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Related Experiment Video

Updated: Jun 12, 2026

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

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

Published on: October 28, 2022

Multiple sensor fault diagnosis for dynamic processes.

Cheng-Chih Li¹, Jyh-Cheng Jeng

¹Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan.

ISA Transactions

|June 15, 2010

Summary

This summary is machine-generated.

This study introduces a new method for sensor fault diagnosis in industrial plants. It effectively detects, isolates, and identifies multiple sensor faults in dynamic systems, enhancing process safety and operations.

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Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

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Published on: October 1, 2007

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Last Updated: Jun 12, 2026

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

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

Published on: October 28, 2022

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Published on: October 1, 2007

Area of Science:

Industrial Automation
Process Control
Fault Diagnosis

Background:

Modern industrial plants rely on numerous sensors for safe and efficient operation.
Sensor failures can compromise process safety and optimal performance.
Accurate sensor fault diagnosis is essential for industrial systems.

Purpose of the Study:

To develop a systematic approach for detecting, isolating, and identifying multiple sensor faults in multivariate dynamic systems.
To enhance the reliability and safety of industrial processes through improved sensor fault diagnosis.

Main Methods:

Definition of deviation vectors for sensor observations.
Derivation of the basic sensor fault matrix (BSFM) using normalized basic fault vectors.
Projection of process deviation vectors onto the BSFM space to obtain weight vectors for fault diagnosis.
Proposal of a novel monitoring index and derivation of sensor fault detectability.

Main Results:

A systematic methodology for multiple sensor fault diagnosis in dynamic systems.
Effective utilization of weight vectors for fault isolation and identification.
Demonstrated effectiveness through simulation examples and comparison with PCA-based methods.

Conclusions:

The proposed approach provides a robust framework for sensor fault diagnosis.
The methodology enhances process safety and operational efficiency in industrial plants.
The technique offers improved isolatability and identifiability of multiple sensor faults.