Reliability analysis for complex electromechanical multi-state systems utilizing universal generating function techniques

Large-scale adoption of gas-fired power plants (GPPs) significantly accelerates the integration of power systems and natural gas systems (NGSs). Because the operation of NGS has a significant impact on the reliability of power system, it is important to simultaneously evaluate the reliabilities of power system and NGS. Moreover, due to the stochastic failures and various operating characteristics of components, the integrated gas and power systems (IGPS) considering multiple states can be viewed as a multi-state system. Therefore, a multi-state model is proposed for reliability evaluation of IGPS based on universal generating function (UGF) techniques. First, a framework is proposed to evaluate the reliability of NGS: multi-state models for different components in the NGS are developed as the corresponding UGFs. These UGFs are aggregated utilizing the gas flow calculation operator to obtain the multi-state model of NGS. Moreover, a gas-to-power calculation operator is developed to convert the multi-state model of gas injection at each node to the power output models of GPPs in power system. In this manner, the impacts of NGS could be incorporated into the reliability evaluation of power system. Furthermore, nodal reliability indices for both the NGS and the power system are proposed to evaluate the reliability performances of IGPS. The proposed methods are validated using the integrated gas and power test system.

Universal generating function (UGF) technology is the basic approach to reliability evaluation of multi-state systems. For products with high reliability and long lifetime in practical engineering, their system structure is usually uncertain and the external working condition is extremely complex. Thus, accurate failure or degradation data of components are difficult to define. Data are often characterized by types of randomness, probability, grey, fuzzy and other uncertain numbers. Based on interval grey number, the concept of generalized uncertain number is proposed. In this paper, we first unify arbitrary type of uncertain numbers, propose the definition of generalized standard uncertain number and study algorithm such as addition, subtraction, multiplication and division between them. Based on generalized standard uncertain number, generalized universal generating function, which is an extension of fuzzy UGF, is proposed. Several commonly used generalized universal generating operators are discussed with system connection structure between components. According to the relationship between output performance and system demand level, reliability indexes such as availability, expected output performance and mean performance deficiency are evaluated. Comparisons in a real case show the effectiveness of the proposed approach. Therefore, relevant results can strengthen the theoretical research depth of uncertain reliability and definitely expand the application scope of fuzzy universal generating function.

As the core foundation of reliability engineering and one of the most crucial aspects of general quality, reliability has been attracting increasing attention from industry and academia. Numerous publications on reliability analysis (RA) have appeared in academic journals, conference proceedings, and technical reports. However, there has not been a systematic summary that covers RA data, models, methods, indicators, and software comprehensively. To fill this gap, this paper analyzed the challenges and the state of the art, as well as determined the future prospects for research on RA. This paper introduces the features of modern complex electromechanical systems (CES), then summarizes the current challenges to the RA of CES. Next, it reviews the latest developments, such as reliability theories, models, methods, and software, then summarizes and compares their advantages and limitations. Finally, it discusses future prospects from the perspectives of reliability data, modeling, decoupling, evaluation indicators, and timely prediction. It is expected that this paper would serve as an introduction to RA for researchers new to this field and as a summary of the current frontiers of RA for experienced researchers.

Laboratory aids for development of electromechanical systems are described in this paper. Standardized components for ``mocking up'' complex electromechanical systems have proved to be time-saving aids in laboratory work at the Sperry Gyroscope Company, Inc. Illustrations are shown of laboratory setups using these components and descriptions are given of the more generally used mechanical and electronic components.

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.

At present, complex electromechanical systems are widely used in modern society. By evaluating complex electromechanical systems, it is of great value to improve and upgrade them. Given the complex and interrelated characteristics of performance indicators, this paper establishes the performance evaluation indicator system of a complex electromechanical system, proposes to calculate the weight of each indicator by an improved analytic hierarchy process, revises the indicator weight by entropy weight method, and then adopts fuzzy synthesis evaluation method to evaluate the comprehensive effectiveness of a complex electromechanical system quantitatively. Through the example analysis, it is proved that this method provides an effective way for the effectiveness evaluation of complex electromechanical systems.

