Newsfeeds

Measurement Science and Technology - latest papers

Latest articles for Measurement Science and Technology

IOPscience

  • Identification of compound faults of rolling bearing based on envelope-cross-correlation and improved 1D-LBP
    Faults in bearings are often represented as compound faults in practical engineering. It is far more difficult to extract compound faults than single ones, and the influence which is from component signals irrelevant to fault, and transient impulses caused by the defective bearing are likely to be overwhelmed; this challenges the exact judgment of compound faults of bearings. 1D local binary pattern (1D-LBP) can extract hidden information from the perspective of local features. However, the quantization of signals according to 1D-LBP is vulnerable to noise and the representation of information is not deep enough. To solve this problem, a bearing fault feature extraction method has been proposed, which combines envelope-cross-correlation and improved 1D-LBP. Firstly, the cross-correlation function of envelope signal of vibration signals detected by two sensors is calculated to blend signals; this solves the problem of incomplete fault information contained a single sensor while reducing the noise and highlighting fault features. Secondly, before quantization of the signal, the feature rule for vibration signals in bearing faults is considered and the local skewness (in place of local central values) of data in the window is taken as a criterion for quantization and the study of improved 1D-LBP is used to solve the problem of incomplete representation of feature information in classical 1D-LBP. Thirdly, through quantization of the cross-correlation function (rather than the original vibration signal) according to the improved 1D-LBP, the problem of inexact quantization of 1D-LBP caused by noise has been solved. Finally, a bearing fault is identified by spectrum analysis of autocorrelation function of reconstructed signals after quantization. Through analysis of bearing vibration signals from different compound faults and the comparison of proposed and other classical methods based on the same data, the effectiveness and advantages of the proposed method are verified.

  • MSFF-CBR: case-based reasoning technology for adaptive multi-information fusion fault diagnosis
    With the transmission of massive, high-dimensional, low-value density data, measurement systems are able to capture extensive multi-sensor data. However, challenges such as high-dimensional incompatibility and granularity destruction pose significant issues for existing multi-sensor fusion theories. In this study, case-based reasoning (CBR) is applied to fault diagnosis using multi-information fusion. MSFF-CBR, a two-layer information fusion reasoning system embedding a multi-sensor feature fusion layer (MSFF), was designed. By incorporating the novel F2-Apriori algorithm and an attribute importance measurement model based on the multi-granulation rough set model, MSFF demonstrates exceptional feature fusion efficiency. In the decision fusion layer, three distance-based similarity measurement modes were developed to enable case retrieval and demonstrate the adaptability of MSFF to various sensitivity metrics. The model exhibits efficient multi-sensor information fusion for fault diagnosis under various operating conditions of multi-stage reciprocating compressor.

  • Fiber-optic liquid level sensor based on a peanut-shaped Mach–Zehnder interferometer and random forest network
    Liquid level monitoring ensures precise control of liquid level changes, which is crucial for preventing accidents, ensuring production safety, and maintaining stable system operation. In this work, we theoretically proposed and experimentally demonstrated the integration of a Mach–Zehnder interferometer (MZI) based on cascaded peanut-shaped fiber structures with a random forest network for liquid level monitoring. Because of the phase difference arising during the transmission process between core mode and cladding modes, changes in the liquid level sensed by the sensor result in corresponding changes in the phase difference, subsequently causing a shift in the transmission spectrum. The experimental results show that within the range of 0 mm to 25 mm, the sensitivity of the sensor is 0.084 nm mm−1, with a surprisingly high mean squared error (MSE) of 3.826. The significant error limits its practical application potential. To improve upon the high MSE, a random forest network was introduced for data optimization and training. By constructing multiple decision trees and using their predictions to obtain a final liquid level prediction through voting or averaging, the accuracy and robustness of the model were enhanced, improving the MSE to 0.000355, which represents a reduction in error by over 10 000 times. The designed sensing system holds promise for potential applications in soil moisture systems and liquid level detection in tank trucks. Additionally, the random forest network employed in this system demonstrates universal applicability within point-type optical fiber sensing systems which is possible to be widely utilized.

  • Development and research of a novel seam tracking sensor based on active acoustic signal
    The quality of weld seam tracking in automatic welding directly affects the welding quality and production efficiency. The weld seam tracking sensors commonly used in engineering are mainly based on laser vision and arc. These sensors are easily interfered with by factors such as arc light, spatter, and workpiece mirror reflection, which reduces the real-time tracking effect. Base on the above, in order to improve the accuracy and stability of weld seam tracking, a novel sensor based on an active acoustic signal is developed. The sensor detects differential changes in the active acoustic signal at various groove positions to identify and correct the welding torch’s position in real-time. The frequency response characteristics of the sensor are simulated and studied. The results show that sensor has the highest sensitivity to weld deviation detection when the frequency of the active acoustic signal is 14.4 kHz. The experimental results also verify the effectiveness of the research. The V-groove weld deviation experiment is conducted, and the mean amplitude, standard deviation, and short-time energy features of the active acoustic signal under 0-4 mm deviations are collected and extracted. The analysis shows that the standard deviation feature is the most sensitive to the deviation identification. The weld deviation identification model is established based on the standard deviation, and the V-groove weld seam tracking experiment is conducted. The results show that the deviation is within ±0.5 mm in 74% of cases and within ±0.9 mm in 93% of cases, indicating that the active acoustic signal sensor can achieve reliable tracking, providing a new solution for welding seam tracking.

  • DMS-SLAM: semantic visual SLAM based on deep mask segmentation in dynamic environments
    Visual simultaneous localization and mapping (VSLAM), which is a core technology for intelligent mobile robots, is typically based on static environment assumptions. However, it encounters challenges in dynamic scenes such as accurately distinguishing between static and dynamic feature points and high computational costs. These issues lead to significant deviations in camera tracking and introduce ghosting in mapping. To address these issues, we propose DMS-SLAM, a fast semantic visual SLAM based on deep mask segmentation. First, semantic segmentation is performed using the YOLOv8s-seg network, followed by a mask correction algorithm based on depth consistency and a missed detection compensation algorithm based on relative displacement invariance, ensuring precise segmentation of dynamic objects within the semantic thread. Then, the segmented objects are classified according to dynamic probability, and the motion of potential dynamic objects is determined through a depth mask association algorithm. Dynamic feature points are eliminated by utilizing epipolar geometric constraints to improve localization accuracy. Finally, local point clouds with dynamic objects removed are stitched together to construct a global dense point cloud map, presenting a static scene free of ghosting. Experiments on the Technical University of Munich RGB-D dataset and in real-world scenarios show that the method enhances absolute trajectory accuracy by an average of 96.19% and relative pose accuracy by 94.27% over the ORB-SLAM2 system in highly dynamic environments. Compared to other dynamic semantic visual SLAM algorithms, DMS-SLAM improves operational efficiency by over 51.78%, significantly reducing runtime and generating clear and accurate global 3D point cloud maps, which fully demonstrates its superiority in terms of accuracy, stability and efficiency.