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Related Concept Videos

Fault Types01:18

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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.
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Related Experiment Video

Updated: Dec 20, 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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Incipient fault detection for nonlinear processes based on dynamic multi-block probability related kernel principal

Peipei Cai1, Xiaogang Deng1

  • 1College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China.

ISA Transactions
|May 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new multi-block probability related kernel principal component analysis (KPCA) method for detecting faults in nonlinear industrial processes. The enhanced method improves fault detection by considering dynamic and local statistical changes.

Keywords:
Incipient fault detectionKernel principal component analysisKullback Leibler divergenceNonlinear process

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Area of Science:

  • Industrial Process Monitoring
  • Nonlinear System Analysis
  • Statistical Process Control

Background:

  • Effective detection of incipient faults in nonlinear industrial processes is crucial for operational efficiency and safety.
  • Traditional methods like kernel principal component analysis (KPCA) may not fully capture dynamic or localized changes in process data.
  • Existing statistical monitoring frameworks can overlook subtle probability distribution shifts indicative of early-stage faults.

Purpose of the Study:

  • To propose an enhanced kernel principal component analysis (KPCA) method, termed the multi-block probability related KPCA (DMPRKPCA) method.
  • To develop a robust nonlinear statistical monitoring framework for early fault detection in industrial processes.
  • To improve upon existing methods by incorporating dynamic characteristics and multi-block modeling for enhanced sensitivity.

Main Methods:

  • Constructed a probability-related nonlinear statistical monitoring framework by integrating KPCA with Kullback-Leibler divergence (KLD) to measure probability distribution changes.
  • Designed a dynamic KLD component using an exponentially weighted moving average (EWMA) approach to capture temporal data variations within a moving window.
  • Implemented a multi-block modeling strategy, dividing KLD components into sub-blocks for mean and variance information, to prevent submergence of local statistical changes.

Main Results:

  • The proposed DMPRKPCA method demonstrated superior performance in detecting incipient faults compared to conventional KPCA.
  • Case studies on a numerical system and a simulated chemical reactor validated the effectiveness of the enhanced method.
  • The multi-block strategy successfully highlighted local statistical changes that might be missed by holistic approaches.

Conclusions:

  • The DMPRKPCA method offers a significant advancement in the effective detection of incipient faults in nonlinear industrial processes.
  • The integration of dynamic KLD and multi-block modeling enhances the sensitivity and reliability of fault detection systems.
  • This approach provides a valuable tool for improving the safety and efficiency of industrial operations through advanced process monitoring.