Identification methods for the dynamic systems are considered with respect to electric drives. It is shown that electric drives are complex electromechanical systems, the mechanical part of which is multimass and contains elements with elastic connections. The features of these systems that require considering the gaps in gear units when solving modeling and identification problems; elasticity arising in the shafts; stiffness change of elastic elements in couplings. It is noted that the identification problem of electric drives is a subproblem of managing the state of electromechanical systems. The basis of methods and algorithms for its solution is a set is information about the performance limits of the electric drive. It is shown that identification of the controlled parameters of the electric drive requires using statistical methods based on the expansion of the pulse transient functions to a series of the orthonormal Laguerre transformed system of functions. The method has been proposed in order to assess the condition of the electric drive during operation. This method involves setting the performance limits as a set of boundary points. A distinctive feature of the method is the high identification accuracy.

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 measure—comprehensive 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.

On the basis of a brief introduction about the recent progress of reliability assessment of complex systems, combined methods and state methods are introduced. In view of the shortcomings and deficiencies of these two kinds of methods, a reliability assessment method is put forward which based on fault tree analysis (FTA) and Bayesian network (BN). By applying the results of FTA of a complex system and adopting the transforming method of FTA to BN, BN model for reliability assessment of the complex system is established. A missile engine is taken for example and the effectiveness and the priority of the model in reliability assessment of system is verified.

An empirical model intended for describing the aerodynamic torque acting upon Savonius rotor is proposed. This model allows for parametric analysis of complex mechanical and electromechanical systems containing Savonius rotor using methods of the general mechanics and the theory of dynamical systems. The model is verified using wind tunnel experiments. As an example, a wheeled cart driven by Savonius rotor is considered. Numerical simulation of dynamics of such cart is performed.

Reliability assessment of multi-state phased mission systems with common bus performance sharing considering transmission loss and performance storage

A financial view has been proposed for reliability evaluation of multi-state weighted k-out-of-n:F systems. The cost which is imposed on the components by failures is employed as reliability index. It is used to define a weight for each component. It also provides an opportunity to analyze the reliability of functioning periods in addition to design time in evaluation process. Furthermore, time value of money can be considered in system reliability assessment. The Universal Generating Function (UGF) has been applied to estimate the multi-state weighted k-out-of-n:F system reliability based on components’ reliability measures. At last, the present value of failure cost is calculated using system reliability measures. An illustrative example is provided for the proposed system reliability evaluation.

Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems

System signature has been widely utilized to facilitate reliability assessment of coherent systems and networks consisting of a single type of components, which enables us to separate aspects of components’ failure time and system structure. Recently, survival signature fulfils the similar work, extended to the implementation of systems with multiple types of components. However, when the system has several states, the relevant work is limited to complete the reliability assessment by the survival signature. This paper proposed a novel pattern of survival signature for the multi-state system with multiple types of components and the presented approach is executed to demonstrate the validity via a numerical case with 2-type and 3-state components.

Cascading failures may lead to dramatic collapse in interdependent networks, where the breakdown takes place as a discontinuity of the order parameter. In the cascading failure (CF) model with healing there is a control parameter which at some value suppresses the discontinuity of the order parameter. However, up to this value of the healing parameter the breakdown is a hybrid transition, meaning that, besides this first order character, the transition shows scaling too. In this paper we investigate the question of universality related to the scaling behavior. Recently we showed that the hybrid phase transition in the original CF model has two sets of exponents describing respectively the order parameter and the cascade statistics, which are connected by a scaling law. In the CF model with healing we measure these exponents as a function of the healing parameter. We find two universality classes: In the wide range below the critical healing value the exponents agree with those of the original model, while above this value the model displays trivial scaling meaning that fluctuations follow the central limit theorem.