Reliable Life Prediction Research Articles

The role of small specimen creep testing within a life assessment framework for high temperature power plant

ABSTRACTThe safe operation of ageing materials and structures operating at high temperature and pressure is challenging. This paper, for the first time, clearly addresses the role of small specimen creep testing methods within a practical life assessment framework, with a case study used to illustrate the main principles and applications. To enhance the current practice for assessing the condition of creep ageing components, more proactive use of small specimen testing methods is proposed for the in-service condition assessment of power plant materials, earlier in the plant lifecycle and within a holistic life assessment framework. The paper describes how small specimen creep testing methods and other complementary tools can be used together in a new structured approach to life management. There is a clear need to provide the plant owner with more reliable and effective life prediction tools, based on earlier and more rigorous assessment of in-service life consumption. A novel, holistic, life assessment methodology including the use of small specimen creep testing has been developed by the authors.

A fatigue damage and residual strength model for unidirectional carbon/epoxy composites under on-axis tension-tension loadings

Fibre-reinforced composites experience a degradation of stiffness and strength during fatigue life. Understanding the reduction of these properties is fundamental to establish a reliable fatigue life prediction methodology. This work investigates the loss of stiffness and strength in advanced unidirectional carbon/epoxy laminates under on-axis tension-tension loads. A phenomenological stiffness-based fatigue model is formulated within the framework of continuum damage mechanics, where damage is described by the reduction of the in-plane longitudinal stiffness. The particularity of the model is to assume that the ultimate strain remains constant after fatigue damage. Thus, the residual strength model and the S-N curves are deduced from the residual stiffness model. This assumption reduces the experimental characterization of phenomenological-based approaches. The experimental challenges found in the fatigue experiments are also discussed. The accuracy of the model is verified by comparing the experimental data with the derived S-N curves.

Assessing reliability of fatigue indicator parameters for small crack growth via a probabilistic framework

Microstructurally small cracks exhibit large variability in their fatigue crack growth rate. It is accepted that the inherent variability in microstructural features is related to the uncertainty in the growth rate. However, due to (i) the lack of cycle-by-cycle experimental data, (ii) the complexity of the short crack growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only empirical damage metrics have been postulated to describe the short crack driving force metric (SCDFM) at the mesoscale level. The identification of the SCDFM of polycrystalline engineering alloys is a critical need, in order to achieve more reliable fatigue life prediction and improve material design. In this work, the first steps in the development of a general probabilistic framework are presented, which uses experimental result as an input, retrieves missing experimental data through crystal plasticity (CP) simulations, and extracts correlations utilizing machine learning and Bayesian networks (BNs). More precisely, experimental results representing cycle-by-cycle data of a short crack growing through a beta-metastable titanium alloy, VST-55531, have been acquired via phase and diffraction contrast tomography. These results serve as an input for FFT-based CP simulations, which provide the micromechanical fields influenced by the presence of the crack, complementing the information available from the experiment. In order to assess the correlation between postulated SCDFM and experimental observations, the data is mined and analyzed utilizing BNs. Results show the ability of the framework to autonomously capture relevant correlations and the equivalence in the prediction capability of different postulated SCDFMs for the high cycle fatigue regime.

Debonding of concrete-epoxy interface under the coupled effect of moisture and sustained load

Fiber reinforced polymer (FRP) is a prevalent material for strengthening or retrofitting concrete structures. It has been found that the effectiveness of entire strengthening or retrofitting scheme highly depends on the bond performance of concrete-epoxy interface. During service life, structures are exposed to complicated and unavoidable mechanical and environmental situations that can cause bond deterioration. In order to ensure the structural safety in a long run, a comprehensive experiment focusing on the coupled effect of sustained load and moisture on the bond property of concrete-epoxy interface is conducted. A drastic deterioration of concrete-epoxy interfacial fracture toughness, up to 77%, is observed under coupled sustained load and moisture exposure. Based on our experimental investigation, a predictive model is developed to describe the bond property variations of concrete-epoxy interface against long-term sustained load and moisture. The proposed predictive model can be used to obtain a more reliable and accurate service life prediction and maintenance planning for FRP-bonded concrete structures.

