Zhonglai Wang

An Approach to Reliability Assessment Under Degradation and Shock Process

Product performance usually degrades with time. When shocks exist, the degradation could be more rapid. This research investigates the reliability analysis when typical degradation and shocks are involved. Three failure modes are considered: catastrophic (binary state) failure, degradation (continuous processes), and failure due to shocks (impulse processes). The overall reliability equation with three failure modes is derived. The effects of shocks on performance are classified into two types: a sudden increase in the failure rate after a shock, and a direct random change in the degradation after the occurrence of a shock. Two shock scenarios are considered. In the first scenario, shocks occur with a fixed time period; while in the second scenario, shocks occur with varying time periods. An engineering example is given to demonstrate the proposed methods.

Optimal Design Accounting for Reliability, Maintenance, and Warranty

Reliability-based design (RBD) ensures high reliability with a reduced cost. Most of the RBD methodologies do not account for maintenance and warranty actions. As a result, the RBD result may not be truly optimal in terms of lifecycle reliability. This work attempts to integrate reliability, maintenance, and warranty during RBD. Three RBD models are built. The total cost of production, maintenance, and warranty are minimized. The computational procedures for solving the RBD models are developed. As demonstrated by two examples, the proposed RBD models meet not only the initial reliability requirement but also the maintenance and warranty requirements with reduced costs.

A Joint Redundancy and Imperfect Maintenance Strategy Optimization for Multi-State Systems

The redundancy allocation problem has been extensively studied with the aim of determining optimal redundancy levels of components at various stages to achieve the required system reliability or availability. In most existing studies, failed elements are assumed to be as good as new after repair, from a failure perspective. Due to deterioration, the repaired element cannot always be restored to a virtually new condition unless replaced with a new element. In this paper, we present an approach of joint redundancy and imperfect maintenance strategy optimization for multi-state systems. Along with determining the optimal redundancy levels, the element replacement strategy under imperfect repair is also optimized simultaneously, so as to reach the desired availability with minimal average expenditure. A generalized imperfect repair model is proposed to characterize the stochastic behavior of multi-state elements (MSEs) after repair, and a replacement policy under which a MSE is replaced once it reaches the pre-determined number of failures is introduced. The cost-repair efficiency relation, which regards the imperfect repair efficiency as a function of assigned repair cost, is put forth to provide a flexibility of assigning repair efforts strategically among MSEs. The benefits of the proposed method compared to the existing ones are demonstrated and verified via an illustrative case study of a three-stage coal transportation system.

Reliability analysis on competitive failure processes under fuzzy degradation data

Reliability is the ability of a system to perform its required functions under stated conditions for a specified period of time. Reliability analysis is an important tool to evaluate the performance of a system and make maintenance decision. In engineering practices, there are usually several processes to cause a system to failure. Generally, the failure processes can be categorized into degradation process and shock process. This research investigates the system reliability analysis when both degradation process and shock process are involved. Furthermore, the effect due to shock process on the degradation process is considered and degradation analysis is conducted under fuzzy degradation data. After that, a system reliability model on competitive failure processes under fuzzy degradation data is constructed. Since several states are formed when the effect due to shock process on degradation process is accounted for, multi-state system reliability theory is employed to evaluate the proposed model. This method could be used to assess reliability of many devices precisely in the real world, such as diesel engines. A practical engineering example is provided to illustrate the proposed model and method.

Reliability-Based Multidisciplinary Design Optimization Using Subset Simulation Analysis and Its Application in the Hydraulic Transmission Mechanism Design

The Monte Carlo simulation (MCS) can provide high reliability evaluation accuracy. However, the efficiency of the crude MCS is quite low, in large part because it is computationally expensive to evaluate a very small failure probability. In this paper, a subset simulation-based reliability analysis (SSRA) approach is combined with multidisciplinary design optimization (MDO) to improve the computational efficiency in reliability-based MDO (RBMDO) problems. Furthermore, the sequential optimization and reliability assessment (SORA) approach is utilized to decouple an RBMDO problem into a sequential of deterministic MDO and reliability evaluation problems. The formula of MDO with SSRA within the framework of SORA is proposed to solve a design optimization problem of a hydraulic transmission mechanism. [DOI: 10.1115/1.4029756]

