FDI using Multiple Parity Vectors for Redundant Inertial Sensors

doi:10.3166/ejc.12.437-449

European Journal of Control

Volume 12, Issue 4, 2006, Pages 437-449

https://doi.org/10.3166/ejc.12.437-449 Get rights and content

This paper discusses fault detection, isolation, and accommodation (FDIA) problem of redundant sensors in inertial navigation systems (INS). The averaged parity vector (APV) method is suggested to improve the probability of correct isolation and reduce the numbers of false alarms and wrong isolation. The proposed method employs a new accommodation threshold based on the error covariance of an estimated variable, which is related to the navigation accuracy of INS. This accommodation threshold is also used as the fault detection threshold, so the APV algorithm detects only non-tolerable faults. Given a conditional probability of correct isolation, the minimum number of sample measurements is given with respect to fault size. Computer simulation and experimental results verify the validity of the proposed method.

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Citation Excerpt :
Chen et al. in [33] has proposed another method for multi-fault condition monitoring for a slurry pump based on PCA and Sequential Probability Ratio Test (SPRT). Specific examples related to the aeronautical systems can be found in [34,35], but, in general, there are very few examples of complete studies applied to the robust multiple-fault diagnosis of aircrafts especially based on experimental data. In the present study, a complete robust data-based diagnostic scheme for the Fault Detection, Isolation and Estimation of multiple sensor faults is proposed.
A general robust data-driven scheme for the Fault Detection, Isolation and Estimation of multiple sensor faults is proposed and validated using multi-flight data records. Robustness to modelling uncertainty and noise is achieved through an optimized data-driven design of the three blocks that constitute the scheme. First, a robust Fault Detection (FD) filter given by the linear combination of previously identified Analytical Redundancy Relationships (AARs) is derived as the solution of a multi-objective optimization where the minimum fault sensitivity is maximized while the standard deviation (STD) of the filtered error, in nominal condition, is minimized. Then, a Fault Pre-Isolation (FPI) block is introduced to select a restricted number of sensors containing with high likelihood the subset of the faulty sensors. In this phase, robustness is achieved through the data-driven design of a redundant number of Multiple-ARRs and a voting logic. Finally, the robust Fault Isolation (FI) is achieved relying on the design of a large collection of additional AARs whose fault signatures are specifically designed to optimize, at the same time, noise immunity while maximizing the decoupling of the (pre-isolated) fault directions. A procedure based on fault amplitude reconstruction is proposed to isolate the set of faulty sensors sequentially. The proposed scheme has been applied to the design of a multiple Fault Diagnosis scheme for a set of 8 sensors of a semi-autonomous aircraft basing on multi-flight data. Validation results are compared with state-of-the-art multiple Fault Diagnosis schemes.
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FDI using Multiple Parity Vectors for Redundant Inertial Sensors

Automatica

Instrument fault detection and isolation: state of the art and new research trends

IEEE Trans Instrum Meas

Analytical redundancy for sensor fault isolation and accommodation in public transportation vehicles

IEEE Trans Instrum Meas

A sequential failure detection technique and its application

IEEE Trans Autom Control

Parity vector approach for detecting failures in dynamic systems

Int J Syst

Generalized likelihood test for FDI in redundant sensor configurations

J Guid Control

Detection, isolation, and identification of sensor faults in nuclear power plants

IEEE Trans Control Syst Technol

Fault detection and diagnosis in engineering systems

Marcel Dekker

A redundant strapdown inertial reference unit (SIRU)

J Spacecraft

Evaluating sensor orientations for navigation performance and failure detection

IEEE Trans Aerosp Electron Syst

Configuration of redundant sensor system and its fault detection using parity vector method

Reliability analysis of fault-tolerant IMU architectures with redundant inertial sensors

IEEE AES Mag

Optimal parity vector sensitive to designated sensor fault

IEEE Trans Aerosp Electron Syst

Aircraft sensor failure detection by analytical redundancy in concise encyclopedia of aeronautics & space systems

Issues of fault diagnosis for dynamic systems