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Related Concept Videos

Control Systems01:10

Control Systems

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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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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...
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Transient and Steady-state Response01:24

Transient and Steady-state Response

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In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
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Random and Systematic Errors01:20

Random and Systematic Errors

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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...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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In Situ Soil Moisture Sensors in Undisturbed Soils
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A Risk Evaluation Framework in System Control Subject to Sensor Degradation and Failure.

Tangxiao Yuan1,2, Weilin Xu1, Kondo Hloindo Adjallah2

  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)
|March 13, 2024
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Summary

Sensor failures can erode trust in data-driven decisions. This study introduces a risk assessment framework to evaluate and predict decision errors caused by sensor degradation, enhancing system reliability.

Keywords:
black boxdecision-makingframeworkrisk assessmentrisk predictionsensor failure

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Area of Science:

  • Computer Science
  • Engineering
  • Risk Management

Background:

  • Sensor degradation and failure pose significant challenges to the reliability of data-driven decision-making models.
  • Confidence in adopting new decision-making systems, particularly in risk-sensitive applications, is often undermined by sensor unreliability.

Purpose of the Study:

  • To introduce a novel risk assessment framework specifically designed for classification algorithms.
  • To evaluate and quantify decision-making risks stemming from sensor degradation and failures.

Main Methods:

  • The framework involves on-site fault-free and failure data collection, fault data generation, and simulated decision-making processes.
  • It includes steps for risk identification, quantitative risk assessment, and risk prediction.
  • The method was validated using a case study in an access control system.

Main Results:

  • The framework allows users to assess potential decision errors based on current data collection status.
  • It enables the ranking of risk sensitivity to sensor data for optimizing data collection before model adoption.
  • The approach facilitates the prediction of future system risks by considering sensor lifespan, informing maintenance strategies.

Conclusions:

  • The developed risk assessment framework enhances confidence in data-driven decision-making by addressing sensor reliability concerns.
  • It provides a systematic approach to identify, quantify, and predict risks associated with sensor degradation.
  • The findings support proactive sensor maintenance and optimized data collection for improved system performance and safety.