A Unified Framework for Integrated Optimization Under Uncertainty

Reliability and robustness are two main attributes of design under uncertainty. Hence, it is necessary to combine reliability-based design and robust design at the design stage. In this paper, a unified framework for integrating reliability-based design and robust design is proposed. In the proposed framework, the probabilistic objective function is converted to a deterministic objective function by the Taylor series expansion or inverse reliability strategy with accounting for the probabilistic characteristic of the objective function. Therefore, with this unified framework, there is no need to deal with a multiobjective optimization problem to integrate reliability-based design and robust design any more. The probabilistic constraints are converted to deterministic constraints with inverse reliability strategy at the same time. In order to solve the unified framework, an improved sequential optimization and reliability assessment method is proposed. Three examples are given to illustrate the benefits of the proposed methods.

Time-Dependent Reliability of Dynamic Systems Using Subset Simulation With Splitting Over a Series of Correlated Time Intervals

Abstract : Time-dependent reliability is the probability that a system will perform its intended function successfully for a specified time. Unless many and often unrealistic assumptions are made, the accuracy and efficiency of time-dependent reliability estimation are major issues which may limit its practicality. Monte Carlo simulation (MCS) is accurate and easy to use but it is computationally prohibitive for high dimensional, long duration, time-dependent (dynamic) systems with a low failure probability. This work addresses systems with random parameters excited by stochastic processes. Their response is calculated by time integrating a set of differential equations at discrete times. The limit state functions are therefore, explicit in time and depend on time-invariant random variables and time-dependent stochastic processes. We present an improved subset simulation with splitting approach by partitioning the original high dimensional random process into a series of correlated, short duration, low dimensional random processes. Subset simulation reduces the computational cost by introducing appropriate intermediate failure sub-domains to express the low failure probability as a product of larger conditional failure probabilities. Splitting is an efficient sampling method to estimate the conditional probabilities. The proposed subset simulation with splitting not only estimates the timedependent probability of failure at a given time but also estimates the cumulative distribution function up to that time with approximately the same cost. A vibration example involving a vehicle on a stochastic road demonstrates the advantages of the proposed approach.

Hybrid Fuzzy Skyhook Surface Control Using Multi-Objective Microgenetic Algorithm for Semi-Active Vehicle Suspension System Ride Comfort Stability Analysis

A polynomial function supervising fuzzy sliding mode control (PSFaSMC), which embedded with skyhook surface method, is proposed for the ride comfort of a vehicle semiactive suspension. The multi-objective microgenetic algorithm (MOlGA) has been utilized to determine the PSFaSMC controller’s parameter alignment in a training process with three ride comfort objectives for the vehicle semi-active suspension, which is called the “offline” step. Then, the optimized parameters are applied to the real-time control process by the polynomial function supervising controller, which is named “online” step. A two-degree-of-freedom dynamic model of the vehicle semi-active suspension systems with the stability analysis is given for passenger’s ride comfort enhancement studies, and a simulation with the given initial conditions has been devised in MATLAB. The numerical results have shown that this hybrid control method is able to provide real-time enhanced level of reliable ride comfort performance for the semi-active suspension system. [DOI: 10.1115/1.4006220]

Interaction prediction optimization in multidisciplinary design optimization problems.

The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.

Reliability modeling for dependent competitive failure processes

In practical engineering applications, many factors of systems themselves and of random environments cause systems to suffer from degradation and shocks. Degradations, such as wear and erosion, occur in many systems, especially mechanical systems. Shocking is also a significant cause of system failure and hence has been paid more attention to. Shocks caused by the factors of the systems themselves usually have regular periods; especially for rotating devices, shocks have approximately fixed periods. Shocks caused by the factors of random environment usually follow Poisson process. In this paper, a new system reliability model is proposed for systems that involve dependent and competitive degradations and shocks. This model will have wide application in many fields. A numerical example is given to illustrate the model.