Reliability Of Engineering Systems Research Articles

Models of damage accumulation during repeated mechanical actions of unregulated factors

An approach for estimating the reliability of complex engineering systems under conditions of repeated mechanical actions of unregulated factors is proposed. Mathematical models are developed for determining the probability of maintaining functionality which make it possible to take into account the recurrence of unregulated mechanical effects.

Use of COTS Functional Analysis Software as an IVHM Design Tool for Detection and Isolation of UAV Fuel System Faults

This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated Vehicle Health Management (IVHM) design – the placement of sensors and the diagnostic rules to be used in interrogating their output. The qualitative functional analysis was chosen as a route for early assessment of failures in complex systems. Functional models of system components are required for capturing the available system knowledge used during various stages of system and IVHM design. MADeTM (Maintenance Aware Design environment), a COTS software tool developed by PHM Technology, was used for the health management design. A model has been built incorporating the failure diagrams of five failure modes for five different components of a UAV fuel system. Thus an inherent health management solution for the system and the optimised sensor set solution have been defined. The automatically generated sensor set solution also contains a diagnostic rule set, which was validated on the fuel rig for different operation modes taking into account the predicted fault detection/isolation and ambiguity group coefficients. It was concluded that when using functional modelling, the IVHM design and the actual system design cannot be done in isolation. The functional approach requires permanent input from the system designer and reliability engineers in order to construct a functional model that will qualitatively represent the real system. In other words, the physical insight should not be isolated from the failure phenomena and the diagnostic analysis tools should be able to adequately capture the experience bases. This approach has been verified on a laboratory bench top test rig which can simulate a range of possible fuel system faults. The rig is fully instrumented in order to allow benchmarking of various sensing solution for fault detection/isolation that were identified using functional analysis.

Integrated framework to optimize RAM and cost decisions in a process plant

The research implications of reliability, availability and maintainability (RAM) aspects of engineering systems in recent years have increased substantially due to rising operation and maintenance costs. To strike a balance between the two the paper presents a framework which makes use of both qualitative and quantitative techniques to optimize RAM and cost decisions in a process plant. In the quantitative analysis, the imprecise and vague information regarding the system failure behavior is quantified by using the principles of fuzzy mathematics in terms of fuzzy and crisp values. Further, to manage the system reliability for best economic performance a resource optimization model based on multi-stage decision making (MSDM) has been proposed. The model makes use of crisp output values of unit's reliability along with relevant system information (number of components, manpower, cost ranges). In the qualitative analysis the in-depth analysis of the system is carried out using Root Cause analysis (RCA) and Failure Mode & Effects Analysis (FMEA). The ambiguities associated with the traditional FMEA are handled using Fuzzy Decision Making System (FDMS) and Grey Relation Analysis (GRA). The suggested framework has been illustrated with the help of a case.

Improving the engineering systems reliability by formalizing the failure search

Improving the engineering systems reliability by formalizing the failure search

Function-Oriented Risk Model for Engineering System

Risk analysis is important to increase the reliability of engineering system. And an excellent model is the foundation. Traditional models are event-oriented so that it can’t describe the totality of the system. This paper introduces a function-oriented modeling approach-Goal Tree Success Tree and Master Logic Diagram. It provides a powerful functional/structural description and modeling method. It can represent the goals, the functions, the components and their interactions of the engineering system. So, it is a systemic modeling method for the risk analysis of engineering system. A case study of the servo mechanism of the launch vehicle shows that this approach can identify the key risk factors and the hidden risk factors. And it can trace the failure influence. It provides a systemic approach to risk analysis for researchers

Efficient Bayesian Network Learning for System Optimization in Reliability Engineering

We present a new Bayesian network modeling that learns the behavior of an unknown system from real data and can be used for reliability engineering and optimization processes in industrial systems. The suggested approach relies on quantitative criteria for addressing the trade-off between the complexity of a learned model and its prediction accuracy. These criteria are based on measures from Information Theory as they predetermine both the accuracy as well as the complexity of the model. We illustrate the proposed method by a classical example of system reliability engineering. Using computer experiments, we show how in a targeted Bayesian network learning, a tremendous reduction in the model complexity can be accomplished, while maintaining most of the essential information for optimizing the system.

