A hybrid load flow and event driven simulation approach to multi-state system reliability evaluation

High reliability and security have become the hallmarks of complex electromechanical systems. Owing to the difficulties in fault data collection, ambiguity and uncertainty have been inevitably associated with complex electromechanical systems. Thus, the ability to perform reliability evaluation using scarce fault data is of immense significance to these machines and is the focus of this study. A similarity based cloud model is proposed to evaluate the running state of complex electromechanical systems. By combining objective and subjective factors, the reliability of complex electromechanical systems is evaluated by calculating the similarity between the cloud models of actual and standardised states. Next, the inverter of an offshore wind turbine is used to verify the accuracy and effectiveness of the proposed approach. The cloud model based framework for reliability evaluation inherits the preponderance of the uncertainty problem, overcomes the drawbacks of the current reliability approaches, and provides a theoretical basis, as well as a practical approach for the maintenance and repair of complex electromechanical systems with missing fault data. Additionally, it also provides a new methodology for solving the uncertainty problems caused by paucity of data.

Section: Cloud Model Transformation Of Uncertain Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comprehensive reliability evaluation of multistate complex electromechanical systems based on similarity of cloud models

Wang

Gao

et al. 2020

“…Yet its direction is known and fixed. 45,46 The first step to capture the components and their connections of such systems is to model them as networks, whose nodes represent the components and whose links stand for the interactions between them. Ultimately, the holistic topology of the complex EMS can be conveniently and accurately represented by a directed network with components and connections among components with edges.…”

Section: Complex Ems As a Networkmentioning

confidence: 99%

Reliability assessment of complex electromechanical systems: A network perspective

Lin

Wang

Jia

et al. 2018

The network theory is widely applied to improve the reliability of a complex electromechanical system. In this application, system reliability assessment with network theory has been paid a great deal of attention. Because of instrument malfunctions, staff omissions, imperfect inspection strategies, and complex structures, field failure data are often subject to interval censoring, making the holistic reliability assessment becomes a difficult task. Most traditional methods assume reliability of critical components or partial reliability as system reliability, which may cause a large bias in system reliability estimation. This paper proposes a novel method to evaluate and predict the system reliability of a complex electromechanical system subject to the insufficient fault data problem from a network perspective. First, the system modeling based on network theory is developed to describe the topology of a holistic system. Second, interval-valued intuitionistic hesitant fuzzy number is proposed in order to solve insufficient data for single component. Then, a new measurecomprehensive reliability-that can reflect the reliability of nodes in combination with functional properties and topological properties, which are formulated by failure data and network model, respectively, is constructed for system reliability assessment. Subsequently, an improved system reliability model based on percolation theory is given in terms of comprehensive reliability of nodes. Finally, to verify the effectiveness of the proposed method, a simulation and a real case study for traction system are implemented.

“…The reliability of a MSS can be defined as its ability to meet a required system performance level . There exist a variety of methods to evaluate the reliability of MSSs, such as stochastic processes,() Monte Carlo simulations,() multi‐valued decision diagram (MDD),() and universal generating function (UGF). () Stochastic processes are suitable for components whose performance follows the continuous stochastic process.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of multi‐state series systems with performance sharing mechanism under epistemic uncertainty

Qiu

Ming

2019

For real engineering systems, it is sometimes difficult to obtain sufficient data to estimate the precise values of some parameters in reliability analysis. This kind of uncertainty is called epistemic uncertainty. Because of the epistemic uncertainty, traditional universal generating function (UGF) technique is not appropriate to analyze the reliability of systems with performance sharing mechanism under epistemic uncertainty. This paper proposes a belief UGF (BUGF)-based method to evaluate the reliability of multi-state series systems with performance sharing mechanism under epistemic uncertainty. The proposed BUGF-based reliability analysis method is validated by an illustrative example and compared with the interval UGF (IUGF)-based methods with interval arithmetic or affine arithmetic. The illustrative example shows that the proposed BUGF-based method is more efficient than the IUGF-based methods in the reliability analysis of multi-state systems (MSSs) with performance sharing mechanism under epistemic uncertainty.