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 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook 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 write by Carolin Wagner. This book was released on 2022-08-12. 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 available in PDF, EPUB and Kindle. 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
Secure-by-Design Enterprise Architectures and Business Processes in Supply Chains. Handling Threats from Physical Transport Goods in Parcel Mail Services
Secure-by-Design Enterprise Architectures and Business Processes in Supply Chains. Handling Threats from Physical Transport Goods in Parcel Mail Services - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Secure-by-Design Enterprise Architectures and Business Processes in Supply Chains. Handling Threats from Physical Transport Goods in Parcel Mail Services write by Michael Middelhoff. This book was released on . Secure-by-Design Enterprise Architectures and Business Processes in Supply Chains. Handling Threats from Physical Transport Goods in Parcel Mail Services available in PDF, EPUB and Kindle. 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.
Predictive Maintenance in Dynamic Systems
Predictive Maintenance in Dynamic Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Predictive Maintenance in Dynamic Systems write by Edwin Lughofer. This book was released on 2019-02-28. Predictive Maintenance in Dynamic Systems available in PDF, EPUB and Kindle. 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.
Diagnostics and Prognostics of Engineering Systems: Methods and Techniques
Diagnostics and Prognostics of Engineering Systems: Methods and Techniques - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Diagnostics and Prognostics of Engineering Systems: Methods and Techniques write by Kadry, Seifedine. This book was released on 2012-09-30. Diagnostics and Prognostics of Engineering Systems: Methods and Techniques available in PDF, EPUB and Kindle. 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.
Knowledge-Based Predictive Maintenance for Fleet Management
Knowledge-Based Predictive Maintenance for Fleet Management - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Knowledge-Based Predictive Maintenance for Fleet Management write by Patrick Killeen. This book was released on 2020. Knowledge-Based Predictive Maintenance for Fleet Management available in PDF, EPUB and Kindle. 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.
