scholarly journals Reliability modeling for dependent competing failure processes with phase-type distribution considering changing degradation rate

2021 ◽  
Vol 23 (4) ◽  
pp. 627-635
Author(s):  
Hao Lyu ◽  
Shuai Wang ◽  
Xiaowen Zhang ◽  
Zaiyou Yang ◽  
Michael Pecht
Keyword(s):  
Degradation Rate ◽  
Failure Process ◽  
Survival Function ◽  
Interarrival Time ◽  
Reliability Model ◽  
Shock Model ◽  
Ph Distribution ◽  
Soft Failure ◽  
Phase Type ◽  
Hard Failure

In this paper, a system reliability model subject to Dependent Competing Failure Processes (DCFP) with phase-type (PH) distribution considering changing degradation rate is proposed. When the sum of continuous degradation and sudden degradation exceeds the soft failure threshold, soft failure occurs. The interarrival time between two successive shocks and total number of shocks before hard failure occurring follow the continuous PH distribution and discrete PH distribution, respectively. The hard failure reliability is calculated using the PH distribution survival function. Due to the shock on soft failure process, the degradation rate of soft failure will increase. When the number of shocks reaches a specific value, degradation rate changes. The hard failure is calculated by the extreme shock model, cumulative shock model, and run shock model, respectively. The closed-form reliability function is derived combining with the hard and soft failure reliability model. Finally, a Micro-Electro-Mechanical System (MEMS) demonstrates the effectiveness of the proposed model.

scholarly journals Reliability modeling for competing failure systems with instant-shift hard failure threshold

2018 ◽  
Vol 42 (4) ◽  
pp. 457-467 ◽  
Author(s):  
Jingyi Liu ◽  
Yugang Zhang ◽  
Bifeng Song
Keyword(s):  
Sensitivity Analysis ◽  
System Reliability ◽  
Reliability Model ◽  
External Shocks ◽  
Reliability Modeling ◽  
Soft Failure ◽  
Random Shock ◽  
Hard Failure ◽  
Engineering Practices ◽  
Random Shocks

Many researchers have modeled systems under multiple dependent competing failure processes (MDCFP) in recent years. Typically, those failure processes consist of degradation (soft failure) and random shock (hard failure). In previous papers the threshold of hard failure has been a fixed value, which does not reflect engineering practices. Threshold refers to the ability to resist external random shocks, which shifts with time as the system is used. Thus, this paper establishes a model for MDCFP with instant-shift hard threshold. The hard failure threshold changes with time instantaneously, and it is also influenced by external shocks. This paper also presents a system reliability model. The effectiveness of the presented model is demonstrated by a reliability analysis of the micro-engine at Sandia National Laboratories. In addition, a sensitivity analysis is performed for specific parameters.

scholarly journals Reliability Evaluation of Multiple DCFP System subject to Shifting Threshold

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chunping Li ◽  
Huibing Hao ◽  
Fang Xu ◽  
Guotao Zhao
Keyword(s):  
Wiener Process ◽  
System Reliability ◽  
Failure Process ◽  
Reliability Evaluation ◽  
Probabilistic Methods ◽  
Reliability Models ◽  
Soft Failure ◽  
Evaluation Models ◽  
Hard Failure ◽  
Random Shocks

This paper focuses on system reliability analysis with dependent competing failure process due to soft failure and hard failure. Some new probabilistic methods based on cumulative shock model and nonlinear Wiener process under different shifting thresholds situation are obtained. Considering that nonlinearity exists extensively in practice, the continuous soft failure process is governed by random effected nonlinear Wiener process. Firstly, reliability evaluation models for hard failure and soft failure are obtained under the cumulative shock, respectively. Furthermore, some system reliability models under different shifting thresholds situation are studied, in which failure threshold will decrease after a certain number of shocks. A real numerical example about fatigue crack growth dataset is carried out to demonstrate the proposed procedure. Numerical results indicate that both random shocks and shifting threshold have significant effect on system reliability. Finally, some sensitivity analysis are also been given.

