Bingchang Hou
Shanghai Jiao Tong University
32 Papers
1 Citations
Bingchang Hou is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Fault detection and isolation. The author has an hindex of 4, co-authored 7 publications. Previous affiliations of Bingchang Hou include Chongqing University.
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Papers
Investigations on quasi-arithmetic means for machine condition monitoring
TL;DR: Experimental results demonstrate that some special cases of the generalized framework can simultaneously detect incipient rotating faults, exhibit a monotonic degradation tendency and be robust to impulsive noises, and they are better than existing sparsity measures for machine health monitoring.
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Interpretable online updated weights: Optimized square envelope spectrum for machine condition monitoring and fault diagnosis
TL;DR: In this paper , a fault cyclostationarity-based convex optimization model was proposed to solve the problem of fault detection in rotating machines, and an online weight updating algorithm was developed to relieve the requirement of historical data and to make the weight updating of the proposed optimization model adaptive to online monitoring data.
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A tacholess order tracking method for wind turbine planetary gearbox fault detection
TL;DR: A novel tacholess order tracking method based on generalized demodulation (GD) for WT fault detection, where the phase reference information is obtained from the generator shaft vibration signal through GD and Hilbert transform rather than the gearbox vibration signal.
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Generalized Gini indices: Complementary sparsity measures to Box-Cox sparsity measures for machine condition monitoring
TL;DR: In this article , generalized Gini indices (GGIs) are proposed to quantify the sparsity changes of Bernoulli coefficients, which can be used in any algorithms that need sparsity measures.
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Difference mode decomposition for adaptive signal decomposition
TL;DR: In this paper , a new decomposition approach called Difference Mode Decomposition (DMD) is proposed to adaptively decompose a mixed signal into CC, reference components, and noise, and enrich the domain of adaptive mode decomposition.
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