RESIDUAL LIFE PREDICTION AND DESIGN CORRECTION METHOD OF CORRODED CIRCULAR STEEL TUBES BASED ON TIME-VARYING RELIABILITY

In order to improve the prediction accuracy of non-Gaussian data and build reasonably the prediction model, a novel residual life prediction method is proposed. A dynamic weighted Markov model is constructed by real time data and historical data, and the residual life is predicted by particle filter. The particles of the state vector are predicted and updated instantaneously using particle filter. The probability distribution of the predicted value is estimated by the updated particles. The residual life can be predicted using the set threshold of the state. This method improves the accuracy of residual life prediction. Finally, the advantage of this proposed method was shown experimentally using the bearings’ full cycle data.

Condition monitoring data and reliability data were fused, and the residual life prediction method was proposed based on Proportional Hazard Model (PHM) for rolling bearings of rail vehicles. This method can provide basic data support for rail vehicles' operation security and provide guidance for the optimization of maintenance strategy. Firstly, rolling bearings of rail vehicles was briefly introduced. And then, three specific steps of the proposed life prediction method were described in detail, which including sample data collection, parameter estimation and life prediction. Finally, the experiment was carried out which used vibration data and failure event data of the rolling bearings' full life cycle, and the results show that the correlation coefficient between PHM outputs and the targets was greater than 0.9. The experiment results verify the effectiveness and feasibility of the proposed residual life prediction approach based on PHM.

Finite element simulation of load bearing capacity of circular CFST long columns with localized corrosion under eccentric load

Effect of local corrosion on the axial compression behavior of circular steel tubes

Galvanic corrosion induced localized defects and resulting strength reduction of circular steel tubes under axial compression: An experimental study

This paper presents a finite element analysis (FEA) of and reliability study on concrete-filled steel tube (CFST) members under the combined effects of corrosion and compressive loading. First, a stochastic-based FE model is established through the proposed secondary development program based on ABAQUS 2021 software. The model could account for the uncertainties of material, geometric, and corrosion effect on CFST members. The reliability of the built model was validated through experimental data of corroded CFST members under compression loading. Subsequently, the compressive performance of CFST under a combination of corrosion and loading was further investigated by numerical parameter analysis. A total of 1800 models were created to clarify the coupling mechanism among the core concrete strength, the steel tube strength, the steel ratio, and the maximum strength of the CFST member. Three theoretical formulas presented in classical design standards were used to calculate the axial compressive strength of the corroded CFST, and the uncertainty parameters μkp and δkp were also obtained for the discussed design formulas. Finally, the First Order and Second Moment (FOSM) method was employed to estimate the reliability indices β across different standards. The calculations revealed that the reliability indices β according to European standard ranges from 2.93 to 5.52, with some results falling below the target reliability index βT of 3.65. In addition, the multi-parameter coupling effects on reliability index β were investigated, and the main influencing factors were obtained. By leveraging the reliability analysis, reasonable design requirements can be proposed for CFST members under the coupling effects of corrosion and external load, which provides a design basis for the CFST member.

Residual life prediction is a critical and difficult problem in condition-based maintenance decision-making. Aiming to deal with the problems that practical data is limited and the estimation of initial parameters is not accurate in maintenance practice, a residual life prediction method for gearbox based on stochastic filtering (SF) is proposed. In this method, recursive expectation maximization (REM) algorithm is introduced to update the parameters, and a maximum likelihood estimation method is designed to update the unknown parameters. Finally, the validity and practicability of the model are validated by an example.

SUMMARYIn this paper, an online deterioration prediction and residual life prediction method is proposed for maintenance of rotating equipment in thermal power plants. The condition of rotating equipment is inspected by vibration measurement. A mathematical model for the deterioration of the rotating equipment is derived in order to predict the future condition of the rotating equipment. The residual life of the rotating equipment is predicted by deterioration prediction using online estimation of the model parameters with a forgetting factor. The effectiveness of the residual life prediction method is assured by applying it to actual vibration data of rotating equipment.

