Zobrazeno 1 - 10
of 233
pro vyhledávání: '"Farahat, Ahmed"'
Autor:
Lee, Xian Yeow, Kumar, Aman, Vidyaratne, Lasitha, Rao, Aniruddha Rajendra, Farahat, Ahmed, Gupta, Chetan
Publikováno v:
2023 IEEE International Conference on Prognostics and Health Management (ICPHM)
This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig. Various traditional machine learning and deep learning methods have been proposed for multiv
Externí odkaz:
http://arxiv.org/abs/2305.05532
Autor:
Yadav, Nishant, Alam, Mahbubul, Farahat, Ahmed, Ghosh, Dipanjan, Gupta, Chetan, Ganguly, Auroop R.
Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains. While such adversarial approaches achieve domain-level alignment,
Externí odkaz:
http://arxiv.org/abs/2301.03826
Publikováno v:
In Annual Conference of the PHM Society, vol. 12, no. 1, pp. 9-9. 2020
Traditionally, fault detection and isolation community has used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to
Externí odkaz:
http://arxiv.org/abs/2110.15385
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to the remaining useful life estimation or likelihood of failure in near
Externí odkaz:
http://arxiv.org/abs/2109.15050
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time series models, for a considered data analytics task, provides useful guida
Externí odkaz:
http://arxiv.org/abs/2103.09348
Autor:
Wang, Lijing, Ghosh, Dipanjan, Diaz, Maria Teresa Gonzalez, Farahat, Ahmed, Alam, Mahbubul, Gupta, Chetan, Chen, Jiangzhuo, Marathe, Madhav
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant behavior. From an AI model perspective it means given the same input the u
Externí odkaz:
http://arxiv.org/abs/2011.06796
Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches for prognostics that leverage the power of machine learning models are g
Externí odkaz:
http://arxiv.org/abs/2006.03729
Publikováno v:
In Journal of Clinical Orthopaedics and Trauma November 2023 46
Autor:
Hu, Jueming, Wang, Haiyan, Tang, Hsiu-Khuern, Kanazawa, Takuya, Gupta, Chetan, Farahat, Ahmed
Publikováno v:
In Computers & Industrial Engineering November 2023 185
Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the key challenges in PHM is to accurately predict impending failures in the
Externí odkaz:
http://arxiv.org/abs/1910.02034