Constant Failure Rate Model Research Articles

Structural reliability-based assessment of Nigerian Anogeissus schimperi timber bridge beam in shear and bearing forces

This research investigates the reliability of Anogeissus schimperi timber specie grown in North Western Nigeria as a bridge beam in shear and bearing forces. Specimens for laboratory tests were prepared using the timber specie in accordance with BS 373 (1957). Tests were carried out to determine the physical and mechanical properties at 12% moisture content in line with BS 5268 (2002). Statistical analysis was carried out using strength properties obtained and the specie was classified to strength class D60, confirmed to be Hardwood. Anogeissus schimperi timber bridge beam was designed in accordance to BS5268 (2002), using deterministicapproach. While, reliability analysis to confirm the safety level of the timber bridge beam designed was carried out using constant failure rate model in accordance with Jimoh, (2018). Sensitivity analysis to ascertain the safety margin of a simply supported timber bridge beam subjected to Shear and bearing by varying the span, depth, width and live load was carried out. Results of reliability analysis showed that Anogeissus schimperi met the minimum reliability index of 0.5 under ultimate state of loading in Shear and bearing. Safety index was found to be directly proportional to the depth and width but inversely proportional to the span and live loadof the timber bridge beam during Sensitivity Analysis. The result confirmed that Anogeissus schimperi specie from north western Nigeria at 400mm depth, 150mm breadth and 5000mm span under ultimate limit state loading in Shear and bearing can be used as a reliable timber bridge beam material.
 Keywords: Bridge Beam, Nigerian Anogeissus schimperi Reliability, Structural Material, Timber, Ultimate Limit State.

Mission Profile Based System-Level Reliability Analysis of DC/DC Converters for a Backup Power Application

Reliability analysis is an important tool for assisting the design phase of a power electronic converter to fulfill its life-cycle specifications. Existing converter-level reliability analysis methods have two major limitations: 1) being based on constant failure rate models; and 2) lack of consideration of long-term operation conditions (i.e., mission profile). Although various studies have been presented on power electronic component-level lifetime prediction based on wear-out failure mechanisms and mission profile, it is still a challenge to apply the same method to the reliability analysis of converters with multiple components. Component lifetime prediction based on associated models provides only a B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> lifetime information (i.e., the time when X% items fail), but the time-dependent reliability curve is still not available. In this paper, a converter-level reliability analysis approach is proposed based on time-dependent failure rate models and long-term mission profiles. Two different methods to obtain the component-level time-to-failure are illustrated by a case study of dc/dc converters for a 5 kW fuel cell-based backup power system. The reliability analysis of the converters with and without redundancy is also performed to assist the decision making in the design phase of the fuel cell power conditioning stage.

Modelling weather dependence in online reliability assessment of power systems

Probabilistic reliability assessment of power systems is an ongoing field of research, particularly in the development of tools to model the probability of exogenous threats and their potential consequences. This paper describes the application of a weather-dependent failure rate model to a region of the Icelandic transmission system, using 10 years of weather data and overhead line fault records. The studied failure rate model is compared with a constant failure rate model, in terms of variability and how well the models perform in a blind test over a 2 year period in reflecting the occurrence of outages. The weather-dependent and constant failure rate models are used as input to a state-of-the-art risk assessment tool to determine the sensitivity of such software to weather-dependent threats. The results show the importance of weather-dependent contingency probabilities in risk estimation, and in quantitative assessment of maintenance activities. The results also demonstrate that inclusion of weather dependence in power system reliability assessments affects the overall distribution of risk as a positively skewed distribution, with high-risk periods occurring at low frequency.

Is high-altitude mountaineering Russian roulette?

AbstractWhether the nature of the risks associated with climbing high-altitude (8000 m) peaks is in some sense “controllable” is a longstanding debate in the mountaineering community. Well-known mountaineers David Roberts and Ed Viesturs explore this issue in their recent memoirs. Roberts views the primary risks as “objective” or uncontrollable, whereas Viesturs maintains that experience and attention to safety can make a significant difference. This study sheds light on the Roberts-Viesturs debate using a comprehensive dataset of climbing on Nepalese Himalayan peaks. To test whether the data is consistent with a constant failure rate model (Roberts) or a decreasing failure rate model (Viesturs), it draws on Total Time on Test (TTT) plots from the reliability engineering literature and applies graphical inference techniques to them.

