From Deterioration Modeling to Remaining Useful Life Control 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 From Deterioration Modeling to Remaining Useful Life Control PDF full book. Access full book title From Deterioration Modeling to Remaining Useful Life Control by Diego Jair Rodriguez obando. Download full books in PDF and EPUB format.
Author: Diego Jair Rodriguez obando
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Remaining Useful Lifetime (RUL) can be simply defined as a prediction of the remaining time that a system is able to perform its intended function, from the current time to the final failure. This predicted time mostly depends on the state of deterioration of the system components and their expected future operating conditions. Thus, the RUL prediction is an uncertain process and its control is not trivial task.In general, the purpose for predicting the RUL is to influence decision-making for the system. In this dissertation a comprehensive framework for controlling the RUL is presented. Model uncertainties as well as system disturbances have been considered into the proposed framework. Issues as uncertainty treatment and inclusion of RUL objectives in the control strategy are studied from the modeling until a final global control architecture. It is shown that the RUL can be predicted from a suitable estimation of the deterioration, and from hypothesis on the future operation conditions. Friction drive systems are used for illustrating the usefulness of the aforementioned global architecture. For this kind of system, the friction is the source of motion and at the same time the source of deterioration. This double characteristic of friction is a motivation for controlling automatically the deterioration of the system by keeping a trade-off, between motion requirements and desired RUL values. In this thesis, a new control-oriented model for friction drive systems, which includes a dynamical model of the deterioration is proposed. The amount of deterioration has been considered as a function of the dissipated energy, at the contact surface, during the mechanical power transmission. An approach to estimate the current deterioration condition of a friction drive system is proposed. The approach is based on an Extended Kalman Filter (EKF) which uses an augmented model including the mechanical dynamical system and the deterioration dynamics. At every time instant, the EKF also provides intervals which surely includes the actual deterioration value which a given probability. A new architecture for controlling the RUL is proposed, which includes: a deterioration condition monitoring system (for instance the proposed EKF), a system operation condition estimator, a RUL controller system, and a RUL actuation principle. The operation condition estimator is based on the assumption that it is possible quantify certain characteristics of the motion requirements, for instance the duty cycle of motor torques. The RUL controller uses a cost function that weights the motion requirements and the desired RUL values to modify a varying-parameter filter, used here as the RUL-actuating-principle. The RUL-actuating-principle is based on a modification of the demanded torques, coming from a possible motion controller system. Preliminary results show that it is possible to control de RUL according to the proposed theoretical framework.
Author: Pierre Dersin
Publisher: CRC Press
ISBN: 1000886905
Category : Technology & Engineering
Languages : en
Pages : 452
Get Book Here
Book Description
This book applies traditional reliability engineering methods to prognostics and health management (PHM), looking at remaining useful life (RUL) and its dynamics, to enable engineers to effectively and accurately predict machinery and systems useful lifespan. One of the key tools used in defining and implementing predictive maintenance policies is the RUL indicator. However, it is essential to account for the uncertainty inherent to the RUL, as otherwise predictive maintenance strategies can be incorrect. This can cause high costs or, alternatively, inappropriate decisions. Methods used to estimate RUL are numerous and diverse and, broadly speaking, fall into three categories: model-based, data-driven, or hybrid, which uses both. The author starts by building on established theory and looks at traditional reliability engineering methods through their relation to PHM requirements and presents the concept of RUL loss rate. Following on from this, the author presents an innovative general method for defining a nonlinear transformation enabling the mean residual life to become a linear function of time. He applies this method to frequently encountered time-to-failure distributions, such as Weibull and gamma, and degradation processes. Latest research results, including the author’s (some of which were previously unpublished), are drawn upon and combined with very classical work. Statistical estimation techniques are then presented to estimate RUL from field data, and risk-based methods for maintenance optimization are described, including the use of RUL dynamics for predictive maintenance. The book ends with suggestions for future research, including links with machine learning and deep learning. The theory is illustrated by industrial examples. Each chapter is followed by a series of exercises. FEATURES Provides both practical and theoretical background of RUL Describes how the uncertainty of RUL can be related to RUL loss rate Provides new insights into time-to-failure distributions Offers tools for predictive maintenance This book will be of interest to engineers, researchers and students in reliability engineering, prognostics and health management, and maintenance management.
