A Process-Centric View on Predictive Maintenance and Fleet Prognostics. Development of a Process Reference Model and a Development Method for Fleet Prognostics to Guide Predictive Maintenance Projects PDF Download
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Author: Carolin Wagner
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832555153
Category : Computers
Languages : en
Pages : 320
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Book Description
In the age of digitalization and the fourth industrial revolution, predictive maintenance is becoming increasingly important as a proactive maintenance type. Despite the economic benefits that predictive maintenance generates for companies, its practical application is still in its early stages. This is often due to two prevailing challenges. First, there is a deficiency of knowledge about predictive maintenance and its concrete realization. Second, there is a lack of high quality and rich data of historical machine failures. To increase the representativeness of data, data from several similar machines (i.e. a fleet) should be considered. To foster the effective implementation of predictive maintenance, supportive guidance in the realization of a predictive maintenance project is needed. For this reason, this dissertation presents a process reference model and a development method for fleet prognostics. The process reference model describes a comprehensive and application-independent view of the complete predictive maintenance process. The model is supplemented by the fleet prognostic development method. To address the specific characteristics of the fleet, a systematic process is depicted which provides a means to assess the heterogeneity of the fleet from a data-driven perspective and simplifies the design of an algorithm considering fleet data. Finally, the applicability and value of the research results are demonstrated with three industrial cases
Author: Carolin Wagner
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832555153
Category : Computers
Languages : en
Pages : 320
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Book Description
In the age of digitalization and the fourth industrial revolution, predictive maintenance is becoming increasingly important as a proactive maintenance type. Despite the economic benefits that predictive maintenance generates for companies, its practical application is still in its early stages. This is often due to two prevailing challenges. First, there is a deficiency of knowledge about predictive maintenance and its concrete realization. Second, there is a lack of high quality and rich data of historical machine failures. To increase the representativeness of data, data from several similar machines (i.e. a fleet) should be considered. To foster the effective implementation of predictive maintenance, supportive guidance in the realization of a predictive maintenance project is needed. For this reason, this dissertation presents a process reference model and a development method for fleet prognostics. The process reference model describes a comprehensive and application-independent view of the complete predictive maintenance process. The model is supplemented by the fleet prognostic development method. To address the specific characteristics of the fleet, a systematic process is depicted which provides a means to assess the heterogeneity of the fleet from a data-driven perspective and simplifies the design of an algorithm considering fleet data. Finally, the applicability and value of the research results are demonstrated with three industrial cases
Author: Michael Middelhoff
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832557083
Category :
Languages : en
Pages : 272
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Book Description
Supply chain security encompasses measures preventing theft, smuggling, and sabotage through heightened awareness, enhanced visibility, and increased transparency. This necessitates the adoption of a security-by-design paradigm to achieve effective and efficient security measures, yielding additional benefits such as diminished supply chain costs. Given their vulnerability, transportation and logistics service providers play a pivotal role in supply chain security. This thesis leverages systems security engineering and security-by-design to provide a methodology for designing and evaluating security measures for physical transport goods. It formulates nine principles that define security-by-design and establishes a supply chain security framework. An adaptation of the TOGAF architecture development facilitates the creation of secure-by-design enterprise architectures. Security measures are documented using security-enhanced processes based on BPMN. This enables an analysis and compliance assessment to ascertain the alignment of security with business objectives and the adequate implementation of requirements. The culmination of these efforts is exemplified through a case study.
Author: Edwin Lughofer
Publisher: Springer
ISBN: 3030056457
Category : Technology & Engineering
Languages : en
Pages : 567
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Book Description
This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.
Author: Kadry, Seifedine
Publisher: IGI Global
ISBN: 146662096X
Category : Technology & Engineering
Languages : en
Pages : 461
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Book Description
Industrial Prognostics predicts an industrial systems lifespan using probability measurements to determine the way a machine operates. Prognostics are essential in determining being able to predict and stop failures before they occur. Therefore the development of dependable prognostic procedures for engineering systems is important to increase the systems performance and reliability. Diagnostics and Prognostics of Engineering Systems: Methods and Techniques provides widespread coverage and discussions on the methods and techniques of diagnosis and prognosis systems. Including practical examples to display the methods effectiveness in real-world applications as well as the latest trends and research, this reference source aims to introduce fundamental theory and practice for system diagnosis and prognosis.
