The device's extended indoor and outdoor usage was impressive. Sensors were configured in multiple ways to evaluate simultaneous concentration and flow rates. The low-cost, low-power (LP IoT-compliant) design was achieved via a custom printed circuit board and optimized firmware that matched the controller's particular characteristics.
Digitization's evolution has paved the way for new technologies, driving the precision of condition monitoring and fault diagnosis within the Industry 4.0 environment. Though vibration signal analysis is a prevalent method for fault identification in scholarly works, the process frequently necessitates the deployment of costly instrumentation in challenging-to-access areas. This paper proposes a solution for diagnosing electrical machine faults using edge-based machine learning techniques, applying motor current signature analysis (MCSA) to classify data for broken rotor bar detection. This paper investigates the processes of feature extraction, classification, and model training/testing for three different machine learning methods using a public dataset, with a concluding aim of exporting diagnostic results for a different machine. Employing an edge computing methodology, data acquisition, signal processing, and model implementation are carried out on an economical Arduino platform. Small and medium-sized companies can utilize this, but it's essential to acknowledge the platform's limited resources. Positive results were observed in the testing of the proposed solution on electrical machines at the Mining and Industrial Engineering School of the UCLM in Almaden.
Genuine leather, produced by chemically treating animal hides, often with chemical or vegetable agents, differs from synthetic leather, which is constructed from a combination of fabric and polymers. Identifying the difference between natural and synthetic leather is becoming a more challenging endeavor, fueled by the growing adoption of synthetic leather. Laser-induced breakdown spectroscopy (LIBS) is assessed in this investigation to differentiate between leather, synthetic leather, and polymers, which are very similar materials. LIBS is currently extensively employed in producing a distinguishing signature for varied materials. Animal hides, tanned with vegetable, chromium, or titanium agents, were jointly examined with diverse polymers and synthetic leather materials. Signatures from tanning agents (chromium, titanium, aluminum) and dyes/pigments were present in the spectra, coupled with characteristic absorption bands stemming from the polymer. Four clusters of samples were identified using principal factor analysis, each exhibiting distinct characteristics associated with different tanning methods and whether they were polymer or synthetic leather.
Inaccurate temperature readings in thermography are frequently attributed to emissivity fluctuations, since infrared signal processing relies on the precise emissivity values for reliable temperature estimations. This paper details a thermal pattern reconstruction and emissivity correction technique, rooted in physical process modeling and thermal feature extraction, specifically for eddy current pulsed thermography. A new algorithm for adjusting emissivity is designed to resolve difficulties with pattern recognition in thermographic observations over both space and time. The innovative aspect of this approach lies in the capacity to adjust the thermal pattern using the average normalization of thermal characteristics. The proposed methodology practically improves fault detection and material characterization, independent of emissivity variations on the object's surfaces. The proposed methodology has been confirmed through experimental studies encompassing case-depth evaluations of heat-treated steels, examinations of gear failures, and fatigue assessments of gears utilized in rolling stock. By employing the proposed technique, thermography-based inspection methods exhibit increased detectability and a resulting improvement in inspection efficiency, particularly valuable for high-speed NDT&E applications, such as those concerning rolling stock.
We present, in this paper, a new 3D visualization method for objects far away in low-light conditions. Visualizing three-dimensional objects using traditional methods might yield diminished quality, especially for distant objects that display a reduced level of resolution. Consequently, our method employs digital zoom, enabling the cropping and interpolation of the region of interest from the image, thereby enhancing the visual fidelity of three-dimensional images viewed from afar. When photon levels are low, three-dimensional imagery at long ranges may not be possible because of the shortage of photons. To resolve this, one can utilize photon counting integral imaging, despite the possibility of a limited photon count for distant objects. Due to the implementation of photon counting integral imaging with digital zooming, a three-dimensional image reconstruction is feasible in our approach. this website This paper employs multiple observation photon-counting integral imaging (N observations) to achieve a more accurate three-dimensional image reconstruction at long distances, especially in low-light environments. Our optical experiments and calculation of performance metrics, including peak sidelobe ratio, demonstrated the practicality of our suggested approach. As a result, our method can improve the visualization of three-dimensional objects located at long distances under circumstances with a dearth of photons.