Finite-time maintenance cost analysis of engineering systems affected by stochastic degradation

The performance and reliability of engineering systems and structures are usually affected by uncertain degradation that occurs in service as a result of various physical and environmental processes, such as corrosion, erosion, fatigue, and creep. To maintain reliability of degrading systems, periodic inspection and preventive maintenance programmes are adopted. In the literature, the optimization of a maintenance programme is typically based on the minimization of the asymptotic cost rate. However, many engineering systems operate in a relatively short and finite time horizon in which the application of the asymptotic approximation becomes questionable. This paper presents an accurate formulation for computing the expected value and variance of the cost of a condition-based maintenance programme over a defined time horizon. A stochastic gamma process is used to model uncertain degradation. This paper emphasizes that the consideration of variance of the cost is of utmost importance in maintenance optimization, because it helps to identify a more robust (less uncertain) solution in a set of competing optimum solutions based on expected cost.

Sergio E. Serrano: Engineering uncertainty and risk analysis: balanced approach to probability, statistics, stochastic modeling, and stochastic differential equations

Uncertainty and risk analyses are becoming more and more important in engineering sciences due to increasing climatic variation and other types of natural hazards. On average, every year infrastructure damages caused by earthquakes, floods, typhoons and other catastrophic events induce costs corresponding to hundreds of billions of dollars besides thousands of lives lost. Consequently, uncertainty and risk analyses are some of the most important subjects for engineers. In line with this, the author, Prof. Sergio E. Serrano, Temple University, has issued a second, completely revised text book on the subject. The book is a comprehensive collection of stochastic and statistical engineering techniques with uncertainty and risk analyses that are applicable in a broad range of problems. The focus is clearly on engineering applications rather than on theoretical derivation. Even rather advanced techniques involving nonlinear differential equations are solved analytically in supplied codes and presented with illustrative examples. The strong and very useful aspect of this text book is that it contains a multitude of solved examples as well as problems with answers. Consequently, the potential reader of this book might be engineering students as well as professional engineers. The book consists of fourteen chapters starting with a discussion of engineering uncertainty analysis and ending with a summary and discussion on seven steps for analysis of engineering problems. Chapter 2 introduces concepts of probability and frequency. Chapter 3 clarifies the concept of random variables with discrete and continuous variables. Chapter 4 then introduces the basics of random system modeling and Monte Carlo simulation. Chapter 5 presents the concept of several random variables and Chapter 6 introduces basic ideas of statistics. Chapter 7 shows how to fit probabilistic models to experimental data and then follows linear regression in Chapter 8. Chapter 9 explains how to develop models for reliability of engineering systems and Chapter 10 shows how to design engineering experiments. In Chapter 11 methods for testing experimental data are introduced together with ANOVA techniques. Chapter 12 is a synthesis of previous chapter in the way that it explains and visualizes random processes using explained Maple codes. Finally, Chapter 13 introduces novel techniques to solve linear and nonlinear differential equations by means of analytical methods. The above shows that it is a very comprehensive text book on uncertainty and risk. This together with solved examples and included Maple codes makes it one of the most useful books on the market for teaching engineering students as well solving practical engineering problems involving risk and uncertainty estimation. I am sure that the book will have a significant positive impact on improving engineering solving during coming decades.

A Memetic Algorithm for Multi-Level Redundancy Allocation

Redundancy allocation problems (RAPs) have attracted much attention for the past thirty years due to its wide applications in improving the reliability of various engineering systems. Because RAP is an NP-hard problem, and exact methods are only applicable to small instances, various heuristic and meta-heuristic methods have been proposed to solve it. In the literature, most studies on RAPs have been conducted for single-level systems. However, real-world engineering systems usually contain multiple levels. In this paper, the RAP on multi-level systems is investigated. A novel memetic algorithm (MA) is proposed to solve this problem. Two genetic operators, namely breadth-first crossover and breadth-first mutation, and a local search method are designed for the MA. Comprehensive experimental studies have shown that the proposed MA outperformed the state-of-the-art approach significantly on two representative examples.

