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Related Concept Videos

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...

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Related Experiment Videos

Mission availability for bounded-cumulative-downtime system.

Yu Zhou1, Gang Kou, Daji Ergu

  • 1School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China.

Plos One
|July 12, 2013
PubMed
Summary
This summary is machine-generated.

A new mathematical model quantifies mission availability for systems with bounded cumulative downtime. This model accurately predicts system reliability under repair constraints, ensuring operational continuity.

Related Experiment Videos

Area of Science:

  • Reliability Engineering
  • Mathematical Modeling

Background:

  • Assessing mission availability is crucial for systems with cumulative downtime constraints.
  • Existing models often struggle with complex downtime scenarios and repair probabilities.

Purpose of the Study:

  • To propose a novel mathematical model for mission availability in bounded-cumulative-downtime systems.
  • To develop a closed-form expression for mission availability considering simultaneous uptime and downtime constraints.

Main Methods:

  • Formulation of a mathematical model incorporating cumulative downtime and uptime as constraints.
  • Definition of mission availability as the probability of not exceeding downtime limits before accrued uptime.
  • Modeling cumulative downtime as a random variable with a cumulative distribution function.

Main Results:

  • A closed-form expression for mission availability was derived.
  • Numerical simulations validated the model's effectiveness and accuracy.
  • Acceptable relative errors were observed, confirming model performance.

Conclusions:

  • The proposed model effectively calculates mission availability for bounded-cumulative-downtime systems.
  • The model provides a reliable tool for predicting system performance under operational constraints.
  • The study highlights significant applications of the developed mission availability model.