scholarly journals A Parameter Estimation Method of Shock Model Constructed with Phase-Type Distribution on the Condition of Interval Data

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Long Wang ◽  
Yue Li ◽  
Yanling Qian ◽  
Xu Luo
Keyword(s):  
Parameter Estimation ◽  
Likelihood Function ◽  
Estimation Method ◽  
Interval Data ◽  
Shock Model ◽  
Phase Type Distribution ◽  
Type Distribution ◽  
Ph Distribution ◽  
Parameter Estimation Method ◽  
Phase Type

The phase-type distribution (also known as PH distribution) has mathematical properties of denseness and closure in calculation and is, therefore, widely used in shock model constructions describing occurrence time of a shock or its damage. However, in the case of samples with only interval data, modeling with PH distribution will cause decoupling issues in parameter estimation. Aiming at this problem, an approximate parameter estimation method based on building PH distribution with dynamic order is proposed. Firstly, the shock model established by PH distribution and the likelihood function under samples with only interval data are briefly introduced. Then, the principle and steps of the method are introduced in detail, and the derivation processes of some related formulas are also given. Finally, the performance of the algorithm is illustrated by a case with three different types of distributions.

Reliability modeling and analysis for systems governed by multiple competing failures processes

2020 ◽  
pp. 1748006X2097447
Author(s):  
Hongda Gao ◽  
Dejing Kong ◽  
Yixin Sun
Keyword(s):  
Process Model ◽  
Failure Modes ◽  
Failure Process ◽  
Extended Model ◽  
Reliability Modeling ◽  
Modeling And Analysis ◽  
Soft Failure ◽  
Shock Process ◽  
Hard Failure ◽  
Reliability Systems

Due to that the operating environment is becoming more and more complex and rigorous, the multiple competing failure modes for the reliability system is much commonly seen. In order to improve the system performance, a sensor-based degradation calibration policy (SBDC policy) is presented in this paper. The model considers the competing failure process which is described by the soft and hard failure modes. In detail, the soft failures occur when the degradation of the system exceeds the failure threshold, and the hard failures are caused by the same shock process. We use the Wiener process model to describe the soft failure and the shock process to describe the catastrophic failure. Meanwhile, in the shock process, the damage associated with the system is normal distributed which is related to the duration of the adjacent shocks. This extended model with calibrations has a good application value for the corresponding complex reliability systems which are subject to the dependent competing failure modes. By the model in this article, the system reliability and safety can be improved and the risk of the abrupt damage shall be reduced as the circumstance changes.

Reliability analysis for systems subject to mutually dependent degradation and shock processes

2021 ◽  
pp. 1748006X2110114
Author(s):  
Lina Bian ◽  
Guanjun Wang ◽  
Fengjun Duan
Keyword(s):  
Failure Modes ◽  
Failure Process ◽  
Analytical Techniques ◽  
Reliability Function ◽  
External Shocks ◽  
Reliability Indices ◽  
Soft Failure ◽  
Proposed Model ◽  
Mean Lifetime ◽  
Hard Failure

This paper studies the reliability problem for systems subject to two types of dependent competing failure processes, that is, soft failure and hard failure processes. A soft failure happens when the total degradation of the system exceeds a given critical level, while a hard failure occurs when the accumulative shock load caused by shocks surpasses the hard failure threshold. These two failure processes are mutually dependent due to the fact that external shocks will bring sudden increments in the degradation of the system, and the total amount of degradation will decrease the hard failure threshold of the system. The system fails whenever either of these two failure modes happens. Assuming that the arrival of shocks follows a Poisson process, the reliability function of the system under cumulative shock model is derived by using some analytical techniques. Some important reliability indices, including the mean lifetime of the system, the expected number of shocks until system failure, the probabilities of soft and hard failures, are calculated explicitly. Moreover, a special case that the hard failure process and soft failure process are mutually independent is also discussed. Monte Carlo method is employed to calculate the multiple integrals existing in the expressions of reliability function and reliability indices. A numerical example of the Reinforced Concrete pier columns on sea bridge is presented to illustrate the proposed model.

