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

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.

Sensor Fault Detection and Validation For Chemical Process Using A Neural Network Model

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Chemical Process ◽  
Sensor Fault ◽  
Sensor Fault Detection

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

scholarly journals Sensor Fault Detection for Salient PMSM based on Parity-Space Residual Generation and Robust Exact Differentiation

2020 ◽  
Vol 53 (2) ◽  
pp. 86-91
Author(s):  
Benjamin Jahn ◽  
Michael Brückner ◽  
Stanislav Gerber ◽  
Yuri A.W. Shardt
Keyword(s):  
Fault Detection ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Parity Space ◽  
Residual Generation

scholarly journals Integral Sensor Fault Detection and Isolation for Railway Traction Drive

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1543 ◽  
Author(s):  
Fernando Garramiola ◽  
Jon del Olmo ◽  
Javier Poza ◽  
Patxi Madina ◽  
Gaizka Almandoz
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Traction Drive ◽  
Railway Traction ◽  
Sensor Fault Detection

Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives

2013 ◽  
Vol 7 (7) ◽  
pp. 607-617 ◽  
Author(s):  
Xinan Zhang ◽  
Gilbert Foo ◽  
Mahinda Don Vilathgamuwa ◽  
King Jet Tseng ◽  
Bikramjit Singh Bhangu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Motor Drives ◽  
Sensor Fault ◽  
Induction Motor Drives ◽  
System Reconfiguration ◽  
Sensor Fault Detection
Sign in / Sign up

Export Citation Format

Share Document