F
Fang Duan
Researcher at London South Bank University
Publications - 47
Citations - 442
Fang Duan is an academic researcher from London South Bank University. The author has contributed to research in topics: Condition monitoring & Bearing (mechanical). The author has an hindex of 9, co-authored 43 publications receiving 323 citations. Previous affiliations of Fang Duan include University of Adelaide.
Papers
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Canonical Variable Analysis and Long Short-term Memory for Fault Diagnosis and Performance Estimation of a Centrifugal Compressor
TL;DR: A dynamic process monitoring method based on canonical variable analysis (CVA) and long short-term memory (LSTM) that can effectively detect process abnormalities and perform multi-step-ahead prediction of the system's behavior after the appearance of a fault is proposed.
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A study on helicopter main gearbox planetary bearing fault diagnosis
TL;DR: Diagnosis of a MGB planetary bearing with seeded defect was investigated and results indicate that the seeded planetary bearing defect was successfully detected in both test cases.
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Condition Monitoring of an Induction Motor Stator Windings Via Global Optimization Based on the Hyperbolic Cross Points
Fang Duan,Rastko Zivanovic +1 more
TL;DR: A novel method that enables efficient and accurate monitoring of the stator winding circuit fault and is based on the sparse grid optimization method applied in the least squares estimation of the circuit parameters that characterize the condition of a fault incipient.
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Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm
TL;DR: A novel offline induction motor parameter estimation method based on sparse grid optimization algorithm that is noninvasive as it uses external measurements, resulting in reduced system complexity and cost.
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Multidimensional prognostics for rotating machinery: A review
TL;DR: A review of multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors can be found in this article, where the authors provide a guide for researchers considering prognosis options for multi-sensor rotating equipment.