scholarly journals Reliability Modeling for Humidity Sensors Subject to Multiple Dependent Competing Failure Processes with Self-Recovery

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2714 ◽  
Author(s):  
Jia Qi ◽  
Zhen Zhou ◽  
Chenchen Niu ◽  
Chunyu Wang ◽  
Juan Wu
Keyword(s):  
Reliability Model ◽  
Humidity Sensors ◽  
Reliability Modeling ◽  
Reliability Models ◽  
Soft Failure ◽  
Recent Developments ◽  
General Reliability ◽  
Hard Failure ◽  
Mechanism Of Failure ◽  
Random Shocks

Recent developments in humidity sensors have heightened the need for reliability. Seeing as many products such as humidity sensors experience multiple dependent competing failure processes (MDCFPs) with self-recovery, this paper proposes a new general reliability model. Previous research into MDCFPs has primarily focused on the processes of degradation and random shocks, which are appropriate for most products. However, the existing reliability models for MDCFPs cannot fully characterize the failure processes of products such as humidity sensors with significant self-recovery, leading to an underestimation of reliability. In this paper, the effect of self-recovery on degradation was analyzed using a conditional probability. A reliability model for soft failure with self-recovery was obtained. Then, combined with the model of hard failure due to random shocks, a general reliability model with self-recovery was established. Finally, reliability tests of the humidity sensors were presented to verify the proposed reliability model. Reliability modeling for products subject to MDCFPs with considering self-recovery can provide a better understanding of the mechanism of failure and offer an alternative method to predict the reliability of products.

scholarly journals Mathematical Modeling of Survivability Function for Thermoelectric Module

2021 ◽  
Vol 2056 (1) ◽  
pp. 012028
Author(s):  
Sh Sattar ◽  
A Osipkov ◽  
V V Belyaev
Keyword(s):  
Mechanical Stress ◽  
Residual Life ◽  
Survival Function ◽  
Thermoelectric Module ◽  
High Ratio ◽  
Distribution Model ◽  
Mean Residual Life ◽  
Reliability Model ◽  
Strength Based ◽  
Selection Of

Abstract Developing an optimized reliability model for thermoelectric module at the stress where the probability of module to functions without abruptive failure is a challenging aspect. One of the major reasons is the mismatch of thermal expansion coefficient, which has severe effects on segmented moduli compared to unsegmented moduli. The likelihood of a thermoelectric module to survive at certain level of thermo-mechanical stresses varies by varying number of component (layers) in thermoelectric leg. On another hand, selection of an adequate distribution model to predict reliability and sustainability of the thermoelectric module requires development of new optimized stress-strength-based model. In this paper the predictive reliability model for high temperature segmented module is derived from parametric Lognormal mean residual life and nonparametric Lognormal-kernel survival function to measure probability of module to survive at certain thermo-mechanical stress. A comprehensive comparative discussion has been done to illustrate the maximum likelihood based on Bayesian nonparametric lognormal-Kernel inference method regarding to Monte Carlo simulation, Weibull’s distribution, and Lognormal mean residual life for various shapes for the survival function. It has been demonstrated that nonparametric lognormal-kernel survival function has high ratio of probability to predict the survival of module at higher discrete thermo-mechanical stress data.

Shocks, runs and random sums

2001 ◽  
Vol 38 (02) ◽  
pp. 438-448 ◽  
Author(s):  
F. Mallor ◽  
E. Omey
Keyword(s):  
Distribution Function ◽  
Critical Level ◽  
Random Variables ◽  
Reliability Model ◽  
Shock Model ◽  
Random Sums ◽  
Critical Set ◽  
Limit Behaviour

In this paper we study random variables related to a shock reliability model. Our models can be used to study systems that fail when k consecutive shocks with critical magnitude (e.g. above or below a certain critical level) occur. We obtain properties of the distribution function of the random variables involved and we obtain their limit behaviour when k tends to infinity or when the probability of entering a critical set tends to zero. This model generalises the Poisson shock model.

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