In industrial production processes, rotational speed is a key parameter for equipment condition monitoring and fault diagnosis. To achieve rotational speed measurement of rotational equipment under a condition of high temperature and heavy dust, this article proposes a digital approach using an electrostatic sensor. The proposed method utilizes a strip of a predetermined material stuck on the rotational shaft which will accumulate a charge because of the relative motion with the air. Then an electrostatic sensor mounted near the strip is employed to obtain the fluctuating signal related to the rotation of the charged strip. Via a signal conversion circuit, a square wave, the frequency of which equals that of the rotation shaft can be obtained. Having the square wave, the M/T method and T method are adopted to work out the rotational speed. Experiments were conducted on a laboratory-scale test rig to compare the proposed method with the auto-correlation method. The largest relative errors of the auto-correlation method with the sampling rate of 2 ksps, 5 ksps are 3.2% and 1.3%, respectively. The relative errors using digital approaches are both within ±4‰. The linearity of the digital approach combined with the M/T method or T method is also superior to that of the auto-correlation method. The performance of the standard deviations and response speed was also compared and analyzed to show the priority of the digital approach.

The spectrum scanned by a Fourier transform spectrometer (FTIR) often has a baseline drift. However, baseline distortion rarely occurs in a laboratory owing to the insignificant effects of environmental vibrations and electromagnetic factors. Even if it occurs, the distorted spectrum can be manually eliminated. However, in a complex environment, especially after the long-term operation of a spectrometer, the scanned spectrum may be distorted to different degrees. Herein, the origins of spectral baseline drifts and distortions are analyzed and simulated using MATLAB; furthermore, a baseline correction method based on the baseline-type model is proposed. The results of experiments performed on the methane spectrum confirm that the proposed method outperformed the improved modified multi-polynomial fitting and iterative averaging methods.

Many in-motion coarse alignment methods have been proposed and have achieved good results for the strapdown inertial navigation system (SINS) aided by the global positioning system (GPS). However, most existing methods are only applicable to the high-accuracy SINS and the potential influence of the outliers in the GPS outputs are usually ignored. In this article, we consider two practical difficulties: first, the accuracy of the vector observations is degraded by the gyroscope bias; second, the GPS outputs are affected by outliers. To address these issues, we propose a new robust quaternion unscented Kalman filter (RQUKF) method for the low-accuracy SINS and GPS integrated system. First, the RQUKF method is used to estimate the gyroscope bias with the aid of the GPS. Second, to obtain the reliable GPS outputs, the magnitude matching method based on the Huber's robust theory is utilized to detect and eliminate the outliers. The simulation and field tests are conducted and the results illustrate that even if the GPS outputs are affected by outliers, the proposed method still has the higher accuracy in terms of the yaw angle, than traditional algorithms without outliers in the GPS outputs.

Electrostatic sensing technology using correlation signal processing offers an approach to the measurement of rotational speed in the automatic control system of large generators and centrifugal machines. In this article, a double autocorrelation method was proposed to improve the accuracy and robustness of the measurement on the designed test rig. An electrostatic sensor was used to obtain signals from the rotor surface. Then, the rotational speed was determined from the period of rotational motion calculated from a double autocorrelation method. At the same time, experiments with sampling rates of 2ksps (kilo samples per second), 5ksps, 10ksps, 20ksps were carried out on a laboratory-scale test rig under a rotational speed range from 400 r min ⁻¹ to 4200 r min ⁻¹ . The results show that the double autocorrelation method improves the accuracy and robustness. The measurement accuracy also improves with the sampling rate-the relative errors using 2ksps, 5ksps, 10ksps, and 20ksps are within 1.5%, 1%, 0.4%, and 0.3% respectively. The linearity of them is 1.47%, 0.61%, 0.28%, 0.17% correspondingly. The experiments also reveal that the measurement error has a tendency to increase with the rotational speed.

Electrical capacitance tomography (ECT) is an image-based technology which has been widely used for gas-solid two-phase flow measurement. Solid phase electrification is an inevitable phenomenon during pneumatic conveying. To reduce the influence of medium electrification on ECT image reconstruction, a composite sensitivity matrix (CSM) which takes into account the effect of charge electrification is developed in this paper, so that image reconstruction error can be well suppressed. This paper firstly analyzes how medium electrification affects ECT sensitivity field from the perspective of electromagnetic theory. The CSM is then derived accordingly. Simulations and physical experiments are carried out to validate the proposed method. Experiment results show that the relative error and the correlative coefficient of the reconstructed images have been significantly improved comparing with that in traditional approaches. The results support effectiveness of the proposed method.

Existing target segmentation methods are typically considered easy to implement and yield satisfactory results. However, they generally cannot adapt to a new environment without tiring parameter tuning, which leads to poor performance, including issues such as over-segmentation, under-segmentation, missing segmentation, and false positives. To avoid the wrong segmentation in a parameter-tuning-free and user-friendly fashion, we propose a generalized target segmentation (GTS) method based on the image-view representation of point clouds. Specifically, the method avoids devising a Euclidean space-based algorithm that is sensitive to surrounding objects and to the varied point cloud density and intensity in a new environment. The target segment produced by GTS can be used with any target-based extrinsic calibration architecture, based on which this paper further proposes a generalized target-based (in this case, chessboard) extrinsic calibration framework called GTSCalib for a non-repetitive scanning LiDAR and a camera. GTSCalib additionally introduces a novel intensity threshold method based on kernel density estimation (KDE) for 3D corner detection and the SQPnP solver for optimization to achieve more generalized and robust performance. Extensive simulations and experiments demonstrate that GTSCalib has high generalization ability, robustness, and accuracy. The code is released at https://github.com/Natsu-Akatsuki/GTSCalib. Note to Practitioners —Calibration is necessary for many non-repetitive scanning LiDAR-camera systems to enable sensor fusion in the fields of mapping, localization, and perception. Unfortunately, existing target-based (in this case, chessboard) calibration methods are weakly adaptable to the surrounding environment with variable density or intensity of point clouds, resulting in unstable performance, particularly for the target segmentation submodule. To solve this problem, we introduce a new target segmentation approach, GTS, and a more generalized and robust extrinsic calibration framework, GTSCalib. The proposed GTSCalib is very suitable for practitioners looking for a robust and accurate target-based calibration without limits on the target’s pose or its surrounding environment and without the need for time-consuming parameter tuning.