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Intelligent Sensing Technologies for Blade Health Monitoring and Fault Detection
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Intelligent Sensing Technologies for Blade Health Monitoring and Fault Detection

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (20 May 2026) | Viewed by 2805

Editor

Prof. Dr. Baijie Qiao

E-Mail Website
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

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (3 papers)

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Research

37 pages, 10527 KB  
Article
Cross-Sensor Consistency-Guided Dual-Spectrum Fusion for Offshore Wind Turbine Blade Defect Diagnosis and Risk Grading
by Yukun Wang, Chenhao Sun, Ruifeng Liao, Lijun Luo and Jiefeng Duan
Sensors 2026, 26(12), 3878; https://doi.org/10.3390/s26123878 - 18 Jun 2026
Viewed by 490
Abstract
Offshore wind turbine blades are chronically exposed to complex marine environments with high humidity, salt spray, strong wind, waves, and intense radiation. Under such conditions, blade defects often exhibit small sizes, weak visual features, and heterogeneous visible infrared manifestations. Conventional single-sensor monitoring and [...] Read more.
Offshore wind turbine blades are chronically exposed to complex marine environments with high humidity, salt spray, strong wind, waves, and intense radiation. Under such conditions, blade defects often exhibit small sizes, weak visual features, and heterogeneous visible infrared manifestations. Conventional single-sensor monitoring and empirically weighted fusion methods are insufficient for reliable defect diagnosis and risk grading. To address this problem, this paper proposes a cross-sensor consistency-guided dual-spectrum fusion framework, termed CG-DSF, for offshore wind turbine blade defect diagnosis and risk assessment. First, visible-light images and infrared thermal images are acquired by UAV-mounted imaging sensors, and sensor-specific branches are constructed to extract surface structural features and thermal anomaly responses. Second, visible and infrared features are aligned at the feature token level, and cross-sensor evidence is evaluated for spatial consistency, diagnostic semantic consistency, and anomaly consistency. A reliability-aware fusion strategy is then used to suppress low-quality or conflicting observations and construct a unified defect representation. Finally, a series of representative simulation case studies are carried out to comprehensively assess the overall performance and practical applicability of the constructed model. Experimental results reveal that the proposed framework possesses evident advantages in blade defect identification for offshore wind turbines, offering a feasible solution for advancing proactive and intelligent condition-based operation and maintenance of offshore wind assets in complex marine environments. Full article
(This article belongs to the Special Issue Intelligent Sensing Technologies for Blade Health Monitoring and Fault Detection)
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18 pages, 11817 KB  
Article
Anisotropic Magnetoresistive Sensors: Dynamic Modeling and Characterization for Blade Tip-Timing Measurements
by Daniele Busti, Lorenzo Capponi, Antonella Gaspari, Laura Fabbiano and Gianluca Rossi
Sensors 2026, 26(8), 2506; https://doi.org/10.3390/s26082506 - 18 Apr 2026
Cited by 2 | Viewed by 543
Abstract
Monitoring of blade vibrations in turbomachinery equipped with ferromagnetic blades is currently performed using the Blade Tip-Timing (BTT) non-contact technique. To reduce measurement uncertainty on time samples, BTT systems require measurement probes to meet high dynamic performance requirements. Anisotropic magnetoresistive (AMR) sensors have [...] Read more.
Monitoring of blade vibrations in turbomachinery equipped with ferromagnetic blades is currently performed using the Blade Tip-Timing (BTT) non-contact technique. To reduce measurement uncertainty on time samples, BTT systems require measurement probes to meet high dynamic performance requirements. Anisotropic magnetoresistive (AMR) sensors have recently gained interest for this application owing to their high sensitivity to magnetic flux variations and robustness in harsh, contaminated environments. However, a thorough dynamic characterization of AMR-based BTT probes remains largely unexplored, representing a critical gap in next-generation industrial measurement systems. This work presents a custom-designed signal conditioning circuit tailored for AMR-based BTT measurements, alongside a systematic methodology for characterizing its dynamic performance. The circuit is modeled as a block diagram, from which transfer functions are derived analytically and validated experimentally, providing a rigorous and reproducible framework for probe dynamic assessment. The complete instrumentation chain is then tested on a low-speed rotor test bench in a BTT configuration. Results reveal a fundamental sensitivity–bandwidth trade-off: satisfying the cutoff frequency requirement imposed by BTT applications inherently reduces signal gain below the threshold needed to resolve individual blade-passage events. This finding isolates the key design bottleneck for AMR-based BTT probes and provides quantitative guidance for future optimization of both sensor and circuit design toward industrial tip-timing deployment. Full article
(This article belongs to the Special Issue Intelligent Sensing Technologies for Blade Health Monitoring and Fault Detection)
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25 pages, 3667 KB  
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 1223
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
(This article belongs to the Special Issue Intelligent Sensing Technologies for Blade Health Monitoring and Fault Detection)
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