Bayesian Network Enhanced with Structural Reliability Methods: Application

The enhanced Bayesian network (eBN) methodology described in the companion paper facilitates the assessment of reliability and risk of engineering systems when information about the system evolves in time. We present the application of the eBN (a) to the assessment of the life-cycle reliability of a structural system, (b) to the optimization of a decision on performing measurements in that structural system, and (c) to the risk assessment of an infrastructure system subject to natural hazards and deterioration of constituent structures. In all applications, observations of system performances or the hazards are made at various points in time and the eBN efficiently includes these observations in the analysis to provide an updated probabilistic model of the system at all times.

Engineering Machinery Engine System Reliability Analysis

It exists in complexity and fuzziness in structure and failure character of engineering machinery complex system. To solve the problem, fuzzy theory was introduced into fault tree analysis, and then the steps, principles and methods of fuzzy fault tree are determined. Based on the analysis of engineering machinery typical engine systemic structure and fault criterion, a fuzzy fault tree is established. Used a fuzzy mathematics method of reliability analysis with fuzzy fault rate and the median method of fuzzy significance analysis, the fuzzy fault rate and fuzzy significance of parts and subsystems is calculated. The result indicates that this method can solve the fuzziness problem of failure criterion and data in fault tree analysis. The result provides valuable reference for the reliability analysis.

Reliability-Based Design Using Two-Stage Stochastic Optimization with a Treatment of Model Prediction Errors

Design problems that involve optimization of the reliability of engineering systems are the focus of this paper. Methodologies are discussed applicable to problems that involve nonlinear systems and a large number of uncertain parameters specifying the system and excitation models. To address the complexity of these problems, stochastic simulation is considered for evaluation of the system reliability. An innovative approach, called stochastic subset optimization (SSO), is discussed for performing a sensitivity analysis with respect to the design variables of the problem as well as the uncertain model parameters. In a small number of iterations, SSO converges to a smaller subset of the original design space that has high plausibility of containing the optimal design variables and that consists of near-optimal designs. For higher accuracy, an appropriate stochastic optimization algorithm may then be used to pinpoint the optimal design variables within this subset. This produces an efficient two-stage framework for optimal reliability design. Topics related to the combination of the two different stages for overall enhanced efficiency are discussed. An example is presented that illustrates the effectiveness of the proposed two-stage methodology for a challenging dynamic reliability problem. Also, a study is performed of the influence on the optimal design decisions of the prediction error of the system model, which is introduced because no model makes perfect predictions of the system response.

Formal Reliability Analysis Using Theorem Proving

Reliability analysis has become a tool of fundamental importance to virtually all electrical and computer engineers because of the extensive usage of hardware systems in safety and mission critical domains, such as medicine, military, and transportation. Due to the strong relationship between reliability theory and probabilistic notions, computer simulation techniques have been traditionally used to perform reliability analysis. However, simulation provides less accurate results and cannot handle large-scale systems due to its enormous CPU time requirements. To ensure accurate and complete reliability analysis and thus more reliable hardware designs, we propose to conduct a formal reliability analysis of systems within the sound core of a higher order logic theorem prover (HOL). In this paper, we present the higher order logic formalization of some fundamental reliability theory concepts, which can be built upon to precisely analyze the reliability of various engineering systems. The proposed approach and formalization is then utilized to analyze the repairability conditions for a reconfigurable memory array in the presence of stuck-at and coupling faults.

