Adaptive Kalman filter for actuator fault diagnosis

Zhang, Qinghua

doi:10.1016/j.automatica.2018.03.075

Cited by 132 publications

(31 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, an adaptive estimator, i.e., the regularised adaptive Kalman filter [35,36], is proposed to address the extrinsic calibration between the IMU and vehicle coordinate systems. First, a discretisation is applied to transform the continuous-time system into a discrete-time system and, then, the adaptive estimator is designed based on the discretized system.…”

Section: Estimator Designmentioning

confidence: 99%

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Marco

Kalkkuhl

Raisch

et al. 2020

Sensors

View full text Add to dashboard Cite

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.

show abstract

Section: Estimator Designmentioning

confidence: 99%

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Marco

Kalkkuhl

Raisch

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Distinguishing actuators’ faults from their position sensors’ fault is one of the challenges of this study. In the fault diagnosis of the actuator, the angle signal of the actuator is the key diagnostic variable (Avram et al, 2017; Zhang, 2018). The acquisition of actuator angle depends on the position sensor, but most studies ignore the fault of the position sensor and take the position signal output by the sensor as the true position of the actuator or its unbiased estimation (Hassanabadi et al, 2017; Li et al, 2017; Liu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Novel topology convolutional neural network fault diagnosis for aircraft actuators and their sensors

Wei

Jiang

et al. 2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

The variable working conditions and frequent turns make the aircraft actuator system prone to failure, seriously threatening flight safety. The identification of the airplane actuator system is critical for flight decisions and safety. Most fault diagnosis methods of actuators only focus on the actuators themselves, ignoring the disturbance caused by the fault of the actuator position sensor, which may easily lead to wrong decisions. In order to distinguish the actuator fault from its position sensor fault and identify the fault type accurately, an offline diagnosis method of convolutional neural network (CNN) with novel topology for processing time series is proposed. A new shift layer is added after the input layer, which avoids the loss of a large number of features due to the direct connection between the time series and the convolutional layer. A local topological network learning complex pattern with inception module is designed to improve the diagnostic accuracy in different working conditions. The wide residual structure is introduced to expand the convolutional channel, which allows the network features at the bottom level to propagate directly to the top level to prevent network degradation. Simulation results show that this method can accurately diagnose the actuator fault and its position sensor, with an average accuracy of 96.8%. Compared with the current mainstream data-driven methods, the precision and recall are increased by 6.3% and 6.7% respectively.

show abstract

“…The Kalman filter has been successfully implemented to displacement fusion between acceleration and direct displacement where measurement noise (R) and process noise (Q) can be clearly identified [9]. However, in the case of reference-free displacement estimation where R and Q may not be determinate, the performance of a Kalman filter is not guaranteed [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Bridge Displacement Estimation Using a Co-Located Acceleration and Strain

Sarwar

Park

2020

Sensors

View full text Add to dashboard Cite

Structural displacement is an important metric for assessing structural conditions because it has a direct relationship with the structural stiffness. Many bridge displacement measurement techniques have been developed, but most methods require fixed reference points in the vicinity of the target structure that limits the field implementations. A promising alternative is to use reference-free measurement techniques that indirectly estimate the displacement by using measurements such as acceleration and strain. This paper proposes novel reference-free bridge displacement estimation by the fusion of single acceleration with pseudo-static displacement derived from co-located strain measurements. First, we propose a conversion of the strain at the center of a beam into displacement based on the geometric relationship between strain and deflection curves with reference-free calibration. Second, an adaptive Kalman filter is proposed to fuse the displacement generated by strain with acceleration by recursively estimating the noise covariance of displacement from strain measurements which is vulnerable to measurement condition. Both numerical and experimental validations are presented to demonstrate the efficiency and robustness of the proposed approach.

show abstract

Adaptive Kalman filter for actuator fault diagnosis

Cited by 132 publications

References 22 publications

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Novel topology convolutional neural network fault diagnosis for aircraft actuators and their sensors

Bridge Displacement Estimation Using a Co-Located Acceleration and Strain

Contact Info

Product

Resources

About