Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection

Crispoltoni, Michele; Fravolini, Mario Luca; Balzano, Fabio; D'Urso, Stephane; Napolitano, Marcello R.

doi:10.3390/s18082488

Cited by 22 publications

(12 citation statements)

References 27 publications

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“…Therefore, an outlier can never be identified if it occurs in one of these measurements, but it can be detected. 1.00 0.98 0.93 ∆h 5 1.00 0.98 ∆h 6 1.00…”

Section: On the Probability Levels Of Iterative Outlier Eliminationmentioning

confidence: 99%

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A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions but must be computed numerically by means of Monte Carlo. This paper provides the first results on the Monte Carlo-based critical value inserted into different scenarios of correlation between outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, over-identifications and statistical overlap associated with IDS in the presence of a single outlier. On the basis of such probability levels, we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for cases in which IDS is in play. The MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances in which the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection but the lower the rates of outlier identification. In such a case, the larger the Type I Error, the larger the ratio between the MIB and MDB. We also highlight that an outlier becomes identifiable when the contributions of the measures to the wrong exclusion rate decline simultaneously. In this case, we verify that the effect of the correlation between outlier statistics on the wrong exclusion rate becomes insignificant for a certain outlier magnitude, which increases the probability of identification.

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“…Therefore, an outlier can never be identified if it occurs in one of these measurements, but it can be detected. 1.00 0.98 0.93 ∆h 5 1.00 0.98 ∆h 6 1.00…”

Section: On the Probability Levels Of Iterative Outlier Eliminationmentioning

confidence: 99%

“…In recent years, Outlier Detection has been increasingly applied in sensor data processing [1][2][3][4][5][6][7][8][9]. Despite the countless contributions made over the years, there is continuing research on the subject, mainly because there has been an increase in computational power.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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“…Despite the fact that Hardware Physics Redundancy (HPR) can significantly improve the reliability of IMU, it is necessary to pay due attention to the faults in IMU measurements. A significant number of serious accidents have happened due to IMU failures, such as the crashes of the Qantas F72 and Croatia Boeing 737-200 [29]. In addition, given that low-cost IMU sensors may be carried on UAVs for future UAM applications, such as passenger/cargo air transportation, the output of IMU must be monitored against the possible faults.…”

Section: Of 21mentioning

confidence: 99%

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Wang

Zhan

Zhai

et al. 2020

Sensors

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To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

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“…It was reported that LMI can be used for identification and estimation. LMI can be also used for model identification [20]. To calculate the fault estimation algorithm, additional design constraints were faced, which could not be solved with typical methods.…”

Section: Literature Surveymentioning

confidence: 99%

Stability Driven Optimal Controller Design for High Quality Images

Suh¹

2018

Electronics

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This note presents an optimal design method to enhance image quality in optical image stabilization (OIS) systems. First of all, performance limitations of conventional methods are shown and secondly, a new design framework based on convex optimization is proposed. The resulting controller essentially stabilizes the closed loop systems because the proposed method is derived from Lyapunov stability. From the test results, it is confirmed that this method reduces the effect of hand vibrations and makes images sharp. Additionally, it is shown that the proposed method is also effective in robot vision and recognition rate of deep neural network (DNN) based traffic signs and pedestrians detection in automotive applications. This note has three main contributions. First, performance limitations of the conventional method are shown. Second, from the relation between sensitivity and complementary sensitivity functions, an indirect design method for performance improvement is proposed, and finally, stability guaranteed optimal design is proposed. Unlike conventional methods, the proposed method does not require addition filters to suppress resonances of the plant and this note highlights phases of the closed loop systems on removing external vibrations.

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Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection

Cited by 22 publications

References 27 publications

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Stability Driven Optimal Controller Design for High Quality Images

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