Reliability analysis of a system with two-stage degradation using Wiener processes with piecewise linear drift

2020 ◽  
Vol 32 (1) ◽  
pp. 3-29
Author(s):  
Qinglai Dong ◽  
Lirong Cui
Keyword(s):  
Residual Life ◽  
Piecewise Linear ◽  
Dynamic Environment ◽  
Random Variable ◽  
Degradation Process ◽  
Assessment Method ◽  
Two Stage ◽  
Linear Drift ◽  
Wiener Processes ◽  
Two Stages

Abstract We study a model of a two-stage degradation process in a dynamic environment. The two stages, the normal stage and the defective stage, are separated by the first hitting time of the alarm threshold by the degradation level. Wiener processes with piecewise linear drift are used in each stage to describe the degradation level in a dynamic environment. System failure is triggered in two ways: the system degradation level reaches the defect-based failure threshold; the duration in the defective stage is larger than the duration-based failure threshold. Explicit expressions for the system reliability for different duration-based failure thresholds are obtained. These include when the duration-based failure threshold is zero, when it is a positive constant and when it tends to infinity. A simulation procedure is described for the case in which the duration-based failure threshold is a random variable. Finally, some numerical examples are presented to illustrate the proposed reliability assessment method. The modelling method and the results can be used for reliability evaluation, residual life prediction and maintenance decision optimization of a system with two-stage degradation in a dynamic environment.

scholarly journals Risk Management Maturity Model for Logistic Processes

2021 ◽  
Vol 13 (2) ◽  
pp. 659
Author(s):  
Agnieszka A. Tubis ◽  
Sylwia Werbińska-Wojciechowska
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Development Strategy ◽  
Assessment Method ◽  
Assessment Process ◽  
Strategy Development ◽  
Maturity Model ◽  
Maturity Level ◽  
Two Stage ◽  
Risk Maturity

Recently, the maturity models for risk management are attracting growing attention. The obtained maturity level defines an assessment of an organization’s management competence. Therefore, as a set of various tools and practices, the maturity model is critical for a company’s overall risk maintenance strategy development and implementation. Thus, the purpose of this article is to present a model for risk management maturity for logistic processes. We investigated the main defined assessment areas for risk maturity model implementation in logistic systems. Based on research findings, we introduced a new risk maturity assessment area based on participation in the supply chain—cooperation at risk. The proposed model constitutes the base for a two-stage assessment method implementation, where the global maturity index is introduced. Finally, we implement the proposed two-stage assessment method to verify the proposed model’s diagnostic function and determine its labor intensity. The study confirmed that the five defined maturity areas (knowledge, risk assessment, process risk management, cooperation at risk, and risk monitoring) provide a complex diagnostic tool for risk maturity level identification and, based on the obtained results, allows to define an appropriate development strategy for a given decision-making environment.

Residual life estimation based on a generalized Wiener degradation process

2014 ◽  
Vol 124 ◽  
pp. 13-23 ◽  
Author(s):  
Xiaolin Wang ◽  
Narayanaswamy Balakrishnan ◽  
Bo Guo
Keyword(s):  
Residual Life ◽  
Degradation Process ◽  
Life Estimation

scholarly journals Bivariate Nonlinear Diffusion Degradation Process Modeling via Copula and MCMC

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Huibing Hao ◽  
Chun Su
Keyword(s):  
Nonlinear Diffusion ◽  
Goodness Of Fit ◽  
Real Data ◽  
Degradation Process ◽  
Copula Function ◽  
Assessment Method ◽  
Unknown Parameters ◽  
Degradation Data ◽  
Proposed Model ◽  
Frank Copula

A novel reliability assessment method for degradation product with two dependent performance characteristics (PCs) is proposed, which is different from existing work that only utilized one dimensional degradation data. In this model, the dependence of two PCs is described by the Frank copula function, and each PC is governed by a random effected nonlinear diffusion process where random effects capture the unit to unit differences. Considering that the model is so complicated and analytically intractable, Markov Chain Monte Carlo (MCMC) method is used to estimate the unknown parameters. A numerical example about LED lamp is given to demonstrate the usefulness and validity of the proposed model and method. Numerical results show that the random effected nonlinear diffusion model is very useful by checking the goodness of fit of the real data, and ignoring the dependence between PCs may result in different reliability conclusion.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

scholarly journals Simple Method of Approximate Calculation of Statically Indeterminate Trusses

2019 ◽  
Vol 16 (06) ◽  
pp. 1840026 ◽  
Author(s):  
Janusz Rębielak
Keyword(s):  
Approximate Calculation ◽  
Computer Software ◽  
Two Stage ◽  
Simple Method ◽  
Two Stages

The paper presents principles of the simple method which makes possible approximate calculations of statically indeterminate truss systems in two stages. The two-stage method applies rules of other methods used for calculations of statically determinate trusses. In each of the two stages, there are considered statically determinate trusses, patterns of which are obtained as results of suitable withdrawing of appropriate members from the pattern of the basic statically indeterminate truss. There are presented results of calculations carried out for two cases of load for selected type of plane truss together with comparison of outcomes obtained by means of using appropriate computer software.

scholarly journals Smes’ Performance Evaluation and Optimization Based on DEA and CFI

