Sensitivity analysis of fault detection for few-mode fiber links based on Rayleigh backscattering

2021 ◽  
Author(s):  
Zhenxing He ◽  
Feng Liu ◽  
Wenping Zhang ◽  
Guijun Hu
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Rayleigh Backscattering ◽  
Fiber Links

Development and Validation of a General and Robust Methodology for the Detection and Classification of Gas Turbine Sensor Faults

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Lucrezia Manservigi ◽  
Mauro Venturini ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
Enzo Losi
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Sensor Fault ◽  
Fault Detection And Classification ◽  
Sensor Fault Detection ◽  
Optimal Setting ◽  
Monitoring And Diagnostics ◽  
Development And Validation

Abstract Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics, since raw data cleaning represents a key process in the gas turbine industry. To this end, this paper presents a comprehensive approach for detection, classification, and integrated diagnostics of gas turbine sensors (named DCIDS), which was previously developed by the authors and has been substantially improved and validated by means of field data. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called improved-DCIDS (I-DCIDS), can identify seven classes of faults, i.e., out of range, stuck signal, dithering, standard deviation, trend coherence, spike, and bias. First, this paper details the I-DCIDS methodology for sensor fault detection and classification. The methodology uses basic mathematical laws that require some user-defined configuration parameters, i.e., acceptability thresholds and windows of observation. Second, a sensitivity analysis is carried out on I-DCIDS parameters to derive some rules of thumb about their optimal setting. The sensitivity analysis is performed on four heterogeneous and challenging datasets with redundant sensors acquired from Siemens gas turbines (GTs). The results demonstrate the diagnostic capability of the I-DCIDS approach in a real-world scenario. Moreover, the methodology proves to be suitable for all types of datasets and physical quantities and, thanks to its optimal tuning, can also identify the exact time point of fault onset.

scholarly journals A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3066
Author(s):  
Egidio D’Amato ◽  
Vito Antonio Nardi ◽  
Immacolata Notaro ◽  
Valerio Scordamaglia
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Flight Control ◽  
Particle Filtering ◽  
Low Cost ◽  
Experimental Tests ◽  
Attitude Estimation ◽  
Fault Detection And Isolation ◽  
Raspberry Pi ◽  
Filter Approach

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

Sensitivity analysis for PCA-based chiller sensor fault detection

2016 ◽  
Vol 63 ◽  
pp. 133-143 ◽  
Author(s):  
Yunpeng Hu ◽  
Guannan Li ◽  
Huanxin Chen ◽  
Haorong Li ◽  
Jiangyan Liu
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Sensor Fault ◽  
Sensor Fault Detection

An LMI approach to minimum sensitivity analysis with application to fault detection

Automatica ◽  
2005 ◽  
Vol 41 (11) ◽  
pp. 1995-2004 ◽  
Author(s):  
Jian Liu ◽  
Jian Liang Wang ◽  
Guang-Hong Yang
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Lmi Approach ◽  
Minimum Sensitivity
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