Reliability and Condition-Based Maintenance Analysis of Deteriorating Systems Subject to Generalized Mixed Shock Model

For successful commercialization of evolving devices (e.g., micro-electro-mechanical systems, and biomedical devices), there must be new research focusing on reliability models and analysis tools that can assist manufacturing and maintenance of these devices. These advanced systems may experience multiple failure processes that compete against each other. Two major failure processes are identified to be deteriorating or degradation processes (e.g., wear, fatigue, erosion, corrosion) and random shocks. When these failure processes are dependent, it is a challenging problem to predict reliability of complex systems. This research aims to develop reliability models by exploring new aspects of dependency between competing risks of degradation-based and shock-based failure considering a generalized mixed shock model, and to develop new and effective condition-based maintenance policies based on the developed reliability models. In this research, different aspects of dependency are explored to accurately estimate the reliability of complex systems. When the degradation rate is accelerated as a result of withstanding a particular shock pattern, we develop reliability models with a changing degradation rate for four different shock patterns. When the hard failure threshold reduces due to changes in degradation, we investigate reliability models considering the dependence of the hard failure threshold on the degradation level for two different scenarios. More generally, when the degradation rate and the hard failure threshold can simultaneously transition multiple times, we propose a rich reliability model for a new generalized mixed shock model that is a combination of extreme shock model, δ-shock model and run shock model. This general assumption reflects complex behaviors associated with modern systems and structures that experience multiple sources of external shocks. Based on the developed reliability models, we introduce new condition-based maintenance strategies by including various maintenance actions (e.g., corrective replacement, preventive replacement, and imperfect repair) to minimize the expected long-run average maintenance cost rate. The decisions for maintenance actions are made based on the health condition of systems that can be observed through periodic inspection. The reliability and maintenance models developed in this research can provide timely and effective tools for decision-makers in manufacturing to economically optimize operational decisions for improving reliability, quality and productivity.Industrial Engineering, Department o

Time-dependent reliability analysis for a herringbone planetary gear set with failure dependency under random loads

A gear-bearing coupling dynamic model that includes bearing stiffness, mesh stiffness and mesh errors for a herringbone planetary gear set (HPGS) is proposed. The proposed model is used to predict the random stress process of the gears and bearings along with Monte Carlo simulation, when the effects of tooth surface wear on meshing errors are considered. A calculation model for the random strength process is derived by applying the linear fatigue damage criterion and regarding the Poisson random process as a counting process of the random stress. Assuming that the stress and strength are random processes, a time-dependent reliability model for HPGS with failure dependency is proposed to predict the time-dependent reliability of HPGS based on the failure mode groups that are obtained by the correlation coefficient. The results show that, the meshing errors caused by the tooth surface wear, make the dynamic loads and failure dependency of the parts of HPGS gradually increase over service time. At the same time the fatigue damage, which is caused by the load action, makes the strength of parts of HPGS gradually decrease over service time. Therefore the reliability of HPGS quickly decreases over service time. If the shafts of the planets and sun gear are free in their axial direction, the failure dependency of the parts will significantly decrease, and the reliability of a herringbone planetary gear set will significantly be improved

Wind turbines and seismic hazard: a state-of-the-art review

Wind energy is a rapidly growing field of renewable energy, and as such, intensive scientific and societal interest has been already attracted. Research on wind turbine structures has been mostly focused on the structural analysis, design and/or assessment of wind turbines mainly against normal (environmental) exposures while, so far, only marginal attention has been spent on considering extreme natural hazards that threat the reliability of the lifetime-oriented wind turbine's performance. Especially, recent installations of numerous wind turbines in earthquake prone areas worldwide (e.g., China, USA, India, Southern Europe and East Asia) highlight the necessity for thorough consideration of the seismic implications on these energy harnessing systems. Along these lines, this state-of-the-art paper presents a comparative survey of the published research relevant to the seismic analysis, design and assessment of wind turbines. Based on numerical simulation, either deterministic or probabilistic approaches are reviewed, because they have been adopted to investigate the sensitivity of wind turbines' structural capacity and reliability in earthquake-induced loading. The relevance of seismic hazard for wind turbines is further enlightened by available experimental studies, being also comprehensively reported through this paper. The main contribution of the study presented herein is to identify the key factors for wind turbines' seismic performance, while important milestones for ongoing and future advancement are emphasized