Author: Diego Jair Rodriguez obando
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Remaining Useful Lifetime (RUL) can be simply defined as a prediction of the remaining time that a system is able to perform its intended function, from the current time to the final failure. This predicted time mostly depends on the state of deterioration of the system components and their expected future operating conditions. Thus, the RUL prediction is an uncertain process and its control is not trivial task.In general, the purpose for predicting the RUL is to influence decision-making for the system. In this dissertation a comprehensive framework for controlling the RUL is presented. Model uncertainties as well as system disturbances have been considered into the proposed framework. Issues as uncertainty treatment and inclusion of RUL objectives in the control strategy are studied from the modeling until a final global control architecture. It is shown that the RUL can be predicted from a suitable estimation of the deterioration, and from hypothesis on the future operation conditions. Friction drive systems are used for illustrating the usefulness of the aforementioned global architecture. For this kind of system, the friction is the source of motion and at the same time the source of deterioration. This double characteristic of friction is a motivation for controlling automatically the deterioration of the system by keeping a trade-off, between motion requirements and desired RUL values. In this thesis, a new control-oriented model for friction drive systems, which includes a dynamical model of the deterioration is proposed. The amount of deterioration has been considered as a function of the dissipated energy, at the contact surface, during the mechanical power transmission. An approach to estimate the current deterioration condition of a friction drive system is proposed. The approach is based on an Extended Kalman Filter (EKF) which uses an augmented model including the mechanical dynamical system and the deterioration dynamics. At every time instant, the EKF also provides intervals which surely includes the actual deterioration value which a given probability. A new architecture for controlling the RUL is proposed, which includes: a deterioration condition monitoring system (for instance the proposed EKF), a system operation condition estimator, a RUL controller system, and a RUL actuation principle. The operation condition estimator is based on the assumption that it is possible quantify certain characteristics of the motion requirements, for instance the duty cycle of motor torques. The RUL controller uses a cost function that weights the motion requirements and the desired RUL values to modify a varying-parameter filter, used here as the RUL-actuating-principle. The RUL-actuating-principle is based on a modification of the demanded torques, coming from a possible motion controller system. Preliminary results show that it is possible to control de RUL according to the proposed theoretical framework.
Author: Raymon van Dinter
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Context: Predictive maintenance typically relies on large datasets of run-to-failure data from sensors designed for predictive maintenance purposes, like accelerometers and microphones. However, many industrial companies don't keep track of maintenance logs and motion control data, so they often lack records of run-to-failure data or effective sensors. On the other hand, manufacturing companies often have access to controller data from their Programmable Logic Controller (PLC) systems.Objective: This study aims to develop an approach for predicting the Remaining Useful Life (RUL) of manufacturing systems using PLC data.Method: A model pipeline, which uses stationary PLC data to detect degradation was developed in this research. The models evaluated for degradation modelling are based on: Singular Value Decomposition, Dynamic Time Warping, and Long Short-Term Memory Neural Networks (LSTM). The unsupervised models use a single ground truth time window as input and aim to reconstruct the time window using prior knowledge. The hypothesis is that when the models are trained on healthy, normal operations, the model performs well in reconstructing any normal operation data. However, when degradation increases, the deviation will increase as well. As such, the degradation can be measured through the deviation of the model. The degradation measure can also be used for modeling the RUL using an exponential model for our case study.Results: We tested our model pipeline's capabilities on an industry case study: the of bearing degradation in a water-cooled direct drive rotary motor. As a result, our model pipeline successfully predicted failure up to 11 hours in advance using information from the torque applied to the motor shaft.Conclusion: The experimental results demonstrated that (1) controller data can contain degradation information, (2) it is possible to process the controller data into a health indicator, and (3) the RUL of a motor can be estimated based on the health degradation data. We can estimate the RUL of a motor based on the health degradation data, and our model provides a robust and fast method for doing so. Our model can estimate the RUL up to 11 hours in advance, which is sufficient for manufacturers to organize a servicing event while avoiding serious impact on the production plan.
