Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data

2018 ◽  
Vol 61 (4) ◽  
pp. 1143-1157 ◽  
Author(s):  
Akram Adnane ◽  
Zoubir Ahmed Foitih ◽  
Mohammed Arezki Si Mohammed ◽  
Abdellatif Bellar
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Attitude Estimation ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Satellite Attitude ◽  
Magnetometer Data ◽  
Sensor Fault Detection ◽  
Leo Satellite

scholarly journals Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles Using Parameter Estimation with Recursive Least Squares

Batteries ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Real Time ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Fault Detection And Isolation ◽  
Recursive Least Squares ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Li Ion

With the increase in usage of electric vehicles (EVs), the demand for Lithium-ion (Li-ion) batteries is also on the rise. The battery management system (BMS) plays an important role in ensuring the safe and reliable operation of the battery in EVs. Sensor faults in the BMS can have significant negative effects on the system, hence it is important to diagnose these faults in real-time. Existing sensor fault detection and isolation (FDI) methods have not considered battery degradation. Degradation can affect the long-term performance of the battery and cause false fault detection. This paper presents a model-based sensor FDI scheme for a Li-ion cell undergoing degradation. The proposed scheme uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters in real time. The estimated ECM parameters are put through weighted moving average (WMA) filters, and then cumulative sum control charts (CUSUM) are implemented to detect any significant deviation between unfiltered and filtered data, which would indicate a fault. The current and voltage faults are isolated based on the responsiveness of the parameters when each fault occurs. The proposed FDI scheme is then validated through conducting a series of experiments and simulations.

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

scholarly journals Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

2008 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Fault Detection ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Channel Measurements ◽  
Sensor Fault ◽  
Engine Model ◽  
Dual Channel ◽  
Sensor Fault Detection ◽  
Baseline System ◽  
On Line

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components.

scholarly journals Neuro-Fuzzy for Sensor Fault Detection and Isolation

2014 ◽  
Vol 90 (17) ◽  
pp. 42-46
Author(s):  
Rajendra Sharma ◽  
Snehal Kokil ◽  
Prtit Khaire
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Neuro Fuzzy ◽  
Sensor Fault Detection
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