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Author: Davide Palmisano Publisher: ISBN: Category : Languages : en Pages :
Book Description
This work addresses the application of two di erent methods in order to detect faults in bearings operating within an electromechanical system, based on the measurement of vibrations and stator currents. The electromechanical system considered is a shaft connected to an electric induction motor. Two bearings are mounted on the shaft; these bearings can be metallic or ceramic. The bearings can be found in three di erent conditions: healthy or with a inner race hole of 1 mm or 2 mm. First of all the analisys of theorical fault frequencies was explored. The goal of this method is to identify theorical fault frequencies, depending on features of the bearing, in order to verify the presence of peaks in the frequency signals obtained from laboratory measurements. The accuracy of the theoretical frequency calculation was demonstrated by the actual presence of these peaks in the frequency signals, however it was expected to be found a proportion between the peak heights, and the severity of the fault, but this didn't happened. That led to the development of the second method, based on the building of a neural network able to classify the bearings with respect to their conditions, starting from 15 di erent statistical time domain features as input. Two reduction technicques, LDA and PCA, were implemented in order to reduce the number of input to the two most signi cant features; after that the neural network was built. The results obtained with this second method are very satisfactory as they allow to classify with a good performance both considering the di erent scenarios of bearing material and measured signal taken individually, but also considering all four di erent scenarios at the same time.
Author: Davide Palmisano Publisher: ISBN: Category : Languages : en Pages :
Book Description
This work addresses the application of two di erent methods in order to detect faults in bearings operating within an electromechanical system, based on the measurement of vibrations and stator currents. The electromechanical system considered is a shaft connected to an electric induction motor. Two bearings are mounted on the shaft; these bearings can be metallic or ceramic. The bearings can be found in three di erent conditions: healthy or with a inner race hole of 1 mm or 2 mm. First of all the analisys of theorical fault frequencies was explored. The goal of this method is to identify theorical fault frequencies, depending on features of the bearing, in order to verify the presence of peaks in the frequency signals obtained from laboratory measurements. The accuracy of the theoretical frequency calculation was demonstrated by the actual presence of these peaks in the frequency signals, however it was expected to be found a proportion between the peak heights, and the severity of the fault, but this didn't happened. That led to the development of the second method, based on the building of a neural network able to classify the bearings with respect to their conditions, starting from 15 di erent statistical time domain features as input. Two reduction technicques, LDA and PCA, were implemented in order to reduce the number of input to the two most signi cant features; after that the neural network was built. The results obtained with this second method are very satisfactory as they allow to classify with a good performance both considering the di erent scenarios of bearing material and measured signal taken individually, but also considering all four di erent scenarios at the same time.
Author: Weihua Li Publisher: Springer Nature ISBN: 9819935377 Category : Technology & Engineering Languages : en Pages : 474
Book Description
Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.
Author: Jesús Adolfo Cariño Corrales Publisher: ISBN: Category : Languages : en Pages : 140
Book Description
Condition Based Maintenance is a program that recommends actions based on the information collected and interpreted through condition monitoring and has become accepted since a decade ago by the industry as a key factor to avoiding expensive unplanned machine stoppages and reaching high production ratios. Among the condition based maintenance strategies, data-driven fault diagnosis methodologies have gained increased attention because of the high performance and widen range of applicability due to less restrictive constrains in comparison to other approaches. Therefore, an increased effort is been made to develop reliable methodologies that could diagnose multiple known faults on a machine with initial applications in controlled environments like laboratory test benches. However, applying those methods to industry applications still represent an ongoing challenge due to the multiple limitations involved and the high reliability and robustness required. One of the most important challenges in the industrial sector refers to the management of unexpected events, in respect of how to detect new faults or anomalies in the machine. In addition, the information initially available of the monitored industrial machine is usually limited to the healthy condition, therefore is not only necessary to detect these new scenarios but also incorporate this information to the initial base knowledge. In this regard, this thesis present a series of complementary methodologies that leads to the implementation of a fault detection and identification system capable to detect multiple faults and new scenarios of industrial electromechanical machines under an incremental learning framework to include the new scenarios detected to the initial base knowledge while achieving a high performance and generalization capabilities. Initially, a methodology to increase the performance of novelty detection models to detect unexpected events in electromechanical system is proposed. Then, a methodology to implement a sequential fault detection and identification system composed by a novelty detection and a fault diagnosis stages with high accuracy is proposed. Finally, two different methodologies are proposed to provide the sequential fault detection and identification system the capacity to include new scenarios to the base knowledge. The proposed methodologies have been validated by means of experimental data of laboratory test benches and industrial electromechanical systems.
Author: Rui Yang Publisher: CRC Press ISBN: 1000594920 Category : Technology & Engineering Languages : en Pages : 93
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: Rolf Isermann Publisher: Springer Science & Business Media ISBN: 3642127673 Category : Technology & Engineering Languages : en Pages : 358
Book Description
Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity. This book is a sequel of the book “Fault-Diagnosis Systems” published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as: Electrical drives (DC, AC) Electrical actuators Fluidic actuators (hydraulic, pneumatic) Centrifugal and reciprocating pumps Pipelines (leak detection) Industrial robots Machine tools (main and feed drive, drilling, milling, grinding) Heat exchangers Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented. The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful. The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers.
Author: Patrick Girard Publisher: Springer Nature ISBN: 3031196392 Category : Technology & Engineering Languages : en Pages : 320
Book Description
This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.
Author: Chris Aldrich Publisher: Springer Science & Business Media ISBN: 1447151852 Category : Computers Languages : en Pages : 388
Book Description
This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.
Author: Jing Wang Publisher: Springer Nature ISBN: 9811680442 Category : Technology & Engineering Languages : en Pages : 277
Book Description
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.
Author: Steven X. Ding Publisher: Springer Nature ISBN: 3662620049 Category : Technology & Engineering Languages : en Pages : 664
Book Description
The major objective of this book is to introduce advanced design and (online) optimization methods for fault diagnosis and fault-tolerant control from different aspects. Under the aspect of system types, fault diagnosis and fault-tolerant issues are dealt with for linear time-invariant and time-varying systems as well as for nonlinear and distributed (including networked) systems. From the methodological point of view, both model-based and data-driven schemes are investigated.To allow for a self-contained study and enable an easy implementation in real applications, the necessary knowledge as well as tools in mathematics and control theory are included in this book. The main results with the fault diagnosis and fault-tolerant schemes are presented in form of algorithms and demonstrated by means of benchmark case studies. The intended audience of this book are process and control engineers, engineering students and researchers with control engineering background.
Author: M.G. Singh Publisher: Elsevier ISBN: 1483286665 Category : Technology & Engineering Languages : en Pages : 335
Book Description
Provides an up-to-date review of the latest developments in system reliability maintenance, fault detection and fault-tolerant design techniques. Topics covered include reliability analysis and optimization, maintenance control policies, fault detection techniques, fault-tolerant systems, reliable controllers and robustness, knowledge based approaches and decision support systems. There are further applications papers on process control, robotics, manufacturing systems, communications and power systems. Contains 36 papers.
Author: Davide Palmisano
Author: Davide Palmisano
Author: Weihua Li
Author: Jesús Adolfo Cariño Corrales
Author: Rui Yang
Author: Rolf Isermann
Author: Patrick Girard
Author: Chris Aldrich
Author: Jing Wang
Author: Steven X. Ding
Author: M.G. Singh