scholarly journals A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1645
Author(s):  
Nicholas Cartocci ◽  
Marcello R. Napolitano ◽  
Gabriele Costante ◽  
Mario L. Fravolini
Keyword(s):  
Fault Diagnosis ◽  
Fault Isolation ◽  
Data Driven ◽  
Fault Estimation ◽  
Linear Regression Models ◽  
Sensor Fault ◽  
Fault Reconstruction ◽  
Air Speed ◽  
Using Data

Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors.

scholarly journals Integrated Sensor Fault Diagnosis and Fault-Tolerant Control for Manipulator

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Bowen Hong ◽  
Lina Yao ◽  
Zhiwei Gao
Keyword(s):  
Fault Diagnosis ◽  
Controller Design ◽  
Fault Tolerant ◽  
Adaptive Observer ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Fault ◽  
Integrated Sensor ◽  
Model Control ◽  
Constant Fault

In this paper, an integrated scheme including fault diagnosis and fault-tolerant controller design is proposed for the manipulator system with the sensor fault. Any constant fault or time-varying fault can be estimated by the fault diagnosis scheme based on the adaptive observer rapidly and accurately, and the designed parameters can be solved by the linear matrix inequality. Using the fault estimation information, a fault-tolerant controller combining the characteristics of the proportional differentiation control and the sliding model control is designed to trace the expected trajectory via the back-stepping method. Finally, the effectiveness of the above scheme is verified by the simulation results.

A case study of PavMD a new tool to identify good performing pavements in New Zealand

2021 ◽  
Author(s):  
Jacobus Daniel van der Walt ◽  
Eric Scheepbouwer ◽  
Bryan Pidwerbesky ◽  
Brian Guo ◽  
Max Ferguson ◽  
...  
Keyword(s):  
New Zealand ◽  
Performance Measures ◽  
Data Structures ◽  
Digital Technology ◽  
Data Driven ◽  
Pavement Performance ◽  
Using Data ◽  
Additional Value

With the advancement of digital technology, the collection of pavement performance data has become commonplace. The improvement of tools to extract useful information from pavement databases has become a priority to justify expenditures. This paper presents a case study of PaveMD, a tool that integrates multi-dimensional data structures with a data-driven fuzzy approach to identify good performing pavement sections. Combining this tool with an innovative paradigm where the focus is on repeating success can bring additional value to existing pavement databases. The case study shows that PaveMD can identify pavement sections that are performing well by comparing performance measures for the New Zealand context. In this paper, PaveMD's development is described, and its implementation is showcased using data from the New Zealand Long-Term Pavement Performance (LTPP) database. It is recommended that this approach be further developed and extended to other infrastructure databases internationally.

scholarly journals LPV Model Based Sensor Fault Diagnosis and Isolation for Permanent Magnet Synchronous Generator in Wind Energy Conversion Systems

2018 ◽  
Vol 8 (10) ◽  
pp. 1816 ◽  
Author(s):  
Zhimin Yang ◽  
Yi Chai ◽  
Hongpeng Yin ◽  
Songbing Tao
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Synchronous Generator ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Permanent Magnet Synchronous Generator ◽  
Multiple Sensor ◽  
Residual Generator

This paper deals with the current sensor fault diagnosis and isolation (FDI) problem for a permanent magnet synchronous generator (PMSG) based wind system. An observer based scheme is presented to detect and isolate both additive and multiplicative faults in current sensors, under varying torque and speed. This scheme includes a robust residual generator and a fault estimation based isolator. First, the PMSG system model is reformulated as a linear parameter varying (LPV) model by incorporating the electromechanical dynamics into the current dynamics. Then, polytopic decomposition is introduced for H ∞ design of an LPV residual generator and fault estimator in the form of linear matrix inequalities (LMIs). The proposed gain-scheduled FDI is capable of online monitoring three-phase currents and isolating multiple sensor faults by comparing the diagnosis variables with the predefined thresholds. Finally, a MATLAB/SIMULINK model of wind conversion system is established to illustrate FDI performance of the proposed method. The results show that multiple sensor faults are isolated simultaneously with varying input torque and mechanical power.

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