This paper proposes a residual fatigue life prediction method that considers the loading interaction effect, to predict the fatigue life of composite laminates under variable amplitude loading. Based on the study of static mechanical and fatigue properties of composite laminates, the influence mechanism of the loading interaction effect on material fatigue life is analyzed, and an exponential function including adjacent load amplitudes and fatigue life is proposed as a parameter to describe the loading interaction effect. Then, an equation describing the damage evolution of composite laminates during fatigue is proposed. A simple prediction method for residual fatigue life under variable loading considering the loading interaction effect is proposed based on the damage equivalent theory. Finally, through experimental validation, it is found that the prediction error of the proposed model does not exceed 50%, which is lower than the other models mentioned in the paper.

In this paper, an on-line deterioration prediction and residual life prediction method is proposed for maintenance of rotating equipment in thermal power plants. Condition of rotating equipment is inspected by vibration measurement. A mathematical model for the deterioration of the rotating equipment is derived in order to predict future condition of the rotating equipment. Residual life of the rotating equipment is predicted by deterioration prediction using on-line estimation of model parameters with forgetting factor. The effectiveness of the residual life prediction method is assured by applying it to actual vibration data of rotating equipment.

In order to reduce the high costs of Communication Equipment operation and maintenance management, a novel residual life prediction method for Communication Equipment based on multi- environmental time similarity (METS) theory was proposed to make scientific and reasonable cost maintenance strategy of Communication Equipment. This method has introduced a reference Communication Equipment which has similar operational environment and type of the object Communication Equipment to obtain similarity coefficient (running hours and start/stop cycles) of the reference unit life evaluation parameters under actual operation conditions and benchmark operation conditions by using experience operational data. Based on the METS theory, the object unit factored running hours and start/stop cycles under benchmark operative conditions were obtained through calculating its actual running hours and start/stop cycles under actual operational conditions. And then the object unit residual life was predicted by comparing with maximum maintenance interval. Finally, a factory Communication Equipment HGP component residual life prediction is provided as the example to verify the scientific validity and feasibility of the prediction method.

Enhanced sintering of stainless steel by addition of copper phosphide: J.D. Bolton, H. Preusse. (University of Bradford, Bradford, UK.)

A residual service life prediction method based on critical life curves of selected bridge members is presented in this paper for bridges in service. The lowest target reliability index is ...

Aiming at the problem of inaccurate description of two-phase degradation of products, a two-phase nonlinear Wiener model was established, and a residual life prediction method was proposed. Firstly, considering the random effect of the degenerate change-point, a two-phase nonlinear Wiener degradation model is established by using the normal distribution to describe the drift parameters of each phase. Secondly, based on Bayesian theory, the posteriori distribution of model parameters is derived, and the MHGS method is proposed to estimate the parameters of the two-phase degradation model. Then, a method to determine the degradation stage was proposed, based on DIC criterion. Combined with the state-space model and Kalman filter, the online updating process and residual life probability distribution of the two-phase degradation model were deduced. Finally, the proposed model and method are validated by solder joint degradation data. The results show that the proposed method can accurately estimate the model parameters and predict the residual life. Compared with the two-phase model of linear Wiener process, the two-phase model of nonlinear Wiener process proposed in this paper has higher prediction accuracy.

Based on the dynamic properties of electrical connector intermittent failure, the model and methods for residual life prediction for electrical connectors are studied in this paper. Firstly, the mechanism of electrical connector intermittent failure is analyzed, and the area enclosed by the contact resistance curve and the fault threshold is defined as the generalized severity of intermittent failure to describe how severe the electrical connector’s intermittent failure is. Then, the Hidden Semi-Markov Model (HSMM) is introduced to build the residual life prediction model of the electrical connector. Further, the evaluation method of using the state and prediction method for residual life are studied. Finally, by carrying out the residual life prediction test, the effectiveness of the residual life prediction method for electrical connectors based on intermittent failure and HSMM is verified.