Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM

Da Zhang ¹, Kun Zheng ¹, Fuqi Liu ¹

⁰College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.

Sensors (basel, Switzerland) +

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April 27, 2024

View abstract on PubMed

Summary

A new model combining improved tuna swarm optimization (ITSO) with convolutional neural networks (CNNs) and bi-directional long short-term memory (BiLSTM) networks enhances hydraulic system fault diagnosis accuracy and robustness to noise.

Area Of Science

Engineering
Artificial Intelligence
Signal Processing

Background

Hydraulic systems are critical in many industries but prone to faults.
Accurate and reliable fault diagnosis is essential for system maintenance and safety.
Existing diagnostic methods may lack efficiency or robustness in complex environments.

Purpose Of The Study

To propose an advanced fault diagnosis model for hydraulic systems.
To enhance diagnostic accuracy and robustness using sensor fusion and intelligent optimization.
To leverage deep learning for automatic feature extraction and analysis.

Main Methods

Sensor selection using the random forest algorithm.
Feature extraction and fusion via convolutional neural networks (CNNs).
Sequential data analysis using bi-directional long short-term memory (BiLSTM) networks.
Optimization of CNN-BiLSTM parameters using an improved tuna swarm optimization (ITSO) algorithm.

Main Results

High diagnostic accuracies achieved: plunger pump (99.07%), cooler (99.4%), throttle valve (98.81%), accumulator (98.51%).
The model effectively extracts fused features and utilizes multi-sensor information.
Demonstrated good robustness to noise interference across various signal-to-noise ratios (SNRs).

Conclusions

The proposed ITSO-CNN-BiLSTM model offers accurate and reliable fault diagnosis for hydraulic systems.
The method shows significant potential for real-world applications requiring robust diagnostic capabilities.
ITSO effectively optimizes deep learning models for complex signal processing tasks.

Keywords:

bi-directional long short-term memory network convolutional neural network fault diagnosis hydraulic system information fusion tuna swarm optimization