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The purpose of this paper is to analyse fatigue-life prediction based on a reliability assessment for coil springs of vehicle suspension systems using different road excitations under random loading.

In this study, a reliability assessment was conducted to predict the fatigue life of an automobile coil spring during different road data surfaces. Campus, urban and highway road surfaces were considered to capture fatigue load strain histories using a data acquisition system. Random loadings are applied on top of a coil spring where coil is fixed from down. Fatigue reliability was established as a system of correlated events during the service life to predict the probability of fatigue life using Coffin–Manson, Morrow and Smith–Watson–Topper (SWT) models.

Fatigue-life prediction based on a reliability assessment revealed that the Morrow model can predict a safe region of a life data point for the three road surfaces. Highway road data indicated the highest rate of reliability at 0.8 for approximately 1.69 × 10⁵ cycles for the SWT model.

Reliability assessment of the fatigue life of vehicle coil springs is vital for safe operation. The reliability analysis of a coil spring under random loading excitations can be used for fatigue-life prediction.

The purpose of this paper is to optimize the key dimensions parameters of the missile suspension structure to ensure the structural fatigue life (>10000 cycles) with the reliability of 0.995.

The design objective is the fatigue life reliability of the structure, while the design variables are the four fatigue‐sensitive dimensions. The nominal stress approach is introduced to predict the fatigue life, and it was verified by comparing with experimental data. The second respond surface method is applied to solve the reliability in a finite element‐supported analysis using software MSC Patran/Nastran. A Sequential quadratic programming (SQP) algorithm is used for structure optimization.

The fillet radius r is the most important factor that influences the fatigue life reliability of the structure. The four optimal dimensions parameters are obtained by a reliability‐based design optimization process with the fatigue life and reliability fulfilling the demands.

The optimal result can be used as the design values for missile suspension structure. The feasibility of the reliability‐based design optimization method is validated for the design of missile suspension structure.

The purpose of this paper is to present the durability analysis in predicting the reliability life cycle for an automobile crankshaft under random stress load using the stochastic process. Due to the limitations associated with the actual loading history obtained from the experimental analysis or due to the sensitivity of the strain gauge, the fatigue reliability life cycle assessment has lower accuracy and efficiency for fatigue life prediction.

The proposed Markov process embeds the actual maximum and minimum stresses by a continuous updating process for stress load history data. This is to reduce the large credible intervals and missing loading points used for fatigue life prediction. With the reduction and missing loading intervals, the accuracy of fatigue life prediction for the crankshaft was validated using the statistical correlation properties.

It was observed that fatigue reliability corresponded well by reporting the accuracy of 95–98 per cent with a mean squared error of 1.5–3 per cent for durability and mean cycle to failure. Hence, the proposed fatigue reliability assessment provides an accurate, efficient, fast and cost-effective durability analysis in contrast to costly and lengthy experimental techniques.

An important implication of this study is durability-based life cycle assessment by developing the reliability and hazard rate index under random stress loading using the stochastic technique in modeling for improving the sensitivity of the strain gauge.

The durability analysis is one of the fundamental attributes for the safe operation of any component, especially in the automotive industry. Focusing on safety, structural health monitoring aims at the quantification of the probability of failure under mixed mode loading. In practice, diverse types of protective barriers are placed as safeguards from the hazard posed by the system operation.

Durability analysis has the ability to deal with the longevity and dependability of parts, products and systems in any industry. More poignantly, it is about controlling risk whereby engineering incorporates a wide variety of analytical techniques designed to help engineers understand the failure modes and patterns of these parts, products and systems. This would enable the automotive industry to improve design and increase the life cycle with the durability assessment field focussing on product reliability and sustainability assurance.

The accuracy of the simulated fatigue life was statistically correlated with a 95 per cent boundary condition towards the actual fatigue through the validation process using finite element analysis. Furthermore, the embedded Markov process has high accuracy in generating synthetic load history for the fatigue life cycle assessment. More importantly, the fatigue reliability life cycle assessment can be performed with high accuracy and efficiency in assessing the integrity of the component regarding structural integrity.

This study aims to determine the reliability assessment based on the predicted fatigue life of leaf spring under random strain loading.

Random loading data were extracted from three various road conditions at 200 Hz using a strain gauge for a duration of 100 s. The fatigue life was predicted using strain-life approaches of Coffin–Manson, Morrow and Smith–Watson–Topper (SWT) models.

The leaf spring had the highest fatigue life of 1,544 cycle/block under highway data compared uphill (1,299 cycle/block) and downhill (1,008 cycle/block) data. Besides that, the statistical properties of kurtosis showed that uphill data were the highest at 3.81 resulted in the presence of high amplitude in the strain loading data. For fatigue life-based reliability assessment, the SWT model provided a narrower shape compared to the Coffin–Manson and Morrow models using the Gumbel distribution. The SWT model had the lowest mean cycle to failure of 1,250 cycle/block followed by Morrow model (1,317 cycle/block) and the Coffin–Manson model (1,429 cycle/block). The SWT model considers the mean stress effects by interpreting the strain energy density that will influence the reliability assessment.

