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相关概念视频

Hazard Rate01:11

Hazard Rate

102
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...
102
Fatigue01:21

Fatigue

180
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
180
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.6K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

411
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
411
Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

Updated: Jun 23, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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基于风险的故障检测使用基于故障模式和效果分析的贝叶斯网络.

Bálint Levente Tarcsay1, Ágnes Bárkányi1, Sándor Németh1

  • 1Department of Process Engineering, University of Pannonia, 8200 Veszprém, Hungary.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种混合故障检测 (FD) 方法,使用动态主要组件分析 (DPCA) 和故障模式和效应分析 (FMEA) 的贝叶斯网络 (BNs). 这种方法通过评估工艺故障风险来提高工业安全,以尽量减少错误报警.

关键词:
贝叶斯网络是贝叶斯网络.在 DPCA 找 DPCA美国联邦航空和航空管理局 (FMEA) 的FMEAEAEA.动态风险评估 动态风险评估检测故障的检测故障检测.

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 过程安全 过程安全 过程安全
  • 工业监控 工业监控 工业监控

背景情况:

  • 工业故障检测 (FD) 方法往往缺乏对过程风险评估的强有力的整合.
  • 尽量减少报警率需要区分安全关键和非安全关键的过程异常.
  • 现有的FD技术为整合动态风险分析提供了有限的能力.

研究的目的:

  • 引入一种新的基于风险的混合故障检测 (FD) 方法.
  • 将动态主要组件分析 (DPCA) 与基于贝叶斯网络 (BNs) 的故障模式和影响分析 (FMEA) 整合起来.
  • 通过评估过程故障风险和最大限度地降低报警率来提高故障检测的准确性.

主要方法:

  • 开发了一个混合模型,结合了DPCA和FMEA的BN.
  • 利用FMEA构建一个为监督过程的BN.
  • 雇佣DPCA分析过程数据并估计修改后的风险优先级号码 (RPN).

主要成果:

  • 拟议的混合方法有效地估计了不同过程状态的修改RPN.
  • 通过结合BN和DPCA结果,成功地区分了过程异常.
  • 在工业基准和液态有机载 (LOHC) 反应堆模型上证明了该方法的有效性.

结论:

  • 混合DPCA-FMEA-BN方法为基于风险的故障检测提供了一个强大的框架.
  • 这种方法通过准确识别关键过程故障来提高工业安全.
  • 该技术适用于复杂的工业过程,包括像LOHC这样的新兴技术.