Author
Caichao Zhu
Bio: Caichao Zhu is an academic researcher from Chongqing University. The author has contributed to research in topics: Lubrication & Turbine. The author has an hindex of 22, co-authored 65 publications receiving 1142 citations.
Topics: Lubrication, Turbine, Tooth surface, Residual stress, Spiral bevel gear
Papers
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TL;DR: This work reviews gear lubrication papers with focus on gear efficiency, contact fatigue and dynamics, and compile and categorize key investigations in an expansive field with substantial recent research.
99 citations
21 Mar 2018
TL;DR: In this article, the reliability of wind turbines has become a hotspot in wind power research and the failure modes, failure causes and detection methods of some key components in the wind turbines are summarized and frequently used methods of reliability analysis and research status of wind turbine reliability are analyzed.
Abstract: With the rapid development of wind power industry, the reliability of wind turbines has become a hotspot in wind power research. The failure modes and research progress of wind turbine reliability both at home and abroad are analyzed. The failure modes, failure causes and detection methods of some key components in the wind turbines are summarized. Also, the frequently used methods of reliability analysis and research status of wind turbine reliability are analyzed. Following this, research focuses, methods and measures to improve wind turbine reliability are presented. We also shed light on the condition monitoring and assessment process with condition monitoring system and supervisory control and data acquisition. It is of great significance to reduce the cost of operation and maintenance and to improve the safety of wind turbines.
72 citations
TL;DR: In this article, a thermal starved elastohydrodynamic lubrication (EHL) model is proposed to study effect of starved lubrication on the contact performance of a spur gear pair.
60 citations
TL;DR: In this article, the effect of angular eccentric error on gear geometry is more sensitive than that of radial eccentric error, and the length of contact pattern is firstly decreased and then increased in one period.
59 citations
TL;DR: In this article, a multiple-shot model was used to estimate the evolution of shot peening coverage and the influences of coverage on residual stress, plastic strain and surface topography, and the predicted results reveal that as the shot-peening coverage increases from 100% to 400% under the given processing condition, the maximum residual stress and plastic strain will increase gradually.
55 citations
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TL;DR: In this paper, the authors provide failure rates for the overall wind turbine and its sub-assemblies and the failure modes for the components/subassemblies that are the highest contributors to the overall failure rate.
Abstract: Determining and understanding offshore wind turbine failure rates and resource requirement for repair are vital for modelling and reducing O&M costs and in turn reducing the cost of energy. While few offshore failure rates have been published in the past even less details on resource requirement for repair exist in the public domain. Based on ~350 offshore wind turbines throughout Europe this paper provides failure rates for the overall wind turbine and its sub-assemblies. It also provides failure rates by year of operation, cost category and failure modes for the components/sub-assemblies that are the highest contributor to the overall failure rate. Repair times, average repair costs and average number of technicians required for repair are also detailed in this paper. An onshore to offshore failure rate comparison is carried out for generators and converters based on this analysis and an analysis carried out in a past publication. The results of this paper will contribute to offshore wind O&M cost and resource modelling and aid in better decision making for O&M planners and managers.
400 citations
TL;DR: In this article, the authors discuss recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines.
Abstract: The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.
287 citations
TL;DR: In this paper, the authors proposed the strategy of tailoring strain delocalization to evade long-standing strength-ductility trade-off dilemma, where the achieving of strengthductility synergy depends on the delocalizing of localized strains.
197 citations
TL;DR: A novel data-driven approach is proposed for wind power forecasting by integrating data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter tuning based on gated recurrent deep learning models, which is systematically presented for the first time.
139 citations
TL;DR: The experimental results show that this method can predict the remaining life of gears and bearings well, and it has higher prediction accuracy than the conventional prediction methods.
Abstract: In the mechanical transmission system, the gear is one of the most widely used transmission components. The failure of the gear will cause serious accidents and huge economic loss. Therefore, the remaining life prediction of the gear is of great importance. In order to accurately predict the remaining life of the gear, a new type of long-short-term memory neural network with macroscopic–microscopic attention (MMA) is proposed in this article. First, some typical time-domain and frequency-domain characteristics of vibration signals are calculated, respectively, such as the maximum value, the absolute mean value, the standard deviation, the kurtosis, and so on. Then, the principal component of these characteristics is extracted by the isometric mapping method. The importance of fusional characteristic information is filtered via a proposed MMA mechanism so that the input weight of neural network data and recursive data can reach multilevel real-time amplification. With the new long short-term memory neural network, the health characteristics of gear vibration signals can be predicted based on the known fusion features. The experimental results show that this method can predict the remaining life of gears and bearings well, and it has higher prediction accuracy than the conventional prediction methods.
129 citations