Reliability life prediction of smart meter based on multi-stress degradation model

Reliability life prediction of smart meter based on multi-stress degradation model

Small-sample MTBF estimation for a CNC machine tool

MTBF (Mean Time Between Failures) indicates the life and reliability of machine tools. The research into small-sample MTBF estimation of high grade CNC machine tools has great theoretical and engineering significance. After identifying the prior distribution according to the historical failure data of similar mass-produced machine tools, the authors carry out real-time reliable life prediction of a high-grade machine tool by using the Bayesian method to integrate the prior information and small-sample failure data of the machine tool. The MTBF of the five-axis machining center meets the pattern of 2-parameter Weibull distribution ( b =1.9093, a =919.4951). The MTBF point estimate of the machine tool is 815.80(h), and the interval estimate is [576.70(h), 1,232.42(h)]. Applicable to small sampling conditions and giving thorough consideration to field and test information of similar product, the method proposed in this paper provides a good reference to the reliable life prediction and assessment of special machinanical products with only small-sample test data.

Cutting tools reliability and residual life prediction from degradation indicators in turning process

In the field of cutting tool reliability, an investigation based on four complementary approaches for tool wear assessment is proposed: the approach 1 is a general failure times approach (statistical reliability based on flank wear threshold hitting times), the approach 2 is based on power consumption monitoring (statistical reliability based on power threshold hitting times), the approach 3 is based on vibration signal analysis (statistical reliability based on vibration threshold hitting times), and the approach 4 is based on the evolution of flank wear for each insert (statistical reliability based on predicted failure times). For this study, 30 identical inserts from a same batch were studied with a CNC lathe in identical turning conditions. As the remaining useful life assessment is the final goal, this study highlight four approaches in order to find out the safest one. The results obtained showed that the approach 1 has too many uncertainties, whereas the approach 2 provides more specific and safer replacement times. The approach 3, in the first hand, highlights the fact that cutting conditions are not exactly identical and, in the second hand, leads to a safe conditional replacement of an insert. Finally, it appeared that the approach 4 allows applying a predictive maintenance strategy.

A fuzzy residual strength based fatigue life prediction method

The fatigue damage problems are frequently encountered in the design of civil engineering structures. A realistic and accurate fatigue life prediction is quite essential to ensure the safety of engineering design. However, constructing a reliable fatigue life prediction model can be quite challenging. The use of traditional deterministic approach in predicting the fatigue life is sometimes too dangerous in the real practical designs as the method itself contains a wide range of uncertain factors. In this paper, a new fatigue life prediction method is going to be proposed where the residual strength is been utilized. Several cumulative damage models, capable of predicting the fatigue life of a structural element, are considered. Based on Miner\'s rule, a randomized approach is developed from a deterministic equation. The residual strength is used in a one to one transformation methodology which is used for the derivation of the fatigue life. To arrive at more robust results, fuzzy sets are introduced to model the parameter uncertainties. This leads to a convoluted fuzzy based fatigue life prediction model. The developed model is illustrated in an example analysis. The calculated results are compared with real experimental data. The applicability of this approach for a required reliability level is also discussed.

REAL-TIME RELIABILITY ASSESSMENT AND LIFETIME PREDICTION FOR BEARINGS USING THE INDIVIDUAL STATE DEVIATION BASED ON THE MANIFOLD DISTANCE

In recent years, the real-time reliability evaluation and life prediction for rolling bearings has attracted more attention. Most of the existing methods employ real-time transformation of traditional reliability indices, performance degradation trajectory or distribution analysis, which usually have certain limitations in terms of accuracy and applicability. This paper proposes a method for bearing real-time reliability evaluation and life prediction to avoid the negligence of real-time transformation of the monitored individual, as well as reduce the errors caused by the randomness from individual bearing operational process. The individual state deviation of a running rolling bearing geometrically measured by manifold distance is normalized into a state deviation degree, which is used to formulate a modified real-time reliability model for realtime reliability evaluation and lifetime prediction. Finally, the feasibility and efficiency of this method is validated by bearing run-to-failure experiments.

