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Skip to Search Results- 14Fault Detection
- 3Machine Learning
- 2Fault Diagnosis
- 2Process Monitoring
- 2Rotating Machinery
- 1Adaptive Sampling
- 1Arifin, B M Sirajeel
- 1Bahador Rashidi
- 1Choudhury, M. A. A. Shoukat
- 1Gonzalez, Ruben T
- 1Hajizadeh, Mohammad
- 1Halim, Enayet B.
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Monitoring of Industrial Processes via Non-stationary Probabilistic Slow Feature Analysis Machine Learning Algorithm
DownloadSpring 2020
This research develops a first of its kind machine learning (ML) algorithm, called probabilistic slow feature analysis (PSFA), that monitors and detects faults for non-stationary industrial processes. The novelty of this ML algorithm is that it can monitor and detect faults for non-stationary...
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Near-Field to Far-Field Transformation and Fault Detection Using Adaptive Sampling and Machine Learning in Source Reconstruction Method
DownloadFall 2019
Until not so long ago, near-field and far-field measurement techniques were the two prominent approaches to evaluating antennas. A direct far-field measurement can be conducted in outdoor or indoor environments. The measurement of small antennas can be performed in anechoic chambers. For large...
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Spring 2012
Nadadoor Srinivasan, Venkat R.
Biological engineering is a domain of study that involves applying known engineering principles to biological systems. Qualitative studies in the field of biology have undergone tremendous advancements in the last two decades but quantitation is still in its early stages due to various...
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Fall 2012
This thesis develops an online parameter and state estimation scheme for a linear parameter varying (LPV) system that utilizes an iterative moving window technique. In the proposed scheme, an online algorithm, based on the input/output measurement, is implemented to approximate the real system...