A Hybrid Conditions-Dependent Outage Model of a Transformer in Reliability Evaluation

Constant failure rate model used in the conventional reliability assessment of power systems cannot reflect the impacts of the various operating conditions such as transformer loading, ambient temperature, weather on component reliability. A hybrid conditions-dependent outage model (CDOM) of a transformer is proposed in this paper to include those impacts. The CDOM is the combination of three failure models: the aging failures due to the loss of mechanical strength of conductor insulation; the random failures considering weather conditions, and the outages caused by the direct trips of the overload protections. The component reliability using the proposed model has been tested under different operating conditions. The model is also applied in power system operational reliability assessment. The reliability indices using CDOM are compared with that using the condition-independent outage model. The reliability indices using the CDOMs of transformers can provide useful information for operators to understand possible system and component failure risk in real-time operation and to make important alleviation decisions.

Commentary - caution: constant failure-rate models may be hazardous to your design

This commentary examines the consequences, from a system perspective, of modeling system components using only their failure-rates, viz, the inverse of their MTTF, when, in actuality, they have a lifetime distribution with either an increasing or a decreasing hazard function. Such models are often used because: they are tractable; they are thought to be "robust;" they depend only on average values; or only small amounts of data are available for calculations. However, the results of such models can greatly understate the system-reliability for some time periods and overstate it for others. Using MTTF as a figure of merit for system-reliability can be especially misleading. Furthermore, the redundancy in a redundant system might provide very little of the reliability improvement predicted by the constant failure-rate model, and series systems might, in fact, be much more reliable than predicted. The overall result is that a constant failure-rate model can give very misleading guidance for system-design.

CSISZAR DIVERGENCE FROM CONSTANT FAILURE RATE MODEL FOR GROUPED DATA

We propose a measure of divergence in failure rates of a system from the constant failure rate model for a grouped data situation. We use this measure to compare the divergences of several systems from the constant failure rate model and find the asymptotic distributions of the test statistics. Several applications are discussed to illustrate the procedure. In the context of testing the goodness-of-fit with the constant failure rate model, we conduct a simulation study which shows that this procedure compares favorably with the Pearson chi-square test and the likelihood ratio test procedures.

Measures of Departure from Constant Failure Rate Models and Proportional Hazards Rate Models for Grouped Data

Two measures are proposed to represent the degree of departure from the constant failure rate model of a system when data are grouped. Two measures are also proposed to represent the degree of departure from the proportional hazards rate model when two systems are present and grouped data are considered. In each case one measure is based on the Kullback-Leibler discrepancy and the other is based on the Pearson X 2 type discrepancy using the failure rates. The usefulness of the proposed measures are discussed with applications. A simulation study shows that the proposed measures perform no worse than the goodness-of-fit tests when testing for the constant failure rate model.

Measures of Departure from Constant Failure Rate Models and Proportional Hazards Rate Models for Grouped Data

Measures of Departure from Constant Failure Rate Models and Proportional Hazards Rate Models for Grouped Data

On Discrete Failure Models

In some situations, discrete failure ``time'' distributions are appropriate to model ``lifetimes''. For example, a discrete distribution is appropriate when a piece of equipment operates in cycles and the number of cycles prior to failure is observed. This paper provides three families of discrete parametric distributions which are versatile in fitting increasing, decreasing, and constant failure rate models to either uncensored or right-censored discrete life-test data. The maximum likelihood estimation of parameters, survival probabilities, and mean lifetime is investigated. The MLEs can be computed by simple numerical methods.

Effects of Population Domain Parameters on Statistical Life Tests

A statistical life test plan measuring compliance to a reliability standard often assumes a constant failure rate model on the premises that only time domain parameters are important and that both producer and consumer enter relatively constant risk plans. The authors contend that population domain parameters can introduce significant variations in both producer and consumer risks. In particular, they solve the variations of these risks as a function of sample size for the popular AGREE life test plan.