Welding site inspection is a focal point for research efforts in the manufacturing industry. This study showcases a digital twin system for welding robots, which analyzes weld site acoustics to evaluate a range of possible weld defects. A wavelet filtering method is also implemented to remove the acoustic signal originating from machine noise sources. this website The application of an SeCNN-LSTM model allows for the recognition and categorization of weld acoustic signals, drawing upon the characteristics of robust acoustic signal time sequences. Analysis of the model's verification showed its accuracy to be 91%. In addition to employing numerous metrics, the model was evaluated alongside seven alternative models: CNN-SVM, CNN-LSTM, CNN-GRU, BiLSTM, GRU, CNN-BiLSTM, and LSTM. The digital twin system proposed here integrates deep learning models and acoustic signal filtering and preprocessing techniques. This study sought to create a systematic framework for on-site weld flaw detection, involving data processing, system modeling, and identification strategies. Our suggested method, in addition, could be a substantial resource for researchers pursuing pertinent research topics.
In the channeled spectropolarimeter, the accuracy of Stokes vector reconstruction is fundamentally constrained by the optical system's phase retardance (PROS). The in-orbit calibration of PROS faces obstacles due to its dependence on reference light with a specific polarization angle and susceptibility to environmental disturbances. This research introduces a simple-program-driven instantaneous calibration scheme. A function, tasked with monitoring, is developed to precisely acquire a reference beam possessing a predefined AOP. Numerical analysis facilitates high-precision calibration, eliminating the need for an onboard calibrator. The scheme's resistance to interference and overall effectiveness are clearly demonstrated in the simulation and experimental results. Our fieldable channeled spectropolarimeter research demonstrates that S2 and S3 reconstruction accuracy across the entire wavenumber spectrum are 72 x 10-3 and 33 x 10-3, respectively. this website A core aspect of this scheme is the simplification of the calibration program, preventing interference from the orbital environment on the high-precision calibration of PROS.
3D object segmentation, a cornerstone but intricate concept in computer vision, offers applications in medical image processing, autonomous vehicle technology, robotic control, the design of virtual reality environments, and analysis of lithium-ion battery images, among other areas. Hand-made features and design methods were used in previous 3D segmentation, however, they were unable to extend their application to sizable data or obtain acceptable accuracy levels. The superior performance of deep learning algorithms in 2D computer vision has led to their prevalent use for 3D segmentation tasks. Our proposed method utilizes a CNN-based 3D UNET architecture, informed by the well-regarded 2D UNET, for segmenting volumetric image data. To comprehend the interior alterations of composite materials, for instance, inside a lithium battery cell, it is essential to visualize the transference of different materials, study their migratory paths, and scrutinize their intrinsic properties. This research leverages a combined 3D UNET and VGG19 approach for multiclass segmentation of publicly available sandstone datasets, enabling analysis of microstructures using image data from four different sample categories in volumetric datasets. Forty-four-eight two-dimensional images from our sample are computationally combined to create a 3D volume, facilitating examination of the volumetric dataset. By segmenting each object within the volume data, a solution is established, and a subsequent analysis is carried out on each object to determine its average size, area percentage, total area, and other pertinent details. The IMAGEJ open-source image processing package is instrumental in the further analysis of individual particles. Through the application of convolutional neural networks, this study demonstrated the capability to accurately identify sandstone microstructure traits, attaining an accuracy of 9678% and an IOU of 9112%. To our knowledge, many previous works have applied 3D UNET for segmentation purposes, but few investigations have extended this approach to explicitly illustrate the detailed structures of particles within the specimen. A computationally insightful solution for real-time use is proposed and found to be superior to the current state-of-the-art methods in place. This result is of pivotal importance for constructing a roughly similar model dedicated to the analysis of microstructural properties within three-dimensional datasets.