The reliability assessment based on fatigue life prediction is conducted using the Gumbel distribution to investigate the behaviour of fatigue random loading, where most previous studies had concentrated on a Weibull distribution on random data.

Thus, this study proposes that the Gumbel distribution is suitable for analysing the reliability of random loading data in assessing with the fatigue life prediction of a heavy vehicle leaf spring.

In this paper an overview of the issues underlying surface mount solder joint long‐term reliability is presented. The paper gives state‐of‐the‐art solutions for ‘Design for Reliability’ in simple design tool form, discusses the important accelerated reliability test issues, and provides the equations to estimate the reliability of SM product in use as well as the expected cyclic life in accelerated tests.

Due to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.

First, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.

The TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.

The purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

The purpose of this study is to improve the computational efficiency and accuracy of fatigue reliability analysis.

By absorbing the advantages of Markov chain and active Kriging model into the hierarchical collaborative strategy, an enhanced active Kriging-based hierarchical collaborative model (DCEAK) is proposed.

The analysis results show that the proposed DCEAK method holds high accuracy and efficiency in dealing with fatigue reliability analysis with high nonlinearity and small failure probability.

The effectiveness of the presented method in more complex reliability analysis problems (i.e. noisy problems, high-dimensional issues etc.) should be further validated.

The current efforts can provide a feasible way to analyze the reliability performance and identify the sensitive variables in aeroengine mechanisms.

To improve the computational efficiency and accuracy of fatigue reliability analysis, an enhanced active DCEAK is proposed and the corresponding fatigue reliability framework is established for the first time.

Three test platforms for long-term continuous loading are adopted to test the actuator prototypes of the 500-meter aperture spherical radio telescope (FAST). However, the wire ropes that are the key components of these platforms often break during testing. The purpose of this paper is to present an effective dimension design method for these wire ropes. This method is based on fatigue reliability theory.

Three types of stresses are introduced into the total stress model of the wire rope according to the complicated stress conditions. The fatigue strength of the ropes is also discussed in this paper. Then, the total stress model and the results of fatigue strength analysis are applied to set the optimization function for these wire ropes. Subsequently, this optimization function is used to calculate the reliability of previously developed wire ropes in relation to the actuator test platform.

The wire rope is unreliable, which is a finding that corresponds to those of previous tests. Upon drawing the optimal curve from the optimization function (whose optimal objective is the wire diameter), a wire rope is optimized for the FAST actuator test platforms. Finally, this optimized rope is used on the new actuator test platform. No fracture phenomenon has been detected in tests conducted over the past six months.

The fatigue reliability theory-based optimization function for wire ropes can be adopted for the universal dimension design of other wire ropes.

Aero-engine components endure combined high and low cycle fatigue (CCF) loading during service, which has attracted more research attention in recent years. This study aims to construct a new framework for the prediction of probabilistic fatigue life and reliability evaluation of an aero-engine turbine shaft under CCF loading if considering the material uncertainty.

To study the CCF failure of the aero-engine turbine shaft, a CCF test is carried out. An improved damage accumulation model is first introduced to predict the CCF life and present high prediction accuracy in the CCF loading situation based on the test. Then, the probabilistic fatigue life of the turbine shaft is predicted based on the finite element analysis and Monte Carlo analysis, where the material uncertainty is taken into account. At last, the reliability evaluation of the turbine shaft is conducted by stress-strength interference models based on an improved damage accumulation model.

The results indicate that predictions agree well with the tested data. The improved damage accumulation model can accurately predict the CCF life because of interaction damage between low cycle fatigue loading and high cycle fatigue loading. As a result, a framework is available for accurate probabilistic fatigue life prediction and reliability evaluation.

The proposed framework and the presented testing in this study show high efficiency on probabilistic CCF fatigue life prediction and can provide technical support for fatigue optimization of the turbine shaft.

The novelty of this work is that CCF loading and material uncertainty are considered in probabilistic fatigue life prediction.

The purpose of this paper is to focus on the reliability assessment on the basis of automobile suspension fatigue life using wavelet decomposition method.

The discrete wavelet transform (DWT) of automobile coil spring signal is implemented as a response to different road surfaces. A reliability analysis is applied to determine the potential of the wavelet implementation in fatigue life analysis. The signals used in this study are highway and rural road.

On the basis of the implementation of wavelet decomposition method, low-level decomposition replicates the original signals in comparison with high-level decomposition. The fatigue life of low-level decomposition lies in the 2:1 and 1:2 correlation graph. The percentage difference for mean cycle to failure presents low values for low-level decomposition, with 44.31 per cent for highway and 44.20 per cent for rural road. The percentage of difference for high-level decomposition is high.

The determination of fatigue life analysis by using the DWT method is suitable for low-level decomposition. High-level decomposition is considered noise that cannot be eliminated and does not contribute to the failure of the structure.