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Methodology on Exact Extraction of Time Series Features for Robust Prognostics and Health MonitoringJin, Chao 30 October 2017 (has links)
No description available.
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Cyber-Physical System Augmented Prognostics and Health Management for Fleet-Based SystemsLiu, Zongchang 15 May 2018 (has links)
No description available.
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Evaluation of Health Assessment Techniques for Rotating MachinerySiegel, David January 2009 (has links)
No description available.
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Techniques for Real-Time Tire Health Assessment and Prognostics under Dynamic Operating ConditionsXu, Su January 2011 (has links)
No description available.
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Data Quality Assessment Methodology for Improved Prognostics ModelingChen, Yan 19 April 2012 (has links)
No description available.
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A Comparative Study of Prognostic and Health Assessment Methods in Sensor Rich and Sensorless EnvironmentsSkirtich, Tyler 24 September 2012 (has links)
No description available.
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Fault Detection in a Network of Similar Machines using Clustering ApproachLapira, Edzel R. 05 October 2012 (has links)
No description available.
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An Adaptive Prognostic Methodology and System Framework for Engineering Systems under Dynamic Working RegimesYang, Shanhu 24 May 2016 (has links)
No description available.
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69 |
Performing Diagnostics & Prognostics On Simulated Engine Failures Using Neural NetworksMacmann, Owen 28 June 2016 (has links)
No description available.
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Assessing readiness for implementation of prognostics and health management in small and medium enterprisesFuller, Sara C 09 August 2022 (has links)
Prognostics and Health Management (PHM) refers to using robust sensing, monitoring, and control to detect, assess, and track system health degradation and failure modes, allowing for enhanced management and operational decisions. The need for PHM within a manufacturing facility has increased due to a variety of reasons, such as the increasing complexity of manufacturing equipment.
A lack of readiness for digital implementations is linked to failure. The literature highlights certain barriers and enablers that can signal whether a technology implementation will be successful, such as management and maintenance employees’ desire to change the existing process, an understanding and willingness to take risks with technology, and having employees with the right competencies and motivations.
This thesis identifies barriers and enablers related a successful PHM implementation and develops an assessment tool to identify a company’s readiness level as well as recommendations for increasing the probability of success.
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