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Intelligent Sensing Technologies for Blade Health Monitoring and Fault Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 336

Special Issue Editor


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Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: inverse problem; signal processing; non-contact measurement; blade tip timing; fault diagnosis

Special Issue Information

Dear Colleagues,

Blades constitute the most abundant and critical components of turbomachinery, with their vibration characteristics directly impacting the structural integrity. These harsh work environments necessitate advanced methodologies for the monitoring of blade health to ensure operational safety, optimize maintenance strategies, and prevent catastrophic failures.

Traditional contact-based measurement techniques, such as strain gauge instrumentation, face inherent limitations in high-temperature applications and lack viability for long-term in situ monitoring due to sensor degradation and intrusive installation requirements.

However, such contact measurements cannot be used for long-term and high-temperature health monitoring. Non-contact, non-intrusive forms of measurement, such as blade tip timing (BTT), blade tip clearance (BTC), microphone array, laser Doppler vibrometer and digital image correlation (DIC), provide opportunities for the measurement and monitoring of turbomachinery.

The scope of this Special Issue includes, but not limited to, the following topics:

  • Blade tip timing;
  • Blade tip clearance;
  • High-resolution blade tip timing systems;
  • Deep learning in blade tip timing;
  • Compressed sensing in blade tip timing;
  • Digital twins in blade tip timing;
  • Time-frequency method for blade health monitoring;
  • Anomaly detection in blade vibration signatures;
  • Dynamic frequency identification;
  • Bayesian frameworks for probabilistic fault diagnosis;
  • Mistuning detection in blisks;
  • Dynamic stress/strain full-field reconstruction;
  • High frequency measurement methods in Blade tip timing;
  • Blade tip timing without OPR;
  • Synchronous and asynchronous vibration detection;
  • Acoustic-based fault diagnosis including gas path faults and mechanical vibration faults;
  • Blade/disk/blisk health monitoring;
  • Blade crack diagnose;
  • DIC for full-field vibration measurement;
  • Uncertainty quantification in non-contact measurement;
  • Intelligent sensing.

Prof. Dr. Baijie Qiao
Guest Editor

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Keywords

  • blade tip timing
  • blade tip clearance
  • blade health monitoring
  • fault diagnosis
  • crack monitoring
  • intelligent sensing
  • dynamic frequency identification

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Published Papers (1 paper)

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Research

25 pages, 3667 KiB  
Article
A Long-Time Series Forecast Method for Wind Turbine Blade Strain with Incremental Bi-LSTM Learning
by Bingkai Wang, Wenlei Sun and Hongwei Wang
Sensors 2025, 25(13), 3898; https://doi.org/10.3390/s25133898 - 23 Jun 2025
Viewed by 208
Abstract
This article presents a novel incremental forecast method to address the challenges in long-time strain status prediction for a wind turbine blade (WTB) under wind loading. Taking strain as the key indicator of structural health, a mathematical model is established to characterize the [...] Read more.
This article presents a novel incremental forecast method to address the challenges in long-time strain status prediction for a wind turbine blade (WTB) under wind loading. Taking strain as the key indicator of structural health, a mathematical model is established to characterize the long-time series forecast forecasting process. Based on the Bi-directional Long Short-Term Memory (Bi-LSTM) framework, the proposed method incorporates incremental learning via an error-supervised feedback mechanism, enabling the dynamic self-updating of the model parameters. The experience replay and elastic weight consolidation are integrated to further enhance the prediction accuracy. Ultimately, the experimental results demonstrate that the proposed incremental forecast method achieves a 24% and 4.6% improvement in accuracy over the Bi-LSTM and Transformer, respectively. This research not only provides an effective solution for long-time prediction of WTB health but also offers a novel technical framework and theoretical foundation for long-time series forecasting. Full article
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