Fault Detection and Prediction with Application to Rotating Machinery PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Fault Detection and Prediction with Application to Rotating Machinery PDF full book. Access full book title Fault Detection and Prediction with Application to Rotating Machinery by Gary Ray Halligan. Download full books in PDF and EPUB format.
Author: Gary Ray Halligan Publisher: ISBN: Category : Artificial intelligence Languages : en Pages : 0
Book Description
"In this thesis, the detection and prediction of faults in rotating machinery is undertaken and presented in two papers. In the first paper, Principal Component Analysis (PCA), a well known data-driven dimension reduction technique, is applied to data for normal operation and four fault conditions from a one-half horsepower centrifugal water pump. Fault isolation in this scheme is done by observing the location of the data points in the Principal Component domain, and the time to failure (TTF) is calculated by applying statistical regression on the resulting PC scores. The application of the proposed scheme demonstrated that PCA was able to detect and isolate all four faults. Additionally, the TTF calculation for the impeller failure was found to yield satisfactory results. On the other hand, in the second paper, the fault detection and failure prediction are done by using a model based approach which utilizes a nonlinear observer consisting of an online approximator in discrete-time (OLAD) and a robust adaptive term. Once a fault has been detected, both the OLAD and the robust adaptive term are initiated and the OLAD then utilizes its update law to learn the unknown dynamics of the encountered fault. While in similar applications it is common to use neural networks to be used for the OLAD, in this paper an Artificial Immune System (AIS) is used for the OLAD. The proposed approach was verified through implementation on data from an axial piston pump. The scheme was able to satisfactorily detect and learn both an incipient piston wear fault and an abrupt sensor failure"--Abstract, leaf iv
Author: Gary Ray Halligan Publisher: ISBN: Category : Artificial intelligence Languages : en Pages : 0
Book Description
"In this thesis, the detection and prediction of faults in rotating machinery is undertaken and presented in two papers. In the first paper, Principal Component Analysis (PCA), a well known data-driven dimension reduction technique, is applied to data for normal operation and four fault conditions from a one-half horsepower centrifugal water pump. Fault isolation in this scheme is done by observing the location of the data points in the Principal Component domain, and the time to failure (TTF) is calculated by applying statistical regression on the resulting PC scores. The application of the proposed scheme demonstrated that PCA was able to detect and isolate all four faults. Additionally, the TTF calculation for the impeller failure was found to yield satisfactory results. On the other hand, in the second paper, the fault detection and failure prediction are done by using a model based approach which utilizes a nonlinear observer consisting of an online approximator in discrete-time (OLAD) and a robust adaptive term. Once a fault has been detected, both the OLAD and the robust adaptive term are initiated and the OLAD then utilizes its update law to learn the unknown dynamics of the encountered fault. While in similar applications it is common to use neural networks to be used for the OLAD, in this paper an Artificial Immune System (AIS) is used for the OLAD. The proposed approach was verified through implementation on data from an axial piston pump. The scheme was able to satisfactorily detect and learn both an incipient piston wear fault and an abrupt sensor failure"--Abstract, leaf iv
Author: Yaguo Lei Publisher: Butterworth-Heinemann ISBN: 0128115351 Category : Technology & Engineering Languages : en Pages : 376
Book Description
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
Author: Publisher: ISBN: Category : Languages : en Pages : 52
Book Description
The purpose of this research was to design a neural network based fault diagnosis system that is capable of detecting and classifying incipient faults in rotating machinery. A neural network is essentially a pattern recognition system which produces a mapping from a set of input data to a set of output data. Neural networks are unique in that this mapping is created autonomously, based on a learning algorithm that the user specifies. In this research, the mapping is from a set of measured parameters (e.g., vibration spectrum) of the rotating machinery to a classification of the system's condition (e.g., worn bearings). Limited success has been achieved in this area over the past decade. Research to date indicates that neural networks have the ability to recognize faults in machinery. This research focused on the following objectives: (1) creating a system that can recognize basic fault conditions based on the fault's vibration signature; (2) improving the ability of the neural network to recognize transient conditions as normal rather than classify them as faults; (3) examining the effect that disturbances have on the neural network's output; and (4) developing a system that will enable detection of faults which are not included in the training set.
Author: Asoke K. Nandi Publisher: John Wiley & Sons ISBN: 1119544637 Category : Technology & Engineering Languages : en Pages : 440
Book Description
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 monitoring—guiding 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.
Author: Fakher Chaari Publisher: Springer Nature ISBN: 3030795195 Category : Technology & Engineering Languages : en Pages : 177
Book Description
This book offers an overview of current methods for the intelligent monitoring of rotating machines. It describes the foundations of smart monitoring, guiding readers to develop appropriate machine learning and statistical models for answering important challenges, such as the management and analysis of a large volume of data. It also discusses real-world case studies, highlighting some practical issues and proposing solutions to them. The book offers extensive information on research trends, and innovative strategies to solve emerging, practical issues. It addresses both academics and professionals dealing with condition monitoring, and mechanical and production engineering issues, in the era of industry 4.0.
Author: Yong Zhang Publisher: CRC Press ISBN: 1000835944 Category : Technology & Engineering Languages : en Pages : 290
Book Description
This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning. Features: Unifies existing and emerging concepts concerning robust filtering and fault diagnosis with an emphasis on a variety of network-induced complexities. Explains theories, techniques, and applications of state estimation as well as fault diagnosis from an engineering-oriented perspective. Provides a series of latest results in robust/stochastic filtering, multidate sample, and time-varying system. Captures diagnosis (fault detection, fault isolation and fault estimation) for time-varying multi-rate systems. Includes simulation examples in each chapter to reflect the engineering practice. This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.
Author: Francesco Ferracuti Publisher: MDPI ISBN: 3036504621 Category : Technology & Engineering Languages : en Pages : 130
Book Description
Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions.
Author: Jorge Chuya Sumba Publisher: ISBN: Category : Machine learning Languages : en Pages : 14
Book Description
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
Author: Rui Yang Publisher: CRC Press ISBN: 1000594939 Category : Technology & Engineering Languages : en Pages : 87
Book Description
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.
Author: Gary Ray Halligan
Author: Gary Ray Halligan
Author: Yaguo Lei
Author: 
Author: William O'Neal Nash
Author: Asoke K. Nandi
Author: Fakher Chaari
Author: Yong Zhang
Author: Francesco Ferracuti
Author: Jorge Chuya Sumba
Author: Rui Yang