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.
Aerospace Predictive Maintenance
Aerospace Predictive Maintenance - 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 Aerospace Predictive Maintenance write by Charles Edwin Dibsdale. This book was released on 2020-12-30. Aerospace Predictive Maintenance available in PDF, EPUB and Kindle. Aerospace Predictive Maintenance: Fundamental Concepts, written by longtime practitioner Charles E. Dibsdale based in the UK, considers PdM a subset of Condition Based Maintenance (CBM), and must obey the same underlying rules and pre-requisites that apply to it. Yet, PdM is new because it takes advantage of emerging digital technology in sensing, acquiring data, communicating the data, and processing it. This capability can autonomously analyse the data and send alerts and advice to decision makers, potentially reducing through-life cost and improving safety. Aerospace Predictive Maintenance: Fundamental Concepts provides a history of maintenance, and how performance, safety and the environment make direct demands on maintenance to deliver more for less in multiple industries. It also covers Integrated Vehicle Health Management (IVHM) that aims to provide a platformcentric framework for PdM in the mobility domain. The book discusses PdM maturity, offering a context of the transformation of data through information and knowledge. Understanding some of the precepts of knowledge management provides a really useful and powerful perspective on PdM as an information system. On the other hand, Aerospace Predictive Maintenance: Fundamental Concepts also discusses disadvantages of PdM and shows how these may be addressed. One of the fundamental changes PdM implies is a shift from deterministic black-and-white thinking to more nuanced decision making informed by probabilities and uncertainty. Other concerns such as data management, privacy and ownership are tackled as well. Aerospace Predictive Maintenance: Fundamental Concepts covers additional technologies, such as the Industrial Internet of Things (IIOT) that will result in proliferation of cheap, wireless, ultra-low-power sensors, and will transform PdM into a more economical option. The book brings in the future possibilities of nano technology, which can be used for new sensors, micro-robotics for inspections and self-healing/repairing of systems which can be intergrated with PdM.
A Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus
A Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus - 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 Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus write by Wenjie Chen. This book was released on 2020. A Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus available in PDF, EPUB and Kindle. This thesis describes the design and implementation of constructing a predictive maintenance system for a hybrid vehicle to meet the requirements of the STO (Société de transport de l'Outaouais). Thousands of sensors installed on the bus allow us to observe the real-time performance of the bus while it is running. Abnormal sensor values represent adverse operating conditions and bring attention to the inevitable failures of a bus's components. Therefore, by analyzing real-time sensor streams, predictive maintenance is accomplished based on the unnatural behaviour of a hybrid bus. Currently, transport companies still employ traditional methods of maintenance planning, such as emergency maintenance and preventive maintenance. Traditional maintenance strategies require a great deal of technicians and time to inspect the buses regularly and carefully. In comparison, predictive maintenance can monitor the performance of buses based on the condition of their equipment. To collect data from the hybrid bus and share data with the Internet, IoT technology is adopted to develop predictive maintenance architecture for a fleet management system. Our team devised an IoT architecture for the fleet management system, including the perception layer, middleware layer and application layer. My work focuses on the perception layer, which is responsible for analyzing sensor values, reporting failures of a hybrid bus and connecting with cloud-servers. As one of the predictive maintenance methods, the expert system (also known as a knowledge-based expert system) is built to store expert knowledge in a specific area. The expert system presented in this thesis can store failures of hybrid buses, symptoms of which were provided to us by technicians from the STO. Such breakdowns assist the expert system in predicting the malfunctions of the bus's components based on the symptoms. Inspired by the IDEA methodology, failure symptoms can be represented by active rules with three essential components: event, condition and action. These rules can also be translated into active database features like triggers and mapped into an active database. A gateway is installed on a bus and composed of four modules: data acquisition module, active rules module, rules management module and user interface module. Within the parameters of the architecture and the gateway, this thesis analyzes the entities, relationships and operations in the dynamic system and forms a relational database to store the information related to the bus and active rules.
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
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
Analytical Fleet Maintenance Management
Analytical Fleet Maintenance 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 Analytical Fleet Maintenance Management write by John E Dolce. This book was released on 2009-06-04. Analytical Fleet Maintenance Management available in PDF, EPUB and Kindle. 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.
