Hybrid Learning Approach to Sensor Fault Detection with Flight Test Data

Silva, Brian M. de; Callaham, Jared; Jonker, Jonathan; Goebel, Nicholas; Klemisch, Jennifer; McDonald, D. C.; Hicks, Nathan; Kutz, J. Nathan; Brunton, Steven L.; Aravkin, Aleksandr Y.

doi:10.2514/1.j059943

Cited by 14 publications

(6 citation statements)

References 58 publications

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“…and abrupt faults (noise, short/open circuit, random faults, etc). In this investigation, we simulate four typical sensor fault types, including slow oscillation, increased noise, slow drift, and catastrophic failure, in line with the analogous studies conducted by Van Eykeren and Chu [50] and Silva et al [39]. Among them, slow oscillation and slow drift are incipient faults, while increased noise and catastrophic failure are abrupt faults.…”

Section: Fault Typesmentioning

confidence: 81%

“…The term y k − H k x− k is referred to as innovation or residual and describes the difference between the prediction and actual measurements. Here, we use the moving innovation covariance to describe the characteristics of anomalous behavior, following Mehra and Peschon [44], Hajiyev [45] and Silva et al [39,40], which is defined as…”

Section: Kalman Filtermentioning

confidence: 99%

“…Fathi et al [37] and Jiang and Liu [38] used KF and its variants to denoise observation data and proposed KF-DMD, EnKF-DMD, and DMD-KF-W methods to reconstruct noisy deterministic dynamic systems and random dynamical systems. Silva et al [39,40] proposed a physically enhanced data-driven fault detection method that uses dynamic mode decomposition with control (DMDc) and Kalman filter to generate residuals and also provide a decision tree for fault diagnosis. To the best of our knowledge, this is the first time in the field of FDIR that a combination of the DMD and KF methods was used.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, this is the first time in the field of FDIR that a combination of the DMD and KF methods was used. However, Silva et al [39,40] only provide the binary classification of whether or not the fault occurs; moreover, extraction and analysis of fault features are insufficient. For FDIR problems, it is essential to accurately determine the fault type for fault isolation, controller reconfiguration, and faulttolerance control.…”

Section: Introductionmentioning

confidence: 99%

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A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Song,

Chen,

Wang

et al. 2024

Meas. Sci. Technol.

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Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of modern industrial systems, and accurate fault diagnosis of systems based on multi-source signals remains challenging. This study proposes a data-driven hybrid fault detection/diagnosis method to identify sensor faults in complex systems through interactions between multiple sensors. Dynamic Mode Decomposition with control (DMDc) is used to obtain the approximate model of the investigated system from multi-source signals, extract the underlying physical mechanisms, combined with the Kalman filter observer to generate the residual between the observed data and the predicted data. Then the residual (moving innovation covariance matrix V k) is input into the k-nearest neighbor (kNN) classification algorithm for fault detection and diagnosis. The effectiveness of the proposed method was evaluated using the flight test braking system dataset. The results showed that the accuracy of the proposed method in fault detection and diagnosis (with accuracies of 100% and 100%, respectively) was significantly improved than that of using raw signal data (76.6% and 6.38%) or raw signal data and V k (80.85% and 42.55%). The analysis of different parameters including fault severity, algorithm hyperparameter k, and sensor type showed that the proposed method has high robustness, generalization ability, and practicality.

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Section: Fault Typesmentioning

confidence: 81%

Section: Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Song,

Chen,

Wang

et al. 2024

Meas. Sci. Technol.

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show abstract

“…Broadly speaking, they can be grouped into [5,13]: model-based, knowledge-based and data-driven approaches. These approaches have been used in different application domains: chemical process monitoring [7], aircraft control applications [8,9], wearable health monitoring devices [10] and SHM applications [1,2,5,11,12,23].…”

Section: Introductionmentioning

confidence: 99%

A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring

Oncescu

Cicirello

2022

Lecture Notes in Civil Engineering

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A self-supervised classification algorithm is proposed for detecting and isolating sensor faults of health monitoring devices. This is achieved by automatically extracting information from failure investigations. This approach uses (i) failure reports for extracting comprehensive failure labels; (ii) recorded data of a faulty monitoring device and the information of the failure type for selecting fault-sensitive features. The features-label pairs are then used to train a classification algorithm, so that when a new set of measurements becomes available, the algorithm is capable of identifying with a high accuracy one of the possible failure types included in the training data set. The proposed approach is successfully applied to the failure investigations conducted on a low-cost wearable device, displaying similar challenges encountered in SHM.

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Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

CHEN,

SHENG,

ZHANG

et al. 2024

Chinese Journal of Aeronautics

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Hybrid Learning Approach to Sensor Fault Detection with Flight Test Data

Cited by 14 publications

References 58 publications

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring

Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

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