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Related Experiment Video

Updated: Dec 12, 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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Intelligent Impulse Finder: A boosting multi-kernel learning network using raw data for mechanical fault

Jinglong Chen1, Yuanhong Chang1, Cheng Qu1

  • 1State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.

ISA Transactions
|August 11, 2020
PubMed
Summary

This study introduces a new method for recognizing impulse responses in mechanical vibration signals, overcoming challenges posed by noise and complex data. The approach enhances fault diagnosis accuracy in machinery.

Keywords:
Boosting algorithmConvolutional neural networkFault diagnosisRolling element bearingsTransient impulse

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Intelligent diagnosis methods often act as 'black boxes', lacking transparency in fault classification.
  • Recognizing impulse responses in vibration signals is crucial for mechanical equipment health but is difficult and time-consuming, especially with noise.
  • Existing methods rarely leverage intelligent approaches for impulse recognition in vibration data.

Purpose of the Study:

  • To develop a novel method for accurately recognizing impulse responses in raw mechanical vibration data.
  • To address the challenges of noise and complexity in identifying critical fault indicators.
  • To extract and understand the discriminative knowledge underlying impulse responses.

Main Methods:

  • A single-kernel convolutional neural network (CNN) was employed as a weak classifier to learn discriminative features from raw vibration data.
  • A coarse-to-fine search strategy was developed to precisely locate the positions of impulse responses.
  • A boosting algorithm was utilized to ensemble multiple weak classifiers for robust final output.

Main Results:

  • The proposed method demonstrated higher accuracy in recognizing impulse responses compared to the traditional Laplace wavelet method.
  • Vibration signals from bearings with two distinct faults were effectively analyzed, validating the model's performance.
  • The extracted kernel functions provided novel insights into impulse response characteristics, differing from traditional hypotheses.

Conclusions:

  • The novel impulse recognition method effectively captures impulse responses from raw mechanical data, improving diagnostic accuracy.
  • The approach offers a more transparent understanding of fault diagnosis by revealing discriminative knowledge through extracted kernel functions.
  • This research paves the way for improved intelligent methods in mechanical fault diagnosis by shedding light on impulse response characteristics.