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Reliability assessment of the hydraulic system of wind turbines based on load-sharing using survival signature

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TLDR
The load-sharing based reliability model using survival signature to conduct system reliability assessment is proposed and the effectiveness and feasibility of the proposed methodology are demonstrated by the successful application on the hydraulic system of wind turbines.
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This article is published in Renewable Energy.The article was published on 2020-06-01 and is currently open access. It has received 21 citations till now. The article focuses on the topics: Reliability (statistics) & Fault tree analysis.

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Citations
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Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning

TL;DR: Wang et al. as discussed by the authors proposed an operational state calibration framework based on deep learning and fuzzy synthesis, and three feature-based transfer learning methods were adopted to narrow the discrepancy among the data distribution of the WTGs.
Journal ArticleDOI

Numerically efficient computation of the survival signature for the reliability analysis of large networks

TL;DR: This work presents a new method to approximate the survival signature, where system configurations of low interest are first excluded using percolation theory, while the remaining parts of the signature are approximated by Monte Carlo simulation.
Journal ArticleDOI

Reliability analysis of load-sharing man-machine systems subject to machine degradation, human errors, and random shocks

TL;DR: In this article , a load-sharing man-machine system with multiple MAN-machine units (MMUs) subject to machine degradation, human errors, and random shocks is considered.
Journal ArticleDOI

Efficient reliability analysis of complex systems in consideration of imprecision

TL;DR: The concepts of survival signature, fuzzy probability theory and the two versions of non-intrusive stochastic simulation (NISS) methods are adapted and merged, providing an efficient approach to quantify the reliability of complex systems taking into account the whole uncertainty spectrum.
References
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Book

Statistical Models and Methods for Lifetime Data

TL;DR: Inference procedures for Log-Location-Scale Distributions as discussed by the authors have been used for estimating likelihood and estimating function methods. But they have not yet been applied to the estimation of likelihood.
Journal ArticleDOI

Probability and Statistics with Reliability, Queuing, and Computer Science Applications.

TL;DR: Probability and Statistics with Reliability, Queuing and Computer Science Applications, Second Edition, offers a comprehensive introduction to probabiliby, stochastic processes, and statistics for students of computer science, electrical and computer engineering, and applied mathematics.
Journal ArticleDOI

Statistical Models and Methods for Lifetime Data

Gordon Johnston
- 01 Aug 2003 - 
TL;DR: This book describes and illustrates how to compute a simple “naive” variance estimate and conŽ dence intervals that would be correct under the assumption of an underlying nonhomogeneous Poisson process model.
ReportDOI

On the importance of different components in a multicomponent system

TL;DR: A quantitative definition of this notion of importance is proposed in the present paper for systems with coherent structures, assuming that only the structure of the system is known, or that also the reliabilities of all components are known.
Journal ArticleDOI

An annotated overview of system-reliability optimization

TL;DR: An overview of the methods that have been developed since 1977 for solving various reliability optimization problems; applications of these methods to various types of design problems; and heuristics, metaheuristic algorithms, exact methods, reliability-redundancy allocation, multi-objective optimization and assignment of interchangeable components in reliability systems.
Frequently Asked Questions (11)
Q1. What are the contributions in "Reliability assessment of the hydraulic system of wind turbines based on load-sharing using survival signature" ?

To realistically assess the reliability of the hydraulic system, the authors propose in this article the load-sharing based reliability model using survival signature to conduct system reliability assessment. Then the reliability sensitivity with respect to the distribution parameters of redundant components is studied. 

In wind turbines, many redundancy designs are adopted to improve the reliability of the weakest components and assemblies so that the system reliability can be maintained at a safe level. 

In reality, due to the complex working environment and variable operating conditions, hydraulic systems have high failure rates [1, 2]. 

There are three system success function modes for a system of two load-sharing redundant components: both components function, component A fails while component B functions, and component A functions while component B fails. 

according to the failure mechanism of the redundancy systems, once the redundant components fail, the surviving components will take the full load, their failure rates will increase, and their reliability will decrease. 

Markov-based dynamic fault tree analysis (DFTA) not only has the function of the conventional fault tree analysis (FTA) method but also can model55and evaluate the reliability of the problem with dynamic failure characteristics. 

When the WT is forced to stop for the safety,125the reversing valve (14) gains electricity, and unit (16) loses electricity; then it begins to be braked; the WT starts up when unit (16) gains electricity. 

The components in the load-sharing system of wind turbines, whose failure rates are dependent, can share the total workload.320However, these redundancy systems are often seen as parallel systems, in whichcomponents are independent and can not share the load. 

The reliability importance of the type i component can be derived fromequation (6)∂Rs(t) ∂Ri(t) = m1∑ l1=0 · · · mK∑ lK=0 [ li − miRi(t) Ri(t)[1 − Ri(t)] 

The results of Fig. 6 show that the failure probability can gain the minimal value 0.4749 and the maximum value 0.5989 at cut level α = 0, and gain the median value 0.5369 at cut level α = 1 that is the most likely failure probability value of state S5. 

The optimal solution is obtained as [2,2,2] with thehighest reliability (0.8940) and the acceptable cost (38), which means that the hydraulic pump, the one-way valve, and the overflow valve need to be allocated a redundant component to keep them functioning reliably and safely.