Dynamic Reliability Analysis Method of Degraded Mechanical Components Based on Process Probability Density Function of Stress

It is necessary to develop dynamic reliability models when considering strength degradation of mechanical components. Instant probability density function (IPDF) of stress and process probability density function (PPDF) of stress, which are obtained via different statistical methods, are defined, respectively. In practical engineering, the probability density function (PDF) for the usage of mechanical components is mostly PPDF, such as the PDF acquired via the rain flow counting method. For the convenience of application, IPDF is always approximated by PPDF when using the existing dynamic reliability models. However, it may cause errors in the reliability calculation due to the approximation of IPDF by PPDF. Therefore, dynamic reliability models directly based on PPDF of stress are developed in this paper. Furthermore, the proposed models can be used for reliability assessment in the case of small amount of stress process samples by employing the fuzzy set theory. In addition, the mechanical components in solar array of satellites are chosen as representative examples to illustrate the proposed models. The results show that errors are caused because of the approximation of IPDF by PPDF and the proposed models are accurate in the reliability computation

Stochastic Modeling of Deterioration in Nuclear Power Plant Components

The risk-based life-cycle management of engineering systems in a nuclear power plant is intended to ensure safe and economically efficient operation of energy generation infrastructure over its entire service life. An important element of life-cycle management is to understand, model and forecast the effect of various degradation mechanisms affecting the performance of engineering systems, structures and components. The modeling of degradation in nuclear plant components is confounded by large sampling and temporal uncertainties. The reason is that nuclear systems are not readily accessible for inspections due to high level of radiation and large costs associated with remote data collection methods. The models of degradation used by industry are largely derived from ordinary linear regression methods. The main objective of this thesis is to develop more advanced techniques based on stochastic process theory to model deterioration in engineering components with the purpose of providing more scientific basis to life-cycle management of aging nuclear power plants. This thesis proposes a stochastic gamma process (GP) model for deterioration and develops a suite of statistical techniques for calibrating the model parameters. The gamma process is a versatile and mathematically tractable stochastic model for a wide variety of degradation phenomena, and another desirable property is its nonnegative, monotonically increasing sample paths. In the thesis, the GP model is extended by including additional covariates and also modeling for random effects. The optimization of age-based replacement and condition-based maintenance strategies is also presented. The thesis also investigates improved regression techniques for modeling deterioration. A linear mixed-effects (LME) regression model is presented to resolve an inconsistency of the traditional regression models. The proposed LME model assumes that the randomness in deterioration is decomposed into two parts: the unobserved heterogeneity of individual units and additive measurement errors. Another common way to model deterioration in civil engineering is to treat the rate of deterioration as a random variable. In the context of condition-based maintenance, the thesis shows that the random variable rate (RV) model is inadequate to incorporate temporal variability, because the deterioration along a specific sample path becomes deterministic. This distinction between the RV and GP models has profound implications to the optimization of maintenance strategies. The thesis presents detailed practical applications of the proposed models to feeder pipe systems and fuel channels in CANDU nuclear reactors. In summary, a careful consideration of the nature of uncertainties associated with deterioration is important for credible life-cycle management of engineering systems. If the deterioration process is affected by temporal uncertainty, it is important to model it as a stochastic process

Updated Review of the Life and Reliability Models for HVDC Cables

This paper updates previous reviews of life and reliability models for HVDC cables. The update is motivated by the impressive R&D activities on HVDC cable systems in the last years, with many projects at increasing levels of voltage and power; this makes the sound evaluation of life and reliability of HVDC cables crucial. Physical and phenomenological life models proposed over the years for constant electrical and thermal stresses are reviewed first, including the relevant probabilistic framework and the effects of cable insulation volume enlargement. Then, more recent procedures for life and reliability estimation under time-varying electro-thermal stress are reported, focusing on thermal transients due to load cycles and voltage transients due to long Temporary Over-Voltages, Superimposed Switching Impulses and Voltage Polarity Reversals. Results of the application of such procedures are also presented, with a discussion on their limitations, and on open issues

