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Related Concept Videos

Fault Types01:18

Fault Types

147
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
147
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

266
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
266
Bus Impedance Matrix01:24

Bus Impedance Matrix

198
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
198
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

172
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
172
Interpreting Run Charts01:25

Interpreting Run Charts

2.8K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
2.8K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

4.8K
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...
4.8K

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Related Experiment Video

Updated: Oct 10, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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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Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis.

Yuman Yao1, Yiyang Dai2, Wenjia Luo1

  • 1College of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary

Early fault diagnosis for complex batch processes is crucial. This study introduces a trend analysis method that enhances fault information, achieving high diagnostic accuracy and speed in penicillin fermentation.

Keywords:
QTAbatch processesincipient fault detection

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Area of Science:

  • Chemical Engineering
  • Process Control
  • Data Science

Background:

  • Batch processes are economically valuable but complex, leading to high failure rates.
  • Sensor data in batch processing is often noisy and exhibits multistage variations, hindering accurate fault diagnosis.
  • Early fault detection is critical for operational efficiency and safety in these processes.

Purpose of the Study:

  • To develop a robust fault diagnosis method for batch processes that overcomes noise and multistage variations.
  • To enhance the extraction and utilization of fault information from sensor data.
  • To improve the diagnostic performance and efficiency compared to traditional methods.

Main Methods:

  • An adaptive standardization method based on a time window was developed.
  • Quadratic fitting was employed to extract data trends within the time window.
  • A novel trend recognition method utilizing Euclidean distance calculations was created.

Main Results:

  • The proposed method was validated using penicillin fermentation data.
  • Two test datasets (existing and unknown batches) were used, achieving average diagnostic rates of 100% and 87.5%, respectively.
  • The mean diagnosis time was consistently low at 0.2083 hours.

Conclusions:

  • The developed fault diagnosis algorithm demonstrates superior fault diagnosis and feature extraction capabilities.
  • The method effectively handles noisy and complex data typical of batch processes.
  • This approach offers a significant improvement for early fault detection in industrial batch operations.