of models, and an introduction to deep transfer learning. Case studies for every method are provided, helping readers apply the techniques described in their own work. Offers case studies for each transfer learning algorithm Optimizes the transfer learning models to solve specific engineering problems Describes the roles of transfer components, transfer fields, and transfer order in intelligent machine diagnosis and prognosis

Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems

Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
Author: Yaguo Lei
Publisher: Springer Nature
Total Pages: 292
Release: 2022-10-19
Genre: Technology & Engineering
ISBN: 9811691312
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This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies

Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems

Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems
Author: Rui Yang
Publisher: CRC Press
Total Pages: 87
Release: 2022-06-16
Genre: Technology & Engineering
ISBN: 1000594939
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This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating machinery. Techniques and applications through experiments and case studies for intelligent precision compensation and fault diagnosis are offered along with the introduction of machine learning and deep learning methods. Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems discusses how to formulate and solve precision compensation and fault diagnosis problems. The book includes experimental results on hardware equipment used as practical examples throughout the book. Machine learning and deep learning methods used in intelligent precision compensation and intelligent fault diagnosis are introduced. Applications to deal with relevant problems concerning CNC machining and rotating machinery in industrial engineering systems are provided in detail along with applications used in precision motion systems. Methods, applications, and concepts offered in this book can help all professional engineers and students across many areas of engineering and operations management that are involved in any part of Industry 4.0 transformation.

Vibrations-Based Machine Fault Diagnosis and Prognosis Using Convolutional Neural Networks

Vibrations-Based Machine Fault Diagnosis and Prognosis Using Convolutional Neural Networks
Author: Jacob Hendriks
Publisher:
Total Pages:
Release: 2021
Genre:
ISBN:
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This thesis addresses vibration-based machine health monitoring (MHM) by applying the fundamentals of machine learning (ML), convolutional neural networks (CNNs) and selected signal processing. The thesis first presents an exploration of the relationship between the hyperparameters of two-layer CNNs, the type of signal preprocessing used, and resulting diagnostic accuracy. For this, two popular bearing fault datasets and a gear fault dataset are used to reveal cross-domain trends. It is found that using time-frequency representations provided by the spectrogram transformation results in a reduced dependence on hyperparameter optimization and lays the foundation for the following work. Moreover, by applying ML theory and best practices, the thesis demonstrates shortcomings in currently accepted benchmarking practices to evaluate the domain adaptability of bearing fault diagnosis algorithms and proposes an alternative benchmarking framework to resolve them. A novel data preparation and transfer learning procedure that capitalizes on the use of multiple sensors and that achieves higher accuracy than state-of-the-art algorithms is demonstrated. In addition to fault diagnosis, the thesis addresses bearing health prognosis by applying CNNs to health indicator estimation using data from accelerated life testing. Several data augmentation methods adapted from other ML fields are compared. It is determined that methods proven in sound classification or image recognition fields are not guaranteed to benefit this task. Lastly, the thesis presents a 3D CNN designed for bearing health prognosis that uses a multi-sensor time-frequency input to improves upon single-sensor variants. The thesis explores the strengths, as well as the shortcomings, of CNNs for MHM, an emphasis is placed on network design, signal transformation, and experimental methodology.

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
Author: Yaguo Lei
Publisher: Butterworth-Heinemann
Total Pages: 376
Release: 2016-11-02
Genre: Technology & Engineering
ISBN: 0128115351
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Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The main contents include multi-domain signal processing and feature extraction, intelligent diagnosis models, clustering algorithms, hybrid intelligent diagnosis strategies, and RUL prediction approaches, etc. This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to predict the RUL of rotating machinery. Besides experimental demonstrations, many application cases are presented and illustrated to test the methods mentioned in the book. This valuable reference provides an essential guide on machinery fault diagnosis that helps readers understand basic concepts and fundamental theories. Academic researchers with mechanical engineering or computer science backgrounds, and engineers or practitioners who are in charge of machine safety, operation, and maintenance will find this book very useful. Provides a detailed background and roadmap of intelligent diagnosis and RUL prediction of rotating machinery, involving fault mechanisms, vibration characteristics, health indicators, and diagnosis and prognostics Presents basic theories, advanced methods, and the latest contributions in the field of intelligent fault diagnosis and RUL prediction Includes numerous application cases, and the methods, algorithms, and models introduced in the book are demonstrated by industrial experiences

Intelligent Fault Diagnosis for Rotating Machines Using Deep Learning

Intelligent Fault Diagnosis for Rotating Machines Using Deep Learning
Author: Jorge Chuya Sumba
Publisher:
Total Pages: 14
Release: 2019
Genre: Machine learning
ISBN:
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The diagnosis of failures in high-speed machining centers and other rotary machines is critical in manufacturing systems, because early detection can save a representative amount of time and cost. Fault diagnosis systems generally have two blocks: feature extraction and classification. Feature extraction affects the performance of the prediction model, and essential information is extracted by identifying high-level abstract and representative characteristics. Deep learning (DL) provides an effective way to extract the characteristics of raw data without prior knowledge, compared with traditional machine learning (ML) methods. A feature learning approach was applied using one-dimensional (1-D) convolutional neural networks (CNN) that works directly with raw vibration signals. The network structure consists of small convolutional kernels to perform a nonlinear mapping and extract features; the classifier is a softmax layer. The method has achieved satisfactory performance in terms of prediction accuracy that reaches ∼99 % and ∼97 % using a standard bearings database: the processing time is suitable for real-time applications with ∼8 ms per signal, and the repeatability has a low standard deviation

Condition Monitoring with Vibration Signals

Condition Monitoring with Vibration Signals
Author: Hosameldin Ahmed
Publisher: John Wiley & Sons
Total Pages: 456
Release: 2020-01-07
Genre: Technology & Engineering
ISBN: 1119544629
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Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoringguiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.

Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems

Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
Author: Ruqiang Yan
Publisher: CRC Press
Total Pages: 217
Release: 2024-06-06
Genre: Computers
ISBN: 1040026591
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The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems contributes to improved efficiency, safety, and reliability of mechanical systems in various industrial domains. The book will appeal to academic researchers, practitioners, and students in the fields of intelligent fault diagnosis, prognostics and health management, and deep learning.

Introduction of Intelligent Machine Fault Diagnosis and Prognosis

Introduction of Intelligent Machine Fault Diagnosis and Prognosis
Author: O-Suk Yang
Publisher:
Total Pages: 0
Release: 2009
Genre: Automatic test equipment
ISBN: 9781606922637
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Condition monitoring, fault diagnosis and prognosis of machinery have received considerable attention in recent years and they are increasingly becoming important in industry because of the need to increase reliability and decrease possible loss of production due to the fault of equipments. Early fault detection, diagnosis and prognosis can increase equipment availability and performance, reduce consequential damage, prolong machine life and reduce spare parts inventories and break down maintenance. With the development of the artificial intelligence techniques, many intelligent systems have been employed to assist the maintenance management task to correctly interpret the fault data. The book is very easy to study; even if the reader is a beginner in the fault diagnosis area, they do not need special prerequisite knowledge to understand the contents of this book. The book is equipped with software under MATLAB and offers many examples which are related to fault diagnosis processes. It will be very useful to readers who want to study feature-based intelligent machine fault diagnosis and prognosis techniques. The book is dedicated to graduate students of mechanical and electrical engineering, computer science and for practising engineers.