Author: Hong Sun
Publisher:
ISBN:
Category : Plant maintenance
Languages : en
Pages : 174
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Book Description
Author: Diego Galar
Publisher: CRC Press
ISBN: 9781003097242
Category : Technology & Engineering
Languages : en
Pages : 461
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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: Patrick Killeen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
In recent years, advances in information technology have led to an increasing number of devices (or things) being connected to the internet; the resulting data can be used by applications to acquire new knowledge. The Internet of Things (IoT) (a network of computing devices that have the ability to interact with their environment without requiring user interaction) and big data (a field that deals with the exponentially increasing rate of data creation, which is a challenge for the cloud in its current state and for standard data analysis technologies) have become hot topics. With all this data being produced, new applications such as predictive maintenance are possible. One such application is monitoring a fleet of vehicles in real-time to predict their remaining useful life, which could help companies lower their fleet management costs by reducing their fleet's average vehicle downtime. Consensus self-organized models (COSMO) approach is an example of a predictive maintenance system for a fleet of public transport buses, which attempts to diagnose faulty buses that deviate from the rest of the bus fleet. The present work proposes a novel IoT-based architecture for predictive maintenance that consists of three primary nodes: namely, the vehicle node (VN), the server leader node (SLN), and the root node (RN). The VN represents the vehicle and performs lightweight data acquisition, data analytics, and data storage. The VN is connected to the fleet via its wireless internet connection. The SLN is responsible for managing a region of vehicles, and it performs more heavy-duty data storage, fleet-wide analytics, and networking. The RN is the central point of administration for the entire system. It controls the entire fleet and provides the application interface to the fleet system. A minimally viable prototype (MVP) of the proposed architecture was implemented and deployed to a garage of the Soci\'et\'e de Transport de l'Outaouais (STO), Gatineau, Canada. The VN in the MVP was implemented using a Raspberry Pi, which acquired sensor data from a STO hybrid bus by reading from a J1939 network, the SLN was implemented using a laptop, and the RN was deployed using meshcentral.com. The goal of the MVP was to perform predictive maintenance for the STO to help reduce their fleet management costs. The present work also proposes a fleet-wide unsupervised dynamic sensor selection algorithm, which attempts to improve the sensor selection performed by the COSMO approach. I named this algorithm the improved consensus self-organized models (ICOSMO) approach. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a STO hybrid bus, which was acquired using the MVP, was used to generate synthetic data to simulate vehicles, faults, and repairs. The deviation detection of the COSMO and ICOSMO approach was applied to the synthetic sensor data. The simulation results were used to compare the performance of the COSMO and ICOSMO approach. Results revealed that in general ICOSMO improved the accuracy of COSMO when COSMO was not performing optimally; that is, in the following situations: a) when the histogram distance chosen by COSMO was a poor choice, b) in an environment with relatively high sensor white noise, and c) when COSMO selected poor sensors. On average ICOSMO only rarely reduced the accuracy of COSMO, which is promising since it suggests deploying ICOSMO as a predictive maintenance system should perform just as well or better than COSMO . More experiments are required to better understand the performance of ICOSMO. The goal is to eventually deploy ICOSMO to the MVP.
Author: Gerardus Blokdyk
Publisher: Createspace Independent Publishing Platform
ISBN: 9781985039407
Category :
Languages : en
Pages : 126
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Book Description
How do we Lead with Predictive maintenance in Mind? Who sets the Predictive maintenance standards? How frequently do you track Predictive maintenance measures? Are there any specific expectations or concerns about the Predictive maintenance team, Predictive maintenance itself? How did the Predictive maintenance manager receive input to the development of a Predictive maintenance improvement plan and the estimated completion dates/times of each activity? This astounding Predictive maintenance self-assessment will make you the reliable Predictive maintenance domain auditor by revealing just what you need to know to be fluent and ready for any Predictive maintenance challenge. How do I reduce the effort in the Predictive maintenance work to be done to get problems solved? How can I ensure that plans of action include every Predictive maintenance task and that every Predictive maintenance outcome is in place? How will I save time investigating strategic and tactical options and ensuring Predictive maintenance opportunity costs are low? How can I deliver tailored Predictive maintenance advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Predictive maintenance essentials are covered, from every angle: the Predictive maintenance self-assessment shows succinctly and clearly that what needs to be clarified to organize the business/project activities and processes so that Predictive maintenance outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Predictive maintenance practitioners. Their mastery, combined with the uncommon elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Predictive maintenance are maximized with professional results. Your purchase includes access details to the Predictive maintenance self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.
Author: John E Dolce
Publisher: SAE International
ISBN: 076806905X
Category : Technology & Engineering
Languages : en
Pages : 538
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Book Description
This new edition of Analytical Fleet Maintenance Management, the first update in more than a decade, details state-of-the-art technologies that can benefit fleet managers, and reviews the latest best practices in fleet maintenance management. This third edition contains new chapters on fleet management leadership, and facility design and maintenance, as well as updated arithmetic formulas throughout the book.
Author: Changhua Hu
Publisher: Springer Nature
ISBN: 9811622671
Category : Technology & Engineering
Languages : en
Pages : 278
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Book Description
This book addresses remaining life prediction and predictive maintenance of equipment. It systematically summarizes the key research findings made by the author and his team and focuses on how to create equipment performance degradation and residual life prediction models based on the performance monitoring data produced by currently used and historical equipment. Some of the theoretical results covered here have been used to make remaining life predictions and maintenance-related decisions for aerospace products such as gyros and platforms. Given its scope, the book offers a valuable reference guide for those pursuing theoretical or applied research in the areas of fault diagnosis and fault-tolerant control, remaining life prediction, and maintenance decision-making.
