The device's impressive operational lifespan in both indoor and outdoor settings was confirmed, with sensors configured in a variety of ways to assess concurrent concentration and flow levels. The low-cost, low-power (LP IoT-compliant) design was a consequence of a specifically engineered printed circuit board and firmware adapted for the controller's particular attributes.
New technologies, a byproduct of digitization, now permit advanced condition monitoring and fault diagnosis, aligning with the Industry 4.0 paradigm. Fault detection through vibration signal analysis, while widely discussed in the literature, often poses logistical challenges due to the high cost of equipment needed for hard-to-reach locations. This paper presents a solution for detecting broken rotor bars in electrical machines, leveraging machine learning techniques on the edge and classifying motor current signature analysis (MCSA) data. Employing a public dataset, the paper details the feature extraction, classification, and model training/testing procedures for three machine learning approaches, finally exporting the results to diagnose another machine. The Arduino, a cost-effective platform, is adopted for data acquisition, signal processing, and model implementation using an edge computing strategy. While a resource-constrained platform, small and medium-sized companies can still take advantage of this. Testing of the proposed solution on electrical machines at Almaden's Mining and Industrial Engineering School (UCLM) yielded positive outcomes.
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. The comparative analysis of leather, synthetic leather, and polymers is carried out in this work using the method of laser-induced breakdown spectroscopy (LIBS). LIBS now sees prevalent application in establishing a unique identifier for diverse materials. A comprehensive examination of animal leathers, processed using vegetable, chromium, or titanium tanning agents, was conducted in conjunction with polymers and synthetic leathers, which were collected from several sources. Spectra indicated the presence of the characteristic spectral fingerprints of tanning agents (chromium, titanium, aluminum), dyes and pigments, and the polymer. The use of principal factor analysis allowed for the separation of samples into four main groups, each representing varying tanning procedures and the presence of polymer or synthetic leather.
The reliance of infrared signal extraction and evaluation on emissivity settings makes emissivity variations a significant limiting factor in thermography, impacting accurate temperature determinations. This paper presents a novel approach to emissivity correction and thermal pattern reconstruction within eddy current pulsed thermography. The method relies on physical process modeling and the extraction of thermal features. To improve the reliability of identifying patterns in thermography, an algorithm for correcting emissivity is proposed, considering spatial and temporal domains. The primary novelty of this method is that the thermal pattern's correction is enabled by the average normalization of thermal characteristics. The proposed method, when applied in practice, results in improved fault detectability and material characterization, independent of object surface emissivity changes. Experimental studies, including analyses of heat-treated steel case depth, gear failures, and gear fatigue in rolling stock applications, validate the proposed technique. The proposed technique for thermography-based inspection methods allows for improved detectability and efficiency, specifically advantageous for high-speed NDT&E applications like rolling stock inspections.
We present, in this paper, a new 3D visualization method for objects far away in low-light conditions. Conventional three-dimensional image visualization methods may result in poor image quality, specifically for objects at long distances that possess low resolution. Accordingly, our proposed methodology employs digital zoom to achieve a process of cropping and interpolating the region of interest from the image, ultimately elevating the quality of three-dimensional images taken from a distance. Three-dimensional imaging of distant objects might be difficult under conditions of photon scarcity. Employing photon-counting integral imaging can resolve this, but remote objects may retain a limited photon presence. Utilizing photon counting integral imaging with digital zooming, a three-dimensional image reconstruction is facilitated within our methodology. see more To estimate a more accurate three-dimensional image at significant distances in photon-scarce scenarios, multiple observations using photon-counting integral imaging (N observations) are employed in this paper. Optical experiments, along with performance metric calculations, such as peak sidelobe ratio, are used to demonstrate the workability of our proposed methodology. Thus, our method contributes to a superior visualization of three-dimensional objects at long distances in photon-scarce situations.
Within the manufacturing industry, there is notable research interest focused on weld site inspection. Using the acoustics of the weld site, this study demonstrates a digital twin system for welding robots, aimed at inspecting various potential weld flaws. An additional step involving wavelet filtering is employed to eliminate the acoustic signal originating from machine noise. see more Subsequently, an SeCNN-LSTM model is deployed to identify and classify weld acoustic signals based on the characteristics of robust acoustic signal time series. Analysis of the model's verification showed its accuracy to be 91%. In conjunction with several indicators, a comparative study of the model was conducted, involving seven distinct models, namely CNN-SVM, CNN-LSTM, CNN-GRU, BiLSTM, GRU, CNN-BiLSTM, and LSTM. Acoustic signal filtering and preprocessing techniques, coupled with a deep learning model, are fundamental components of the proposed digital twin system. This work aimed to develop a systematic, on-site approach to identify weld flaws, incorporating data processing, system modeling, and identification techniques. Furthermore, our suggested approach might function as a valuable asset for pertinent research endeavors.
The channeled spectropolarimeter's Stokes vector reconstruction accuracy is hampered by the optical system's phase retardance (PROS). PROS's in-orbit calibration is made difficult by the need for reference light having a specific polarization angle and the instrument's susceptibility to environmental factors. We, in this work, advocate for an instantaneous calibration method using a straightforward program. To precisely acquire a reference beam with a distinct AOP, a monitoring-focused function has been created. The utilization of numerical analysis allows for high-precision calibration, obviating the need for an onboard calibrator. The effectiveness and anti-interference capabilities of the scheme are substantiated by both simulations and experiments. Research employing a fieldable channeled spectropolarimeter indicates that the reconstruction accuracies of S2 and S3 are 72 x 10-3 and 33 x 10-3, respectively, within the complete wavenumber spectrum. see more The scheme is designed to fundamentally streamline the calibration process, thereby ensuring the high-precision calibration of PROS remains unperturbed by the orbital environment.
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. The procedure of 3D segmentation in the past relied on hand-crafted features and design approaches, but these methods exhibited a lack of generalizability to large data sets and fell short in terms of achieving acceptable accuracy. Deep learning methods have become the go-to approach for 3D segmentation jobs due to their impressive track record in 2D computer vision. We propose a CNN-based 3D UNET method, which is modeled on the acclaimed 2D UNET, for segmenting volumetric image data. To analyze the internal modifications of composite materials, such as a lithium-ion battery's composition, the flow of disparate materials, the identification of their directional movement, and the assessment of intrinsic characteristics are indispensable. For microstructure analysis of publicly available sandstone datasets, this paper introduces a multiclass segmentation technique based on a hybrid 3D UNET and VGG19 model. Image data from four distinct object types within the volumetric samples is examined. The 3D volumetric data from our image sample is derived by aggregating 448 two-dimensional images into a single volume. The process of finding a solution involves segmenting each object contained within the volumetric data, subsequently performing a thorough analysis of each segmented object to evaluate metrics such as average size, percentage of area, and total area, among others. Individual particle analysis is further facilitated by the IMAGEJ open-source image processing package. This study showcased the ability of convolutional neural networks to accurately identify sandstone microstructure traits, achieving 9678% accuracy and a 9112% Intersection over Union. Previous research, as far as we are aware, has predominantly employed 3D UNET for segmentation; however, only a handful of publications have advanced the application to showcase the detailed characteristics of particles within the specimen. A superior solution, computationally insightful, is proposed for real-time application, surpassing existing state-of-the-art methods. For the creation of a structurally similar model for the microscopic investigation of volumetric data, this result carries considerable weight.