Reliability Analysis for Multi-Component Systems Subject to Multiple Dependent Competing Failure Processes

Song, Shousen; Coit, David W.; Feng, Qianmei; Peng, Hao

doi:10.1109/tr.2014.2299693

Cited by 216 publications

(97 citation statements)

References 30 publications

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“…Models for failures are widely studied in computer-communication, e.g., finite state Markov process [16] and reliability of other multi-component systems [17] [18]. These models are stochastic but assume stationary properties or distributions.…”

Section: Introductionmentioning

confidence: 99%

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution

Wei¹,

Ji²,

Galvan³

et al. 2016

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This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little's Law then provides a simple approximation of the entire life cycle of failure and recovery through a ( ) ( ) GI t G t / /∞ queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.

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Section: Introductionmentioning

confidence: 99%

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution

Wei¹,

Ji²,

Galvan³

et al. 2016

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show abstract

“…In addition, many authors make a lot of researches about reliability modeling for competing failure mode by jointly considering the degradation process and associated catastrophic failure. [8][9][10] Wang and Coit 11 made the research about the degradation model with random or uncertain failure threshold.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling of multiple performance based on degradation values distribution

et al. 2016

Advances in Mechanical Engineering

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In practical engineering, reliability analysis of products plays an important role in assessing the corresponding remaining life. Products often have more than one failure mechanism, which means reliability should be evaluated by two or more performance characteristics. To resolve the above problem, based on the degradation values distribution method utilization, copula functions as the link functions of their marginal distributions are introduced in this study. The performance characteristics are also considered the dependent as well as independent to make a comparison. Moreover, a framework for reliability assessment of products with the multiple performance degradation is developed. Finally, the proposed methodology and model is validated effectively by the fatigue cracks data from the literature. From the results, we can see that the proposed model is more accurate.

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“…For example, the vibration-based degradation signals of bearings [8] and vibration signals special frequency band energy of gearboxes [14] exhibit two-stage characteristic in degradation test. There are two typical models with independent random increment, continuous time model [7] and cumulative damage model [21], that can be used to present system degradation process. Some researchers have studied on the degradation process modeling for two-stage degradation system.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear degradation modeling and maintenance policy for a two-stage degradation system based on cumulative damage model

Ni¹,

Zhao²,

Song³

et al. 2016

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Reliability Analysis for Multi-Component Systems Subject to Multiple Dependent Competing Failure Processes

Cited by 216 publications

References 30 publications

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution

Reliability modeling of multiple performance based on degradation values distribution

Nonlinear degradation modeling and maintenance policy for a two-stage degradation system based on cumulative damage model

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