Reliability Evaluation Based on Different Distributions of Random Load

The reliability models of the components under the nonstationary random load are developed in this paper. Through the definition of the distribution of the random load, it can be seen that the conventional load-strength interference model is suitable for the calculation of the static reliability of the components, which does not reflect the dynamic change in the reliability and cannot be used to evaluate the dynamic reliability. Therefore, by developing an approach to converting the nonstationary random load into the random load whose pdf is the same at each moment when the random load applies, the reliability model based on the longitudinal distribution is derived. Moreover, through the definition of the transverse standard load and the transverse standard load coefficient, the reliability model based on the transverse distribution is derived. When the occurrence of the random load follows the Poisson process, the dynamic reliability models considering the strength degradation are derived. These models take the correlation between the random load and the strength into consideration. The result shows that the dispersion of the initial strength and that of the transverse standard load coefficient have great influences on the reliability and the hazard rate of the components

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

Assessment of structural reliability: a dynamic monitoring approach

The subject of this thesis is framed in the field of vibration based monitoring. In particular the work is focused on: implementing techniques of extraction of features, the use of collected data to recognize damages and the combined application of knowledge coming from monitoring systems with the classical structural safety formulations to a real case stud

MODELS FOR ASSESSMENT OF FLAWS IN PRESSURE TUBES OF CANDU REACTORS

Probabilistic assessment and life cycle management of engineering components and systems in a nuclear power plant is intended to ensure safe and efficient operation of energy generation over its entire life. The CANDU reactor core consists of 380-480 pressure tubes, which are like miniature pressure vessels that contain natural uranium fuel. Pressure tubes operate under severe temperature and radiation conditions, which result in degradation with ageing. Presence of flaws in a pressure tube makes it vulnerable to delayed hydride cracking (DHC), which may lead to rupture or break-before-leak situation. Therefore, assessment of flaws in the pressure tubes is considered an integral part of a reactor core assessment program. The main objective of the thesis is to develop advanced probabilistic and mechanical stress field models for the assessment of flaws. The flaw assessment models used by the industries are based on deterministic upper/lower bound values for the variables and they ignore uncertainties associated with system parameters. In this thesis, explicit limit state equations are formulated and first order reliability method is employed for reliability computation, which is more efficient than simulation-based methods. A semi-probabilistic approach is adopted to develop an assessment model, which consists of a mechanics-based condition (or equation) involving partial factors that are calibrated to a specified reliability level. This approach is applied to develop models for DHC initiation and leak-before-break assessments. A novel feature of the proposed method is that it bridges the gap between a simple deterministic analysis and complex simulations, and it is amenable to practical applications. The nuclear power plant systems are not easily accessible for inspection and data collection due to exposure to high radiation. For this reason, small samples of pressure tubes are inspected at periodic intervals and small sample of data so collected are used as input to probabilistic analysis. The pressure tube flaw assessment is therefore confounded by large sampling uncertainties. Therefore, determination of adequate sample size is an important issue. In this thesis, a risk informed approach is proposed to define sample size requirement for flaw assessment. Notch-tip stress field is a key factor in any flaw assessment model. Traditionally, linear elastic fracture mechanics (LEFM) and its extension, serves the basis for determination of notch-tip stress field for elastic and elastic-perfectly-plastic material, respectively. However, the LEFM solution is based on small deformation theory and fixed crack geometry, which leads to singular stress and strain field at the crack-tip. The thesis presents new models for notch and crack induced stress fields based on the deformed geometry. In contrast with the classical solution based on small deformation theory, the proposed model uses the Cauchy's stress definition and boundary conditions which are coupled with the deformed geometry. This formulation also incorporates the rotation near the crack-tip, which leads to blunting and displacement of the crack-tip. The solution obtained based on the final deformed configuration yields a non-singular stress field at the crack-tip and a non-linear variation of stress concentration factor for both elastic and elastic-perfectly-plastic material. The proposed stress field formulation approach is applied to formulate an analytical model for estimating the threshold stress intensity factor (KIH) for DHC initiation. The analytical approach provides a relationship between KIH and temperature that is consistent with experimental results