Abstract
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.
Highlights
Fault Diagnosis (FD) is a term that describes the general problem of detecting and isolating faults in physical components or in the instrumentation of a system [1]
Mathematical models play a central role in Analytical Redundancy (AR)-based FD; these are derived from either physical laws governing the dynamics of the system or are inferred directly from experimental data exploiting system identification techniques [3]
In this case, the experimental results confirm that techniques based on transformed directional residuals are more effective than techniques based on primary directional residuals
Summary
Fault Diagnosis (FD) is a term that describes the general problem of detecting and isolating faults in physical components or in the instrumentation of a system [1]. Hardware redundancy (featuring multiple sensors or actuators with the same function) and simple majority voting schemes are used to cope with FD These methods are widespread, there is an increasing number of applications where the additional cost, weight, and size of the components are major constraints, such as small and autonomous flying vehicles. Another study is performed to assess online sensor FI performance by monitoring the temporal history of the residuals and, using this information, to increase or decrease the belief in a fault hypothesis over time For this purpose, a bank of recursive Bayesian filters is designed to infer in-flight sensors’ fault probabilities.
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