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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

403
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
403
Sampling Plans01:23

Sampling Plans

233
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
233
Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

329
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
329
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

315
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
315
Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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An Adaptive Sampling Framework for Life Cycle Degradation Monitoring.

Yuhua Yin1,2, Zhiliang Liu1, Junhao Zhang3

  • 1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

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|January 21, 2023
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Summary
This summary is machine-generated.

This study introduces an adaptive sampling framework to improve condition monitoring by reducing data redundancy and loss. The new method enhances mechanical degradation tracking compared to existing approaches.

Keywords:
adaptive sampling strategycondition monitoringdata lossdata redundancymechanical degradation monitoring

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Area of Science:

  • Engineering
  • Data Science

Background:

  • Data redundancy and loss are critical challenges in condition monitoring.
  • Existing sampling methods lack adaptability to varying conditions.

Purpose of the Study:

  • To propose an adaptive sampling framework for segment intervals to address data issues in condition monitoring.
  • To enhance the monitoring of mechanical degradation.

Main Methods:

  • Developed an adaptive sampling framework based on improvements to existing methods.
  • Implemented the framework for mechanical degradation monitoring using simulation and real datasets.
  • Visually presented sample distributions using color maps and designed five metrics for assessment.

Main Results:

  • The proposed adaptive sampling method demonstrated superiority over existing techniques.
  • Results showed a strong correlation between sampling effectiveness, data status, and degradation indicators.
  • Objective physical indicators yielded better results than feature indicators.

Conclusions:

  • The adaptive sampling framework offers a novel approach to predictive sampling.
  • Significantly improves the accuracy and reliability of mechanical degradation monitoring.