TY - JOUR

T1 - Fault Detection in Axial Deformation Sensors for Hydraulic Turbine Head-Cover Fastening Bolts Using Analytical Redundancy

AU - Yujra Rivas, Eddy

AU - Vyacheslavov, Alexander

AU - Gogolinskiy, Kirill

AU - Sapozhnikova, Kseniia

AU - Taymanov, Roald

N1 - Yujra Rivas, E., Vyacheslavov, A., Gogolinskiy, K., Sapozhnikova, K., & Taymanov, R. (2026). Fault Detection in Axial Deformation Sensors for Hydraulic Turbine Head-Cover Fastening Bolts Using Analytical Redundancy. Sensors, 26(3), 801. https://doi.org/10.3390/s26030801

PY - 2026/1/25

Y1 - 2026/1/25

N2 - This study proposes an analytical redundancy method that combines empirical models with a Kalman filter to ensure the reliability of measurements from axial deformation sensors in a turbine head-cover bolt-monitoring system. This integration enables the development of predictive models that optimally estimate the dynamic deformation of each bolt during turbine operation at full and partial load. The test results of the models under conditions of outliers, measurement noise, and changes in turbine operating mode, evaluated using accuracy and sensitivity metrics, confirmed their high accuracy ( Acc ≈ 0.146 µm) and robustness ( S A < 0.001). The evaluation of the models' responses to simulated sensor faults (offset, drift, precision degradation, stuck-at) revealed characteristic residual patterns for faults with magnitudes > 5 µm. These findings establish the foundation for developing a fault detection and isolation algorithm for continuous monitoring of these sensors' operational health. For practical implementation, the models require validation across all operational modes, and maximum admissible deformation thresholds must be defined.

AB - This study proposes an analytical redundancy method that combines empirical models with a Kalman filter to ensure the reliability of measurements from axial deformation sensors in a turbine head-cover bolt-monitoring system. This integration enables the development of predictive models that optimally estimate the dynamic deformation of each bolt during turbine operation at full and partial load. The test results of the models under conditions of outliers, measurement noise, and changes in turbine operating mode, evaluated using accuracy and sensitivity metrics, confirmed their high accuracy ( Acc ≈ 0.146 µm) and robustness ( S A < 0.001). The evaluation of the models' responses to simulated sensor faults (offset, drift, precision degradation, stuck-at) revealed characteristic residual patterns for faults with magnitudes > 5 µm. These findings establish the foundation for developing a fault detection and isolation algorithm for continuous monitoring of these sensors' operational health. For practical implementation, the models require validation across all operational modes, and maximum admissible deformation thresholds must be defined.

KW - fault detection

KW - analytical redundancy

KW - Kalman filter

KW - Monitoring system

KW - Francis turbine

KW - bolts deformation

KW - Francis turbine

KW - Kalman filter

KW - analytical redundancy

KW - bolts deformation

KW - fault detection

KW - monitoring system

UR - https://www.mendeley.com/catalogue/902ac700-51f9-3804-919a-2c7e80561581/

U2 - 10.3390/s26030801

DO - 10.3390/s26030801

M3 - Article

C2 - 41682317

VL - 26

SP - 801

JO - Sensors

JF - Sensors

SN - 1424-3210

IS - 3

M1 - 801

ER -