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Related Experiment Video

Updated: Oct 15, 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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Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete

Yuequn Zhang1, Lei Luo1, Xu Ji1

  • 1Department of Chemical Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|October 26, 2021
PubMed
Summary

This study introduces a novel fault detection and diagnosis method, DPRF, which accurately handles missing data using random forest decision paths and correction coefficients for improved industrial process monitoring.

Keywords:
decision pathfault diagnosisincomplete datarandom forestreliability scores

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Area of Science:

  • Chemical Engineering
  • Data Science
  • Industrial Process Control

Background:

  • Data-driven fault detection and diagnosis (FDD) is crucial for industrial processes.
  • Existing FDD methods struggle with accuracy when data is missing.
  • Big data analytics has increased the need for robust FDD techniques.

Purpose of the Study:

  • To propose an improved random forest (RF) based FDD method, DPRF, that effectively compensates for incomplete data.
  • To enhance the reliability of FDD in the presence of missing sensor readings.
  • To demonstrate the superiority of DPRF over existing methods in handling data anomalies.

Main Methods:

  • Developed a Decision Path Random Forest (DPRF) model incorporating correction coefficients.
  • Utilized intact training samples to build decision trees within the RF.
  • Inferred sample reliability scores from decision paths and node importance for each tree.
  • Implemented a majority voting system combining predictions and reliability scores.

Main Results:

  • The DPRF model demonstrated superior performance in fault detection and diagnosis with incomplete data.
  • Tested on the Tennessee Eastman (TE) process, DPRF showed enhanced accuracy compared to other FDD methods.
  • Correction coefficients effectively compensated for the influence of missing data points.

Conclusions:

  • The proposed DPRF method offers a robust and accurate solution for FDD with missing data.
  • DPRF significantly improves the reliability of fault diagnosis in industrial big data scenarios.
  • This approach provides a valuable tool for maintaining operational integrity in complex industrial systems.