Multi-State System Reliability Evaluation and Component Allocation Optimization Under Multi-Level Performance Sharing

Abstract

With the development of science and technology, the structure of engineering system has become increasingly large and complex. In order to ensure the safety and stability of the system in operation, the reliability evaluation of complex system has become an important research field. Based on the actual engineering system, this paper proposes a multi-state system with multi-level performance sharing mechanism. On this basis, we established a system reliability evaluation model using universal generating function technique. Through numerical examples, the application of the model and analyze the influence of different parameters on system reliability are demonstrated. In addition, we also use genetic algorithm to optimize the allocation of components in the system, so as to improve the reliability of the system. Different from the previous studies on system reliability with common bus performance sharing, the system proposed in this study is more general.

A Comprehensive Review of Key Technologies for Enhancing the Reliability of Lithium-Ion Power Batteries

“…Huang et al [68] proposed an evaluation index to effectively evaluate the health condition of the battery of an energy-storage device and verified the validity of the evaluation index. Cheng et al [69] provided an algorithm for the reliability assessment of a battery storage system considering the life degradation of lithium-ion batteries, which is a reliability assessment algorithm based on the generalized generating function [70][71][72][73][74][75]. Liu et al [76] study the reliability problem of battery storage systems composed of battery modules and introduced a reliability assessment method for battery modules based on a generalized generating function, as: Define the UGF Expression for the Battery Cell…”

Availability Evaluation and Design Optimization of Multi-State k-out-of-n: G Systems With Random Performance Requirements

“…In (1), the first part of the conditional probability form represents the system state probability that satisfies the weight requirement, and the second part represents the conditional probability of satisfying the quantity requirement under the condition of satisfying the weight requirement. Because 𝑋 and G are not independent and cannot be mapped to each other, it is difficult to calculate the value of the second part based on the one-to-one correspondence between the component state probability and the state performance rate.…”

“…In traditional binary reliability/availability studies, a system or component is typically assumed to have two states: completely working or totally failed. However, complex systems in the real-world, such as power systems, communication systems, and production systems [1], [2], [3] etc., often present multiple states during operation, and different states have different performance rates, which are called multi-state system (MSS). As a more flexible and accurate tool for complex system analysis, the MSS model has been widely studied, because it can characterize the multi-state deteriorating nature of complex systems [4].…”

Systematic Reliability Optimization (ASRO)

“…Techniques applied for reliability optimization include redundancy analysis [5], physics-based models [6], accelerated testing [7], and data-driven methods leveraging operational data [8] [9]. While these approaches have merit, taking advantage of modern smart systems and advanced sensing for optimization remains an open opportunity [10]. Our systematic review examines the latest techniques, particularly AI-based methods, for optimizing design and management of complex cyber-physical systems where failures can have severe economic and safety impacts [11] [12].…”