Author: Thanh Trung Le
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Predictive maintenance plays a crucial role in maintaining continuous production systems since it can help to reduce unnecessary intervention actions and avoid unplanned breakdowns. Indeed, compared to the widely used condition-based maintenance (CBM), the predictive maintenance implements an additional prognostics stage. The maintenance actions are then planned based on the prediction of future deterioration states and residual life of the system. In the framework of the European FP7 project SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment), this thesis concentrates on the development of stochastic deterioration models and the associated remaining useful life (RUL) estimation methods in order to be adapted in the project application cases. Specifically, the thesis research work is divided in two main parts. The first one gives a comprehensive review of the deterioration models and RUL estimation methods existing in the literature. By analyzing their advantages and disadvantages, an adaption of the state of the art approaches is then implemented for the problem considered in the SUPREME project and for the data acquired from a project's test bench. Some practical implementation aspects, such as the issue of delivering the proper RUL information to the maintenance decision module are also detailed in this part. The second part is dedicated to the development of innovative contributions beyond the state-of-the-are in order to develop enhanced deterioration models and RUL estimation methods to solve original prognostics issues raised in the SUPREME project. Specifically, to overcome the co-existence problem of several deterioration modes, the concept of the "multi-branch" models is introduced. It refers to the deterioration models consisting of different branches in which each one represent a deterioration mode. In the framework of this thesis, two multi-branch model types are presented corresponding to the discrete and continuous cases of the systems' health state. In the discrete case, the so-called Multi-branch Hidden Markov Model (Mb-HMM) and the Multi-branch Hidden semi-Markov model (Mb-HsMM) are constructed based on the Markov and semi-Markov models. Concerning the continuous health state case, the Jump Markov Linear System (JMLS) is implemented. For each model, a two-phase framework is carried out for both the diagnostics and prognostics purposes. Through numerical simulations and a case study, we show that the multi-branch models can help to take into account the co-existence problem of multiple deterioration modes, and hence give better performances in RUL estimation compared to the ones obtained by standard "single branch" models.
Author: Diego Galar
Publisher: CRC Press
ISBN: 9781003097242
Category : Technology & Engineering
Languages : en
Pages : 461
Get Book Here
Book Description
Maintenance combines various methods, tools, and techniques in a bid to reduce maintenance costs while increasing the reliability, availability, and security of equipment. Condition-based maintenance (CBM) is one such method, and prognostics forms a key element of a CBM program based on mathematical models for predicting remaining useful life (RUL). Prognostics and Remaining Useful Life (RUL) Estimation: Predicting with Confidence compares the techniques and models used to estimate the RUL of different assets, including a review of the relevant literature on prognostic techniques and their use in the industrial field. This book describes different approaches and prognosis methods for different assets backed up by appropriate case studies. FEATURES Presents a compendium of RUL estimation methods and technologies used in predictive maintenance Describes different approaches and prognosis methods for different assets Includes a comprehensive compilation of methods from model-based and data-driven to hybrid Discusses the benchmarking of RUL estimation methods according to accuracy and uncertainty, depending on the target application, the type of asset, and the forecast performance expected Contains a toolset of methods and a way of deployment aimed at a versatile audience This book is aimed at professionals, senior undergraduates, and graduate students in all interdisciplinary engineering streams that focus on prognosis and maintenance.
Author: Xiao-Sheng Si
Publisher: Springer
ISBN: 3662540304
Category : Technology & Engineering
Languages : en
Pages : 436
Get Book Here
Book Description
This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.