Real-Time Reliability Evaluation and Life Prediction for Bearings Based on Normalized Individual State Deviation

Most of the existing methods for bearing real-time reliability evaluation employ real-time transformation of traditional reliability indices, performance degradation trajectory analysis, and performance degradation distribution, which are usually limited in terms of accuracy and applicability. A method for real-time reliability evaluation and life prediction for bearings based on normalized individual state deviation is proposed in this study. First, a self-organizing map neural network is utilized to obtain the individual state deviation of a running rolling bearing. Second, individual state deviation is normalized into a state deviation degree, which is used to formulate a modified real-time reliability model for the realization of real-time reliability evaluation and residual life prediction. The proposed method combines population information with real-time monitoring information of individual bearings, and thus avoids the negligence of the real-time transformation of the monitored individual. The errors caused by the randomness of the individual bearing operational process are also reduced. Finally, the feasibility and efficiency of the proposed method is validated by performing run-to-failure experiments on bearings.

Fatigue reliability and service life prediction of aluminum naval ship details based on monitoring data

The evolution of naval vessels toward high-speed crafts subjected to severe sea conditions has promoted an increasing interest in lightweight high-strength materials. Due to its strength and weight characteristics, aluminum has been proven especially suitable as construction material for hull structures as well as other vessel parts. However, fatigue in aluminum naval crafts needs to be effectively addressed for the proper life-cycle assessment. Structural health monitoring systems constitute effective tools for measuring the structural response and assessing the structural performance under actual operational conditions. In this article, an approach for using structural health monitoring information in the fatigue reliability analysis and service life prediction of aluminum naval vessels is presented. The accumulated fatigue damage and the fatigue reliability are quantified based on structural health monitoring data acquired under different operational conditions, specified by the ship speeds, sea states, and heading angles. Additionally, an approach for estimating the reliability-based fatigue life under a given operational profile is presented. Seakeeping trial data of an aluminum high-speed naval vessel are used to illustrate the proposed approach.

Simulation-based fatigue life assessment of a mercantile vessel

Despite the availability of other transport methods such as land and air transportations, marine transportation is the most preferred and widely used transportation method in the world because of its economical advantages. In service, ships experience cyclic loading. Hence, it can be said that fatigue fracture, which occurs due to cyclic loading, is one of the most critical failure modes for vessels. Accordingly, this makes fatigue failure prevention an important design requirement in naval architecture. In general, a ship structure contains many structural components. Because of this, structural modeling typically relies on Finite Element Analysis (FEA) techniques. It is possible to increase fatigue performance of the ship structures by using FEA in computer aided engineering environment. Even if literature papers as well as rules of classification societies are available to assess effect of fatigue cracks onto the whole ship structure, analytical studies are relatively scarce because of the difficulties of modeling the whole structure and obtaining reliable fatigue life predictions. As a consequence, the objective of this study is to improve fatigue strength of a mercantile vessel against fatigue loads via analytical method. For this purpose, the fatigue life of the mercantile vessel has been investigated. Two different type of fatigue assessment models, namely Coffin-Manson and Morrow Mean stress approaches, were used and the results were compared. In order to accurately determine the fatigue life of the ship, a nonlinear finite element analysis was conducted considering plastic deformations and residual stresses. The results of this study will provide the designer with some guidelines in designing mercantile vessels.

Statistical Analysis of the Degradation Data Based on Pseudo-Life Distribution

Focus on solving the life and reliability problems of long-life and high reliability products, a method of modeling and parameter estimation on reliability evaluation and life prediction with accelerated degradation data was proposed in this paper. In the first, three statistical methods of degradation test were analyzed, then according to the features of accelerated degradation data, the statistical model of degradation data based on pseudo-life was established. This method can effectively utilize the horizontal information of degradation data of product under different accelerated stress level, and integrate the advantage that continuous-time function model fits the degradation curve of product strongly, and improve the accuracy of reliability assessment and life prediction of product.

Reliable Life Prediction for Special Equipments with the Measurable Potential Failure Mode

Reliable Life Prediction for Special Equipments with the Measurable Potential Failure Mode

Thermal cycling life prediction of Sn-3.0Ag-0.5Cu solder joint using type-I censored data.

