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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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SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System.

Peng Zhou¹, Chang Liu², Jiacan Xu¹

¹College of Engineering Training and Innovation, Shenyang Jianzhu University, Shenyang 110168, China.

Sensors (Basel, Switzerland)

|April 27, 2024

Summary

A new fault diagnosis method (SSFL) accurately detects disk cutter wear in tunnel boring machines (TBMs), improving efficiency and reducing costs. This method enables timely tool replacement, preventing decreased boring performance due to wear.

Keywords:

failure localization hydraulic thrust system tooling system tunnel boring machine (TBM)

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Area of Science:

Engineering
Mechanical Engineering
Predictive Maintenance

Background:

Tooling system wear in tunnel boring machines (TBMs) significantly impacts efficiency, costs, and safety.
Detecting disk cutter wear during operation is challenging, leading to potential project delays and increased expenses.

Purpose of the Study:

To propose a novel fault diagnosis method for TBM tooling systems, specifically addressing disk cutter wear.
To enhance the detection of wear-related faults, thereby improving TBM operational efficiency and safety.

Main Methods:

Developed a 3D model of the TBM hydraulic thrust and tool systems using SolidWorks.
Conducted dynamic simulations with Adams to analyze hydraulic cylinder load variations due to cutter wear.
Modeled and simulated the hydraulic propulsion system in AMESIM, acquiring pressure and flow signals.
Established a SAV-SVDD failure location (SSFL) network model for fault diagnosis.

Main Results:

The SSFL network model achieved 90% accuracy in identifying the failure area of the cutter head.
Correlations were established between hydraulic cylinder signals (pressure, flow) and the extent of tooling system failure.
The SSFL model demonstrated superior performance compared to SAE-SVM and SVDD models.

Conclusions:

The proposed SSFL method effectively diagnoses disk cutter wear in TBMs.
Timely identification of worn tooling enables proactive replacement, mitigating efficiency losses.
The study validates the feasibility of the SSFL approach for real-world TBM applications.