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The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling.

In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order reliability method.

The Bayesian modeling leads to an appropriate assembly function for the tolerance and reliability analysis of mechanical assemblies for assessment of the assembly quality, by evaluation of the assembly requirement(s) at the key characteristics in the assembly process. The efficiency of the proposed method by considering a case study has been illustrated and validated by comparison to Monte Carlo simulations.

The method is practically easy to be automated for use within CAD/CAM software for the assembly quality control in industrial applications.

Bayesian modeling for tolerance–reliability analysis of mechanical assemblies, which has not been previously considered in the literature, is a potentially interesting concept that can be extended to other corresponding fields of the tolerance design and the quality control.

The purpose of this paper is to discuss the characteristics of several stochastic simulation methods applied in computation issue of structure health monitoring (SHM).

On the basis of the previous studies, this research focusses on four promising methods: transitional Markov chain Monte Carlo (TMCMC), slice sampling, slice-Metropolis-Hasting (M-H), and TMCMC-slice algorithm. The slice-M-H is the improved slice sampling algorithm, and the TMCMC-slice is the improved TMCMC algorithm. The performances of the parameters samples generated by these four algorithms are evaluated using two examples: one is the numerical example of a cantilever plate; another is the plate experiment simulating one part of the mechanical structure.

Both the numerical example and experiment show that, identification accuracy of slice-M-H is higher than that of slice sampling; and the identification accuracy of TMCMC-slice is higher than that of TMCMC. In general, the identification accuracy of the methods based on slice (slice sampling and slice-M-H) is higher than that of the methods based on TMCMC (TMCMC and TMCMC-slice).

The stochastic simulation methods evaluated in this paper are mainly two categories of representative methods: one introduces the intermediate probability density functions, and another one is the auxiliary variable approach. This paper provides important references about the stochastic simulation methods to solve the ill-conditioned computation issue, which is commonly encountered in SHM.

This paper aims to measure the sensitivity of the structure’s deformation numerical model (NM) related to the various types of the design parameters, which is a suitable method for parameter selection to increase the time of model-updating.

In this research, a variance-based sensitivity analysis (VBSA) approach is proposed to measure the sensitivity of NM of structures. In this way, the contribution of measurements of the structure (such as design parameter values and geometry) on the output of NM is studied using first-order and total-order sensitivity indices developed by Sobol’. In this way the generated data set of parameters by considering different distributions such as Gaussian or uniform distribution and different order as input along with, the resulted deformation variables of NM as output has been submitted to the Sobol’ indices estimation procedure. To the verification of VBSA results, a gradient-based sensitivity analysis (SA), which is developed as a global SA method has been developed to measure the global sensitivity of NM then implemented over the NM’s results of a tunnel.

Regarding the estimated indices, it has been concluded that the derived deformation functions from the tunnel’s NM usually are non-additive. Also, some parameters have been determined as most effective on the deformation functions, which can be selected for model-updating to avoid a time-consuming process, so those may better to be considered in the group of updating parameters. In this procedure for SA of the model, also some interactions between the selected parameters with other parameters, which are beneficial to be considered in the model-updating procedure, have been detected. In this study, some parameters approximately (27 per cent of the total) with no effect over the all objective functions have been determined to be excluded from the parameter candidates for model-updating. Also, the resulted indices of implemented VBSA were approved during validation by the gradient-based indices.

The introduced method has been implemented for a circular lined tunnel’s NM, which has been created by Fast Lagrangian Analysis of Continua software.

This paper plans to apply a statistical method, which is global on the results of the NM of a soil structure by a complex system for parameter selection to avoid the time-consuming model-updating process.

The purpose of this paper is to value an innovation project derived from a product diversification strategy in the agricultural auto parts sector, considering uncertainties and flexibility as sources of value for the project.

A case study project is presented and valued using real options, with the possibility of including new information modelled by Bayes’ theorem, which enables the fitting of the probabilities of the project for the purpose of valuation through the decision tree method.

The results indicate the effect of new information and provide implications for value creation, demonstrating the need for a more profound and systematic approach to the use of investment strategies considering factors endogenous and exogenous to a firm that can and should be considered in the decision-making process.

This study contributes to an understanding of the relationship of strategy and innovation through product diversification with value creation and serves as a guide for controlling the possibility of investments in diversified companies and agribusiness using the methodology discussed in this paper.

Valuing the inclusion of new information through Bayesian inference in the analysis of investments is a methodological feature that has received minimal attention in the literature. Therefore, the use of Bayesian theory for the real options model applied to agribusiness is the main innovation of this study to demonstrate new ways of modelling uncertainty in addition to stochastic processes.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

The purpose of this paper is to explore risk management models applied to electric power markets. Several Value‐at‐Risk (VaR) models are applied to day‐ahead forward contract electric power price data to see which, if any, could be best used in practice.

A time‐varying parameter estimation procedure is used which gives all models the ability to track volatility clustering.

The RiskMetrics model outperforms the GARCH model for 95 per cent VaR, whereas the GARCH model outperforms RiskMetrics for 99 per cent VaR. Both these models are better at handling volatility clustering than the Stable model. However, the Stable model was more accurate in detecting the numbers of daily returns beyond the VaR limits. The fact that the parsimonious RiskMetrics model performed well suggests that efforts to specify the model dynamics may be unnecessary in practice.

The present study provides a starting point for further research and suggests models that could be applied to electricity markets.

Electricity markets are a challenge to risk modelers, as they typically exhibit non‐Normal return distributions with time‐varying volatility. Previous academic research in this area is rather scarce.

The purpose of this study is to review the existing fatigue and vibration-based structural health monitoring techniques and highlight the advantages of combining both approaches.

Fatigue monitoring requires a fatigue model of the material, the stresses at specific points of the structure, a cycle counting technique and a fatigue damage criterion. Firstly, this paper reviews existing structural health monitoring (SHM) techniques, addresses their principal classifications and presents the main characteristics of each technique, with a particular emphasis on modal-based methodologies. Automated modal analysis, damage detection and localisation techniques are also reviewed. Fatigue monitoring is an SHM technique which evaluate the structural fatigue damage in real time. Stress estimation techniques and damage accumulation models based on the S-N field and the Miner rule are also reviewed in this paper.

A vast amount of research has been carried out in the field of SHM. The literature about fatigue calculation, fatigue testing, fatigue modelling and remaining fatigue life is also extensive. However, the number of publications related to monitor the fatigue process is scarce. A methodology to perform real-time structural fatigue monitoring, in both time and frequency domains, is presented.

Fatigue monitoring can be combined (applied simultaneously) with other vibration-based SHM techniques, which might significantly increase the reliability of the monitoring techniques.