Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Structural first failure times under non-Gaussian stochastic behavior

An analytical moment-based method for calculating structuralfirst failure times under non-Gaussian stochastic behavior is proposed. In the method, a power series that constants can be obtained from response moments （skewness, kurtosis, etc.） is used firstly to map a non-Gaussian structural response into a standard Gaussian process, then mean up-crossing rates, mean clump size and the initial passage probability of a critical barrier level by the original structural response are estimated, and finally, the formula for calculating first failure times is established on the assur^ption that corrected up-crossing rates are independent. An analysis of a nonlinear single-degree-of-freedom dynamical system excited by a Gaussian model of load not only demonstrates the usage of the proposed method but also shows the accuracy and efficiency of the proposed method by comparisons between the present method and other methods such as Monte Carlo simulation and the traditional Gaussian model.

Comparison of Cox proportional hazards model,Cox proportional hazards with time-varying coefficients model,and lognormal accelerated failure time model:Application in time to event analysis of melioidosis patients

Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Methods:A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah,Kedah and Hospital Tuanku Fauziah,Perlis in Northern Malaysia due to confirmed-cultured melioidosis.The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis,and Cox’s models and the findings were compared by using the goodness of fit methods.The analyses were done by using Stata SE version 14.0.Results:A total of 242 patients(53.4%)survived.In this study,the median survival time of melioidosis patients was 30.0 days(95%CI 0.0-60.9).Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model.In AFT survival analysis,a total of seven significant prognostic factors were identified.The results were found to be only a slight difference between the identified prognostic factors among the models.AFT survival showed better results compared to Cox's models,with the lowest Akaike information criteria and best fitted Cox-snell residuals.Conclusions:AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.

Automatic prediction of time to failure of open pit mine slopes based on radar monitoring and inverse velocity method

Radar slope monitoring is now widely used across the world, for example, the slope stability radar(SSR)and the movement and surveying radar(MSR) are currently in use in many mines around the world.However, to fully realize the effectiveness of this radar in notifying mine personnel of an impending slope failure, a method that can confidently predict the time of failure is necessary. The model developed in this study is based on the inverse velocity method pioneered by Fukuzono in 1985. The model named the slope failure prediction model(SFPM) was validated with the displacement data from two slope failures monitored with the MSR. The model was found to be very effective in predicting the time to failure while providing adequate evacuation time once the progressive displacement stage is reached.

Numerical investigation into pillar failure induced by time-dependent skin degradation

This paper focuses on the instability mechanism of an isolated pillar, caused by time-dependent skin degradation and strength heterogeneity. The time-dependent skin degradation is simulated with a non-linear rheological model capable of simulating tertiary creep, whereby two different pillar failure cases are investigated. The first case is of an isolated pillar in a deep hard rock underground mine and subjected to high stresses. The results show that pillar degradation is limited to the regions near the surface or the skin until two months after ore extraction. Afterwards degradation starts to extend deeper into the pillar, eventually leaving a highly-stressed pillar core due to stress transfer from the failed skin.Rockburst potential indices show that the risk increases exponentially at the core as time goes by. It is then demonstrated that the progressive skin degradation cannot be simulated with conventional strain-softening model assuming brittle failure. The parametric study with respect to the degree of heterogeneity reveals that heterogeneity is key to the occurrence of progressive skin degradation. The second case investigated in this study is pillar failure taking place in a very long period. Such failure becomes significantly important when assessing the risk for ground subsidence caused by pillar collapse in an abandoned mine. The analysis results demonstrate that the employed non-linear rheological model can simulate gradual skin degradation taking place over several hundred years. The percentage of damage zone volume within the pillar is merely 1% after a lapse of one days and increases to 50% after one hundred years, indicating a high risk for pillar collapse in the long term. The vertical displacements within the pillar also indicate the risk of subsidence. The proposed method is suitable for evaluating the risk of ground surface subsidence above an abandoned mine.

Robust reliable H_∞ control for discrete-time Markov jump linear systems with actuator failures

The robust reliable H∞ control problem for discrete-time Markovian jump systems with actuator failures is studied. A more practical model of actuator failures than outage is considered. Based on the state feedback method, the resulting closed-loop systems are reliable in that they remain robust stochastically stable and satisfy a certain level of H∞ disturbance attenuation not only when all actuators are operational, but also in case of some actuator failures, The solvability condition of controllers can be equivalent to a feasibility problem of coupled linear matrix inequalities （LMIs）. A numerical example is also given to illustrate the design procedures and their effectiveness.

