Adaptive Flux Observers and Rotor Speed Sensor Fault Detection in Induction Motors

2014 ◽  
pp. 3-18 ◽  
Author(s):  
R. Marino ◽  
S. Scalzi ◽  
P. Tomei ◽  
C. M. Verrelli
Keyword(s):  
Fault Detection ◽  
Induction Motors ◽  
Rotor Speed ◽  
Sensor Fault ◽  
Speed Sensor ◽  
Sensor Fault Detection

Steady-state speed sensor fault detection in induction motors with uncertain parameters: A matter of algebraic equations

2018 ◽  
Vol 80 ◽  
pp. 125-137 ◽  
Author(s):  
Cristiano Maria Verrelli ◽  
Emilio Lorenzani ◽  
Raffaele Fornari ◽  
Michele Mengoni ◽  
Luca Zarri
Keyword(s):  
Steady State ◽  
Fault Detection ◽  
Induction Motors ◽  
Uncertain Parameters ◽  
Algebraic Equations ◽  
Sensor Fault ◽  
Speed Sensor ◽  
Sensor Fault Detection

A New Approach for Effective Position/Speed Sensor Fault Detection in PMSM Drives

2020 ◽  
pp. 359-372
Author(s):  
Saïda Ben Slimen ◽  
Manef Bourogaoui ◽  
Houda Ben Attia Sethom
Keyword(s):  
Fault Detection ◽  
Sensor Fault ◽  
New Approach ◽  
Speed Sensor ◽  
Sensor Fault Detection

scholarly journals TakagiSugeno Fuzzy Observer Design for Induction Motors with unmeasurable Decision Variables: State Estimation and Sensor Fault Detection

2011 ◽  
Vol 23 (4) ◽  
pp. 44-51 ◽  
Author(s):  
M. Allouche ◽  
M. Chaabane ◽  
M. Souissi ◽  
D. Mehdi ◽  
F. Tadeo
Keyword(s):  
Fault Detection ◽  
State Estimation ◽  
Induction Motors ◽  
Observer Design ◽  
Sensor Fault ◽  
Fuzzy Observer ◽  
Decision Variables ◽  
Sensor Fault Detection

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.

Sign in / Sign up

Export Citation Format

Share Document