2013 ◽  
Vol 4 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Zhao Shi ◽  
Josu Takala ◽  
Matti Muhos ◽  
Jyrki Poikkimaki ◽  
Yang Chen
Keyword(s):  
Performance Assessment ◽  
Performance Optimization ◽  
Dynamic Environment ◽  
Critical Factor ◽  
Assessment Method ◽  
Enterprise Performance ◽  
Optimization Strategy ◽  
Real World Data ◽  
Core Content ◽  
Technological Effectiveness

Abstract It is a core content of enterprise performance research evaluating and comparing enterprise performance in dynamic environment. In allusion to this problem, a variety of enterprise performance assessment methods and indexes systems are proposed. Data envelopment analysis (DEA) is a kind of effective mathematical model which is used for comparing the performance among enterprises or different units inside an enterprise, based on the real-world data. Through comparing the performance, DEA can evaluate the enterprise performance from scale effectiveness and technological effectiveness, and then get the performance optimization goals. Critical Factor Index (CFI) is a new enterprise performance assessment method proposed in recent years. This method, based on the performance perception of business leaders or staffs, evaluates the enterprise performance in different dimensions, and then gets the optimization strategy of enterprise resource allocation to improve integrated enterprise performance. This paper has structured a new evaluation and optimization system for performance of small and medium-sized enterprises (SMEs), which combine properly the DEA and CFI method to evaluate and optimize the SMEs’ performance comprehensively, and has confirm this system with data of 5 Finnish SMEs.

Markovian Analysis of Aging Distributed Systems Remaining Life

2014 ◽  
Author(s):  
Anna Bushinskaya ◽  
Sviatoslav Timashev
Keyword(s):  
Distributed Systems ◽  
Markov Process ◽  
Residual Life ◽  
Limit State ◽  
Real Life ◽  
Random Variable ◽  
Operating Pressure ◽  
Remaining Life ◽  
Failure Pressure ◽  
The Moment

Correct assessment of the remaining life of distributed systems such as pipeline systems (PS) with defects plays a crucial role in solving the problem of their integrity. Authors propose a methodology which allows estimating the random residual time (remaining life) of transition of a PS from its current state to a critical or limit state, based on available information on the sizes of the set of growing defects found during an in line inspection (ILI), followed by verification or direct assessment. PS with many actively growing defects is a physical distributed system, which transits from one physical state to another. This transition finally leads to failure of its components, each component being a defect. Such process can be described by a Markov process. The degradation of the PS (measured as monotonous deterioration of its failure pressure Pf (t)) is considered as a non-homogeneous pure death Markov process (NPDMP) of the continuous time and discrete states type. Failure pressure is calculated using one of the internationally recognized pipeline design codes: B13G, B31Gmod, DNV, Battelle and Shell-92. The NPDMP is described by a system of non-homogeneous differential equations, which allows calculating the probability of defects failure pressure being in each of its possible states. On the basis of these probabilities the gamma-percent residual life of defects is calculated. In other words, the moment of time tγ is calculated, which is a random variable, when the failure pressure of pipeline defect Pf (tγ) > Pop, with probability γ, where Pop is the operating pressure. The developed methodology was successfully applied to a real life case, which is presented and discussed.

scholarly journals Study on Life Model of MOV Based on Multiple Parameters and Surge History

2021 ◽  
Author(s):  
RUAN Xiaofei ◽  
Shaoyun JIN ◽  
WEN Weigang ◽  
CHENG Weidong
Keyword(s):  
Life Prediction ◽  
Residual Life ◽  
Cumulative Effect ◽  
Degradation Process ◽  
Life Assessment ◽  
Lightning Protection ◽  
Multiple Parameters ◽  
History Information ◽  
Life Model ◽  
Metal Oxide Varistor

Abstract With the advance of intelligent operation and maintenance in china railways, the requirement of condition monitoring and remaining life prediction for lightning protection equipment has become increasingly urgent. MOV(Metal Oxide Varistor) is the key component of railway surge protector, and it is necessary to study the description model of its degradation process. The output of the model that uses a single parameter to characterize degradation is more prone to contingency, and cannot truly and fully reflect the life state of the MOV. The degradation of MOV is a cumulative effect, and its life model should consider the surge history information. In view of the above problems, a prediction model of the residual life value of MOV is given by combining various degradation related parameters and surge history. Firstly, nine degradation related parameters are fused to construct degradation core. Then, the degradation core and surge history are fused through Markov chain to build a life model of MOV. Then, the model is calibrated with experimental data. Finally, the model is validated and analyzed by experiments. The model can describe the degradation process of MOV more comprehensively and accurately, and can predict the residual life value at the same time, and it has potential application in the life assessment of surge protective devices.

A two-stage method for real-time baseline drift compensation in gas sensors

2022 ◽  
Author(s):  
Chao Zhang ◽  
Wen Wang ◽  
Pan Yong ◽  
Lina Cheng ◽  
Shoupei Zhai ◽  
...  
Keyword(s):  
Real Time ◽  
Gas Sensors ◽  
Piecewise Linear ◽  
Low Cost ◽  
Piecewise Linear Approximation ◽  
Two Stage ◽  
Baseline Drift ◽  
Ambient Temperatures ◽  
Future Design ◽  
Drift Compensation

Abstract Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.

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