Bayesian machine learning-based method for prediction of slope failure time

The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time(SFT).The observational and model uncertainties could lead the predicted SFT calculated from the phenomenological models to deviate from the actual SFT.Currently,very limited study has been conducted on how to evaluate the effect of such uncertainties on SFT prediction.In this paper,a comprehensive slope failure database was compiled.A Bayesian machine learning(BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction,through which the probabilistic distribution of the SFT can be obtained.This method was illustrated in detail with an example.Verification studies show that the BML-based method is superior to the traditional inverse velocity method(INVM)and the maximum likelihood method for predicting SFT.The proposed method in this study provides an effective tool for SFT prediction.

Bayesian Joint Modelling of Survival Time and Longitudinal CD4 Cell Counts Using Accelerated Failure Time and Generalized Error Distributions

Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.

Load-redistribution strategy based on time-varying load against cascading failure of complex network

Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.

EVALUATION OF THE TIME DEPENDENT FAILURE ASSESSMENT CURVES FOR 10CrMo910 AND 316 SS AT 550℃

10CrMo910 and 316 stainless steel are widely adopted in high temperature structures of power generations, chemical processing plants and petroleum refineries. In this work, a total of 10000 hour tensile creep test on 16 specimens of such two materials was conducted at 550℃. On the basis of the experimental results, the isochronous stress-strain curves and time-dependent failure assessment curves of the two materials were given. Finally, the formulae of time dependent failure assessment curve for 10CrMo910 and 316 stainless steel corresponding to long-term creep cases, which could be utilized in the high temperature defects assessment, were established. The procedure for defining the time-dependent failure assessment curves was also presented.

Impact of cirrhosis-related complications on posttransplant survival in patients with acute-on-chronic liver failure

Background:Acute-on-chronic liver failure(ACLF)is a life-threatening syndrome defined as acute decompensation in patients with chronic liver disease.Liver transplantation(LT)is the most effective treatment.We aimed to assess the impact of cirrhosis-related complications pre-LT on the posttransplant prognosis of patients with ACLF.Methods:This was an observational cohort study conducted between January 2018 and December 2020.Clinical characteristics,cirrhosis-related complications at LT and patient survival post-LT were collected.All liver recipients with ACLF were followed for 1 year post-LT.Results:A total of 212 LT recipients with ACLF were enrolled,including 75(35.4%)patients with ACLF-1,64(30.2%)with ACLF-2,and 73(34.4%)with ACLF-3.The median waiting time for LT was 11(4-24)days.The most prevalent cirrhosis-related complication was ascites(78.8%),followed by hepatic encephalopathy(57.1%),bacterial infections(48.1%),hepatorenal syndrome(22.2%)and gastrointestinal bleeding(11.3%).Survival analyses showed that patients with complications at LT had a significantly lower survival probability at both 3 months and 1 year after LT than those without complications(all P<0.05).A simplified model was developed by assigning one point to each complication:transplantation for ACLF with cirrhosis-related complication(TACC)model.Risk stratification of TACC model identified 3 strata(≥4,=3,and≤2)with high,median and low risk of death after LT(P<0.001).Moreover,the TACC model showed a comparable ability for predicting the outcome post-LT to the other four prognostic models(chronic liver failure-consortium ACLF score,Chinese Group on the Study of Severe Hepatitis B-ACLF score,model for end-stage liver disease score and Child-Turcotte-Pugh score).Conclusions:The presence of cirrhosis-related complications pre-LT increases the risk of death post-LT in patients with ACLF.The TACC model based on the number of cirrhosis-related complications pre-LT could stratify posttransplant survival,which might help to determine transplant timing for ACLF.

Time Domain Simulation of a One Line Failure for a DP-assisted Mooring System

这篇论文集中于与一个帮助 DP 的绳索系统装备的一个半能沉入水中的平台的研究。基于帮助 DP 的绳索系统的工作原则和平台运动的模型，一个时间域模拟程序被使用分析影响在一线失败的情况中，在平台运动上，驱动推进器和系在的线的紧张的消费。结果显示出那，在 10 年的风下面主导，一线失败几乎没在系在的线的紧张上有很少影响。当失败线是上风的时，电源消费将与精确性的一个削弱的位置极大地增加。然而失败线是什么时候下风，电源消费将与精确性的一个部分加强的位置被减少。

Time-domain characteristics of overlying strata failure under condition of longwall ascending mining

Ascending mining is one of the most effective ways to solve problems of water inrush, gas outburst and rock burst in coal seams mining. In order to reveal the law of motion and spatiotemporal relationship of overlaying strata, field measurement has been done in a mine. Long distance drillings were constructed from 4# coal seam to 6# coal seam at several certain typical positions, and movement and failure law of overlying strata after mining was analyzed by drilling video and observing the fluid leakage. Besides, we also analyzed the spatiotemporal development law of overlying strata failure with different mining heights and time intervals in the lower coal seam. The results show that: ascending mining is significantly affected by time-domain characteristics of overlaying strata failure after the lower coal seam's mining, height equations of caving zone and fractured zone are given in this paper, and the feasibility of ascending mining was compartmentalized concretely according to the spatiotemporal relationship. Research methods and conclusions of this paper have certain referential significance for the study of ascending mining, mining under water, mining under building, mining under railway and stress-relief mining.