Guest Editors' Introduction: Special Section on System-Level Design of Reliable Architectures

Guest Editors' Introduction: Special Section on System-Level Design of Reliable Architectures

Transformation from Prognostics to Engineering Immune Systems

Reliability of modern engineering systems is becoming exceedingly important for manufacturers to keep a competitive edge in today's global markets. Prognostics and health management (PHM) is an emerging engineering discipline that can help in increasing a system's reliability via predicting faults which allows for preventing unexpected failures and prolonged downtime. In this paper the authors are taking PHM to a new level. Inspired by the biological immune and nervous systems, the authors are introducing the transformation of PHM to engineering immune systems (EIS). EIS are defined in this paper as highly reliable systems that are: capable of surviving disruptions by avoiding it without serious failures and have the ability to resist disturbances while maintaining their stability. In this paper, a background about similar biologically inspired systems is given to compare them with the authors’ concept about EIS, which is focused on mechanical systems and how PHM can evolve from being predictive to being protective.

An exact method for solving logical loops in reliability analysis

This paper presents an exact method for solving logical loops in reliability analysis. The systems that include logical loops are usually described by simultaneous Boolean equations. First, present a basic rule of solving simultaneous Boolean equations. Next, show the analysis procedures for three-component system with external supports. Third, more detailed discussions are given for the establishment of logical loop relation. Finally, take up two typical structures which include more than one logical loop. Their analysis results and corresponding GO-FLOW charts are given. The proposed analytical method is applicable to loop structures that can be described by simultaneous Boolean equations, and it is very useful in evaluating the reliability of complex engineering systems.

Reliability of engineering systems with combined performance control

The paper determines an optimal performance-check rule that maximizes the availability factor of a system subject to two types of failures. Long-use systems are considered such that timely intervention of maintenance staff is possible to make the system serviceable.

Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment

Traditional fault tree (FT) analysis is widely used for reliability and safety assessment of complex and critical engineering systems. The behavior of components of complex systems and their interactions such as sequence- and functional-dependent failures, spares and dynamic redundancy management, and priority of failure events cannot be adequately captured by traditional FTs. Dynamic fault tree (DFT) extend traditional FT by defining additional gates called dynamic gates to model these complex interactions. Markov models are used in solving dynamic gates. However, state space becomes too large for calculation with Markov models when the number of gate inputs increases. In addition, Markov model is applicable for only exponential failure and repair distributions. Modeling test and maintenance information on spare components is also very difficult. To address these difficulties, Monte Carlo simulation-based approach is used in this work to solve dynamic gates. The approach is first applied to a problem available in the literature which is having non-repairable components. The obtained results are in good agreement with those in literature. The approach is later applied to a simplified scheme of electrical power supply system of nuclear power plant (NPP), which is a complex repairable system having tested and maintained spares. The results obtained using this approach are in good agreement with those obtained using analytical approach. In addition to point estimates of reliability measures, failure time, and repair time distributions are also obtained from simulation. Finally a case study on reactor regulation system (RRS) of NPP is carried out to demonstrate the application of simulation-based DFT approach to large-scale problems.

A nonlinear mixed-effects model for degradation data obtained from in-service inspections

Monitoring of degradation and predicting its progression using periodic inspection data are important to ensure safety and reliability of engineering systems. Traditional regression models are inadequate in modeling the periodic inspection data, as it ignores units specific random effects and potential correlation among repeated measurements. This paper presents an advanced nonlinear mixed-effects (NLME) model, generally adopted in bio-statistical literature, for modeling and predicting degradation in nuclear piping system. The proposed model offers considerable improvement by reducing the variance associated with degradation of a specific unit, which leads to more realistic estimates of risk.

Laminate optimization incorporating analysis and model parameter uncertainties for predictable failure

Reliability of engineering systems are affected by uncertainties from four sources: inherent variability in the physical variables, modeling errors, estimation errors and human errors. Here the effect of modeling uncertainties on the predictability and robustness of progressive failure in optimized composites is studied. This paper investigates the effects of different modeling deficiencies such as uncertainties in the homogenization models used for stiffness calculations, in micromechanics models used for calculating the ply or constituent level stresses, in the failure criteria used for predicting failure, and in the models used to degrade ply properties after initial failure. Different optimized composite laminate designs are analyzed and compared to illustrate the interaction among model parameter uncertainties and design variability. A composite laminate is optimized in the presence of model uncertainties. It is shown that achieving a predictable failure sequence requires including model uncertainties along with variability in the reliability optimization.