D
Dong Zhen
Researcher at Hebei University of Technology
Publications - 86
Citations - 1002
Dong Zhen is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has an hindex of 14, co-authored 68 publications receiving 512 citations. Previous affiliations of Dong Zhen include Hebei University & University of Huddersfield.
Papers
More filters
Journal ArticleDOI
A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles
TL;DR: A review of the state-of-the-art online SOC and SOH evaluation technologies published within the recent five years in view of their advantages and limitations and suggests future work in the real-time battery management technology.
Journal ArticleDOI
A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
Omar AlShorman,Muhammad Irfan,Nordin Saad,Dong Zhen,Noman Haider,Adam Glowacz,Ahmad Alshorman +6 more
TL;DR: This work presents an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods, and highlights the advantages and performance limitations of each method.
Journal ArticleDOI
Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping
TL;DR: In this article, the authors used dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor.
Journal ArticleDOI
Helical gear wear monitoring: Modelling and experimental validation
TL;DR: In this paper, a comprehensive dynamic model was developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication principles.
Journal ArticleDOI
Autocorrelated Envelopes for early fault detection of rolling bearings
TL;DR: A detector based on Ensemble Average of Autocorrelated Envelopes (EAAE) is proposed to identify the early occurrence faults in rolling element bearings, of which the fault induced vibration signals are inevitably contaminated or masked by both additive background noise and random phase noise.