Reliability evaluation of bladder accumulator with no failure data

As a bladder accumulator is a high reliable and long life component in a hydraulic system,its cost is high and it takes a lot of time to test its reliability,therefore,a reliability test with small sample is performed,and no failure data is obtained using the method of fixed time truncation. In the case of Weibull distribution,a life reliability model of bladder energy storage is established by Bayesian method using the optimal confidence intervals method,a model of one-sided lower confidence intervals of the reliability and one-sided lower confidence intervals model of the reliability life are established. Results of experiments show that the evaluation method of no failure data under Weibull distribution is a good way to evaluate the reliability of the accumulator,which is convenient for engineering application,and the reliability of the accumulator has theoretical and practical significance.

Self-Sensing TDR for Bearing Failure Detection of CFRP Laminate Fastener Hole with Particular Reference to the Effect of Fasteners

Carbon fiber reinforced polymer composites (CFRP) have been applied to aerospace and automobile structures. For many CFRP structures, mechanical metallic fasteners are usually adopted. For the fasteners used in internal structures such as a wing box, the damage to the CFRP structures around fastener holes is visually quite difficult to find. A simple method to find the damage around fastener holes is required. In this study a self-sensing time domain reflectometry (TDR) method is newly applied to detect bearing failure around the fastener holes of CFRP structures. A microstrip-line method is generally used to create a transmission line. When the transmission line is mounted near the metallic fasteners, they may affect the impedance of the transmission line. In this study, the effect of distance between the fasteners and the transmission line was numerically investigated using a finite difference time domain analysis method. After finding the appropriate distance, experiments were performed to detect the bearing failure around a fastener hole. The experiments showed the performance of the self-sensing TDR for detecting bearing failure.

Concave Group Selection of Nonparameter Additive Accelerated Failure Time Model

In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.

A Time History Method for Analysing Operational Piping Vibrations

Vibration failure of piping is a serious problem and a matter of concern for safety and reliability of plant operations. Fatigue is the main cause of such failures. Due to the complexity of the phenomenon no closed form design solutions are available. In our study an analytical technique based on the theory of vibrations in the time domain has been presented. Using the inverse theory, the problem has been reduced to a system of Volterra Integral equations to be solved simultaneously at every time step. The solution of the inverse problem may be used in the conventional method to calculate stresses and end reactions which are important from the perspective of engineering design and condition monitoring. The method is robust, simple and can be easily adopted by practicing engineers.

RELIABILITY ANALYSIS FOR A REPAIRABLE PARALLEL SYSTEM WITH TIME-VARYING FAILURE RATES

To solve a real problem:how to calculate the reliability of a system with time-varying failure rates in industry systems,this paper studies a model for the load-sharing parallel system with time-varying failure rates,and obtains calculating formulas of reliability and availability of the system by solving differential equations.In this paper,the failure rates are expressed in polynomial configuration.The constant,linear and Weibull failure rate are in their special form.The polynomial failure rates provide flexibility in modeling the practical time-varying failure rates.

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance delivery.Task execution failure becomes common in the CC environment.Therefore,fault-tolerant scheduling techniques in CC environment are essential for handling performance differences,resourcefluxes,and failures.Recently,several intelli-gent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics.With this motivation,this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme(GTO-FTASS)in CC environment.The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account.The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops.Besides,the GTO-FTASS model derives afitness function involving two parameters such as expected time of completion(ETC)and failure probability of executing a task.In addition,the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model.The experimental vali-dation of the GTO-FTASS model has been performed and the results are inspected under several aspects.Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.

Actuator Fault Diagnosis for a Class of One-Dimensional Nonlinear Heat Equations via Backstepping Method

In this paper, an actuator fault diagnosis scheme based on the backstepping method is proposed for a class of nonlinear heat equations. The fault diagnosis scheme includes fault detection, fault estimation and time to failure (TTF) prediction. Firstly, we achieve fault detection by comparing the detection residual with a predetermined threshold, where the detection residual is defined as the difference between the observer output and the system measurement output. Then, we estimate the fault function through the fault parameter update law and calculate the TTF using only limited measurements. Finally, the numerical simulation is performed on a nonlinear heat equation to verify the effectiveness of the proposed fault diagnosis scheme.