Author: Amir Asif
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Aging critical infrastructures and valuable machineries together with recent catastrophic incidents such as the collapse of Morandi bridge, or the Gulf of Mexico oil spill disaster, call for an urgent quest to design advanced and innovative prognostic solutions, and efficiently incorporate multi-sensor streaming data sources for industrial development. Prognostic health management (PHM) is among the most critical disciplines that employs the advancement of the great interdependency between signal processing and machine learning techniques to form a key enabling technology to cope with maintenance development tasks of complex industrial and safety-critical systems. Recent advancements in predictive analytics have empowered the PHM paradigm to move from the traditional condition-based monitoring solutions and preventive maintenance programs to predictive maintenance to provide an early warning of failure, in several domains ranging from manufacturing and industrial systems to transportation and aerospace. The focus of the PHM is centered on two core dimensions; the first is taking into account the behavior and the evolution over time of a fault once it occurs, while the second one aims at estimating/predicting the remaining useful life (RUL) during which a device can perform its intended function. The first dimension is the degradation that is usually determined by a degradation model derived from measurements of critical parameters of relevance to the system. Developing an accurate model for the degradation process is a primary objective in prognosis and health management. Extensive research has been conducted to develop new theories and methodologies for degradation modeling and to accurately capture the degradation dynamics of a system. However, a unified degradation framework has yet not been developed due to: (i) structural uncertainties in the state dynamics of the system and (ii) the complex nature of the degradation process that is often non-linear and difficult to model statistically. Thus even for a single system, there is no consensus on the best degradation model. In this regard, this thesis tries to bridge this gap by proposing a general model that able to model the true degradation path without having any prior knowledge of the true degradation model of the system. Modeling and analysis of degradation behavior lead us to RUL estimation, which is the second dimension of the PHM and the second part of the thesis. The RUL is the main pillar of preventive maintenance, which is the time a machine is expected to work before requiring repair or replacement. Effective and accurate RUL estimation can avoid catastrophic failures, maximize operational availability, and consequently reduce maintenance costs. The RUL estimation is, therefore, of paramount importance and has gained significant attention for its importance to improve systems health management in complex fields including automotive, nuclear, chemical, and aerospace industries to name but a few. A vast number of researches related to different approaches to the concept of remaining useful life have been proposed, and they can be divided into three broad categories: (i) Physics-based; (ii) Data-driven, and; (iii) Hybrid approaches (multiple-model). Each category has its own limitations and issues, such as, hardly adapt to different prognostic applications, in the first one, and accuracy degradation issues, in the second one, because of the deviation of the learned models from the real behavior of the system. In addition to hardly sustain good generalization. Our thesis belongs to the third category, as it is the most promising category, in particular, the new hybrid models, on basis of two different architectures of deep neural networks, which have great potentials to tackle complex prognostic issues associated with systems with complex and unknown degradation processes.
Author: Manju Maharjan
Publisher:
ISBN:
Category : Reliability (Engineering)
Languages : en
Pages : 156
Get Book Here
Book Description
Author: Louis Redding
Publisher: Springer
ISBN: 3319121111
Category : Technology & Engineering
Languages : en
Pages : 459
Get Book Here
Book Description
Demonstrating the latest research and analysis in the area of through-life engineering services (TES), this book utilizes case studies and expert analysis from an international array of practitioners and researchers – who together represent multiple manufacturing sectors: aerospace, railway and automotive – to maximize reader insights into the field of through-life engineering services. As part of the EPSRC Centre in Through-life Engineering Services program to support the academic and industrial community, this book presents an overview of non-destructive testing techniques and applications and provides the reader with the information needed to assess degradation and possible automation of through-life engineering service activities . The latest developments in maintenance-repair-overhaul (MRO) are presented with emphasis on cleaning technologies, repair and overhaul approaches and planning and digital assistance. The impact of these technologies on sustainable enterprises is also analyzed. This book will help to support the existing TES community and will provide future studies with a strong base from which to analyze and apply techn9olgical trends to real world examples.
Author: Pai Zheng
Publisher: Elsevier
ISBN: 0323852483
Category : Technology & Engineering
Languages : en
Pages : 254
Get Book Here
Book Description
Smart Product-Service Systems draws on innovative practice and academic research to demonstrate the unique benefits of Smart PSS and help facilitate its effective implementation. This comprehensive guide explains how Smart PSS reshapes product-service design in several unique aspects, including a closed-loop product design and redesign manner, value co-creation with integrated human-machine intelligence, and solution design context-awareness. Readers in industry as well as academia will find this to be an invaluable guide to the current body of technical knowledge on Smart Product-Service Systems (Smart PSS), future research trajectories, and experiences of implementation. Rapid development of information and communication technologies, artificial intelligence, and digital technologies have driven today’s industries towards the so-called digital servitization era. As a result, a promising IT-driven business paradigm, known as Smart Product-Service Systems (Smart PSS) has emerged, where a large amount of low cost, high performance smart, connected products are leveraged, together with their generated on-demand services, as a single solution bundle to meet individual customer needs. Explains what factors a company needs to consider in their transition towards digital servitization and its advantages Describes how this field relates to the sustainability movement, and how Smart PSS can be implemented in a sustainable way Includes detailed case studies from different industries, including DELTA Electronics Inc. Singapore (smart commercialization), COMAC aviation industry (smart manufacturing servitization), and Van High Tech (smart building services)
