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Facilitation of Circular Supply Chains with Digital Technologies
Published in Ifeyinwa Juliet Orji, Frank Ojadi, The Circular Supply Chain, 2023
Ifeyinwa Juliet Orji, Frank Ojadi
Some academic researchers have pointed out the enabling effects of digitalization and IoT on the design and implementation of circular strategies. Nevertheless, the opportunities of IoT for circular economy have yet to be realized in practice. As such, more research is needed to understand what is hindering the uptake of IoT-enabled circular strategies. in all, prior published studies have emphasized the role of IoT to support the implementation of circular strategies and business models in companies, often in the context of PSS. For example, case studies have shown that IoT can support companies in extending the scope of value creation beyond design and manufacturing to “use solutions” and “operations services”. IoT has also been pointed out as a supportive technology for improved maintenance and repair in PSS. Specifically, sensor-enabled prognostics can improve operational reliability and allow for preventive and predictive maintenance, which can extend the service life of products and systems. Moreover, by collecting data from the use phases, companies can continuously improve the design of their products, for example, to enhance durability.
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Published in Barney L. Capehart, Timothy Middelkoop, Paul J. Allen, David C. Green, Handbook of Web Based Energy Information and Control Systems, 2020
Michael R. Brambley, Srinivas Katipamula
Automated fault detection and diagnostics will lead to greater awareness of system conditions throughout buildings on a continuous basis (see Figure 30-1). Corrective actions will be enacted automatically by “aware” agents capable of correcting faults in some cases (e.g., correcting a control schedule or fixing an incorrect set point). In cases where automatic fault correction is not possible, notifications will be provided to building staff and management regarding faults and their costs. No longer will faults go unrecognized or will an engineer need to study data patterns to detect them. The operating state of building systems will be known, along with the performance and cost impacts of problems, so priorities for operation and maintenance can be made with complete information. Prognostic techniques will automatically predict the remaining serviceable life of equipment and suggest condition-based maintenance actions. Automated proactive testing (as in reference [24]) will be the basis for short-term functional testing. These tests allow a wide range of conditions to be simulated over a relatively short period of time so that problems can be detected faster than if only passive observation of routine operation is used. Proactive testing will enable consistent performance of functional tests automatically during initial commissioning and then at regular periods or when needed throughout the life of the building.
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Published in Barney L. Capehart, Lynne C. Capehart, Paul Allen, David Green, Web Based Enterprise Energy and Building Automation Systems, 2020
Michael R. Brambley, Srinivas Katipamula
Automated fault detection and diagnostics will lead to greater awareness of system conditions throughout buildings on a continuous basis (see Figure 16-1). Corrective actions will be enacted automatically by “aware” agents capable of correcting faults in some cases (e.g., correcting a control schedule or fixing an incorrect set point). In cases where automatic fault correction is not possible, notifications will be provided to building staff and management regarding faults and their costs. No longer will faults go unrecognized or will an engineer need to study data patterns to detect them. The operating state of building systems will be known, along with the performance and cost impacts of problems, so priorities for operation and maintenance can be made with complete information. Prognostic techniques will automatically predict the remaining serviceable life of equipment and suggest condition-based maintenance actions. Automated proactive testing will be the basis for short-term functional testing. These tests allow a wide range of conditions to be simulated over a relatively short period of time so that problems can be detected faster than if only passive observation of routine operation is used. Proactive testing will enable consistent performance of functional tests automatically during initial commissioning and then at regular periods or when needed throughout the life of the building.
A Review of machine learning techniques for wind turbine’s fault detection, diagnosis, and prognosis
Published in International Journal of Green Energy, 2023
Prince Waqas Khan, Yung-Cheol Byun
The installation of large-scale wind turbines necessitates the development of complex operation and maintenance procedures in order to guarantee that the machines are risk-free, lucrative, and efficient Byon (2013). On the basis of condition measurements, prognostics attempts to estimate the amount of time that physical systems still have left in their remaining useful life (RUL) Cheng, Liyan, and Qiao (2017). Through data analysis, the application of diagnostic procedures, and the application of machinery prognostic algorithms, one can arrive at an accurate prediction of the remaining life of the machinery as well as any potential failures that may occur. Predicting the RUL of an operational wind turbine gearbox using vibration measurements is the goal of the paper by Elasha et al. (2019), which proposes combining two supervised machine learning techniques, namely a regression model and a multilayer artificial neural network model. In order to determine the stages of bearing failure, the root mean square (RMS), Kurtosis (KU), and energy index (EI) statistics were analyzed. To test and evaluate the proposed method, a case study was done in which vibration readings were taken from a high-speed shaft bearing used in the gearbox of a wind turbine.
Prognostics and Health Management for Maintenance-Dependent Processes
Published in Nuclear Technology, 2023
Hang Xiao, Alex Hines, Fan Zhang, Jamie B. Coble, J. Wes Hines
There are three main challenges in implementing maintenance-dependent prognostic models in current NPPs: availability of robust sensor and process data, lack of run toward failure data, and access to standard and easily interpretable maintenance records. Probably the most significant challenge in the industrial application of PHM is the availability of a robust sensor suite and its process data. NPPs, especially those built a long time ago, may not have sensors installed that are necessary to detect degradation anomalies and then make reliable prognostic predictions. In Sec. III.C, to transition to multiple fault diagnostics and prognostics, cooling water pump pressure is used to differentiate fault modes. However, there might not be a pressure sensor to collect cooling water pump outlet pressure. Therefore, it is required to have correctly placed sensors and a data historian to collect degradation data over several years. Additionally, for multiple fault processes, additional sensors are required to isolate degradation mechanisms.
An efficient cloud prognostic approach for aircraft engines fleet trending
Published in International Journal of Computers and Applications, 2020
Zohra Bouzidi, Labib Sadek Terrissa, Noureddine Zerhouni, Soheyb Ayad
The prognostic process in industrial maintenance is a main step to predict failure in machinery. In order to estimate the RUL for a machine before a failure, many works in PHM domain have shown that to realize a reliable estimation, the necessity of: An ubiquitous access and the maintenance availability at any time and everywhere.A big infrastructure for running solutions and big memory space for storage data.The communication between factories who have many distributed sites and sharing the experiences that can be easily reused by other industrial users who have similar needs.Multi-tenant application, an architecture where a single application instance will serve multiple clients (tenants), to reduce maintenance costs and improve scalability.The security of maintenance’s data and user’s. To satisfy these requirements, we switched to an IT-based solution by introducing the cloud computing paradigm. Technological advances and the new ideology, namely X as a Service, brought by the emerging cloud computing paradigm are opening new opportunities to tackle existing hurdles for implementation of PHM systems.