Sensor Fault Detection and Recovery Methodology for a Geothermal Heat Exchanger

2018 ◽  
pp. 171-184 ◽  
Author(s):  
Héctor Alaiz-Moretón ◽  
José Luis Casteleiro-Roca ◽  
Laura Fernández Robles ◽  
Esteban Jove ◽  
Manuel Castejón-Limas ◽  
...  
Keyword(s):  
Heat Exchanger ◽  
Fault Detection ◽  
Sensor Fault ◽  
Geothermal Heat ◽  
Geothermal Heat Exchanger ◽  
Sensor Fault Detection

scholarly journals An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger

2016 ◽  
Vol 17 ◽  
pp. 36-47 ◽  
Author(s):  
José Luis Casteleiro-Roca ◽  
Héctor Quintián ◽  
José Luis Calvo-Rolle ◽  
Emilio Corchado ◽  
María del Carmen Meizoso-López ◽  
...  
Keyword(s):  
Heat Exchanger ◽  
Fault Detection ◽  
Heat Pump ◽  
Detection System ◽  
Geothermal Heat ◽  
Geothermal Heat Exchanger

Sensor fault detection and isolation via high-gain observers: Application to a double-pipe heat exchanger

2011 ◽  
Vol 50 (3) ◽  
pp. 480-486 ◽  
Author(s):  
R.F. Escobar ◽  
C.M. Astorga-Zaragoza ◽  
A.C. Téllez-Anguiano ◽  
D. Juárez-Romero ◽  
J.A. Hernández ◽  
...  
Keyword(s):  
Heat Exchanger ◽  
Fault Detection ◽  
High Gain ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Double Pipe ◽  
Pipe Heat

scholarly journals A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2740 ◽  
Author(s):  
Héctor Aláiz-Moretón ◽  
Manuel Castejón-Limas ◽  
José-Luis Casteleiro-Roca ◽  
Esteban Jove ◽  
Laura Fernández Robles ◽  
...  
Keyword(s):  
Heat Exchanger ◽  
Fault Detection ◽  
Detection System ◽  
Practical Importance ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Geothermal Heat ◽  
Adaptive Boosting ◽  
Random Decision Forests ◽  
Geothermal Heat Exchanger

This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.

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.

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