Because solder joint interconnections are the weaknesses of microelectronic packaging, their reliability has great influence on the reliability of the entire packaging structure. Based on an accelerated life test the reliability assessment and life prediction of lead-free solder joints using Weibull distribution are investigated. The type-I interval censored lifetime data were collected from a thermal cycling test, which was implemented on microelectronic packaging with lead-free ball grid array (BGA) and fine-pitch ball grid array (FBGA) interconnection structures. The number of cycles to failure of lead-free solder joints is predicted by using a modified Engelmaier fatigue life model and a type-I censored data processing method. Then, the Pan model is employed to calculate the acceleration factor of this test. A comparison of life predictions between the proposed method and the ones calculated directly by Matlab and Minitab is conducted to demonstrate the practicability and effectiveness of the proposed method. At last, failure analysis and microstructure evolution of lead-free solders are carried out to provide useful guidance for the regular maintenance, replacement of substructure, and subsequent processing of electronic products.

Whole-life Thinking and Engineering the Future

Whole-life thinking for engineers working on the built environment has become more important in a fast changing world.Engineers are increasingly concerned with complex systems, in which the parts interact with each other and with the outside world in many ways – the relationships between the parts determine how the system behaves. Systems thinking provides one approach to developing a more robust whole life approach. Systems thinking is a process of understanding how things influence one another within a wider perspective. Complexity, chaos, and risk are endemic in all major projects. New approaches are needed to produce more reliable whole life predictions. Best value, rather than lowest cost can be achieved by using whole-life appraisal as part of the design and delivery strategy.

A New Approach for Reliability Life Prediction of Rail Vehicle Axle by Considering Vibration Measurement

The effect of vibration on the axle has been considered. Vibration measurements at different speeds have been performed on the axle of a running rail vehicle to figure out displacement, acceleration, time, and frequency response. Based on the experimental works, equivalent stress has been used to find out life of the axles for 90% and 10% reliability. Calculated life values of the rail vehicle axle have been compared with the real life data and it is found that the life of a vehicle axle taking into account the vibration effects is in good agreement with the real life of the axle.

Creep Life Prediction for Aero Gas Turbine Hot Section Component Using Artificial Neural Networks

Accurate and reliable component life prediction is crucial to ensure safety and economics of gas turbine operations. In pursuit of such improved accuracy and reliability, model-based creep life prediction methods have become more complicated and demand higher computational time. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to creep life prediction for production engines while at the same time maintain the same accuracy and reliability as that of the model-based methods. In this paper, a novel creep life prediction approach using artificial neural networks is introduced as an alternative to the model-based creep life prediction approach to provide a quick and accurate estimation of gas turbine creep life. Multilayer feed forward backpropagation neural networks have been utilized to form three neural network–based creep life prediction architectures known as the range-based, functional-based, and sensor-based architectures. The new neural network creep life prediction approach has been tested with a model single-spool turboshaft gas turbine engine. The results show that good generalization can be achieved in all three neural network architectures. It was also found that the sensor-based architecture is better than the other two in terms of accuracy, with 98% of the post-test samples possessing prediction errors within ±0.4%.

Determination Method of a Proper Small-Load-Omitting Criterion Based on the Extreme Load

A crucial step to obtain a reliable fatigue life prediction is to determine a proper small load threshold below which the cycles at small loads or stresses with high frequency causing little fatigue damage are truncated from the original load time history. By taking both the peak over threshold theory and the endurance limit threshold into account, a proper small load threshold is proposed in this paper. The optimal threshold should be high enough to remove the high-frequency small cycles and low enough to minimize the loss of the fatigue damage which maybe be truncated by the empirical small-load omitting threshold. Based on this proper threshold, the fatigue life prediction will be more reliable.

一种基于伪失效寿命的LED可靠性快速评价方法

An efficient and rapid reliability evaluation method of LED has been proposed.The pseudo-failure lifetime is tested and the lifetime data is analyzed by Minitab.The results show that the pseudo-failure lifetime of all samples are Weibull distribution.The reliability evaluation of LED products are made by comparing the scale parameters of Weibull distribution.This method has a certain reference value to reliability evaluation and life prediction of LED.