Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Identifying the extra nanoparticles from SCR-equipped diesel engines and reducing them via regulating surface tension of aqueous urea solution.

Journal of hazardous materials·2026
Same journal

A spectral-decomposition aging index for microplastics in aquatic environments.

Journal of hazardous materials·2026
Same journal

To what extent do spheroidal carbonaceous particles (SCPs) indicate anthropogenic activities in coastal environments?

Journal of hazardous materials·2026
Same journal

Measurement of tyre-related chemicals in roadside retention ponds using the Chemcatcher passive sampler: An ex-situ calibration and in-situ monitoring study.

Journal of hazardous materials·2026
Same journal

Endogenous reactive iron sulfide triggers oxidative toxicity and activity decay in intermittently starved anammox consortia.

Journal of hazardous materials·2026
Same journal

YKT6 suppression contributes to 6PPD-induced cardiotoxicity by impairing autophagosome-lysosome fusion in zebrafish.

Journal of hazardous materials·2026

Related Experiment Videos

Equipment failure rate updating-Bayesian estimation.

Ahmad Shafaghi1

  • 1ABS Consulting, 16855 Northchase Drive, Houston, TX 77060, United States. ashafaghi@absconsulting.com

Journal of Hazardous Materials
|February 26, 2008
PubMed
Summary

This study introduces a Bayesian method to improve equipment failure rate estimates by combining generic data with site-specific evidence. The approach enhances accuracy, especially when data is limited, by using constrained non-informative priors.

Related Experiment Videos

Area of Science:

  • Reliability Engineering
  • Statistical Modeling
  • Risk Assessment

Background:

  • Generic equipment failure data often lacks site-specific accuracy.
  • Uncertainty in generic data and limited prior information can compromise failure rate estimations.
  • Accurate failure rate prediction is crucial for effective maintenance and safety.

Purpose of the Study:

  • To present a Bayesian method for augmenting generic equipment failure data with prior evidence.
  • To demonstrate how site-specific data refines failure rate estimations.
  • To introduce "constrained non-informative priors" for scenarios with limited data.

Main Methods:

  • Bayesian inference to combine prior distributions with likelihood functions.
  • Utilizing plant-specific evidence to form the prior distribution.
  • Application of Poisson likelihood with conjugate gamma distribution for time-based failure rates.
  • Adaptation for demand failure rates using binomial likelihood and conjugate beta distribution.

Main Results:

  • The posterior distribution effectively incorporates site-specific evidence, improving failure rate estimates.
  • Constrained non-informative priors maintain the mean failure rate while accommodating data uncertainty.
  • The methodology is adaptable for both time-based and demand-based failure rate calculations.

Conclusions:

  • Bayesian augmentation offers a robust approach to enhance equipment failure data.
  • The proposed method improves the reliability of failure rate predictions in diverse data conditions.
  • This technique provides a valuable tool for risk assessment and maintenance optimization.