Dot Patterns Research Articles

A Fault Diagnosis Method for Rotating Machinery With Semi-Supervised Graph Convolutional Network and Images Converted From Vibration Signals

In recent years, intelligent fault diagnosis has been widely investigated. Deep learning fault diagnosis methods for rotating machinery are primarily based on the assumption that labeled data are abundant. However, it is difficult to collect sufficient labeled samples for some practical engineering applications. To address this problem, a fault diagnosis method for rotating machinery with semi-supervised graph convolutional network (SSGCN) and two-dimensional (2-D) images converted from vibration signals is proposed in this paper. First, the one-dimensional (1-D) vibration signals are converted into 2-D images, by the no-parameter adjacency symmetrized dot pattern (NPASDP) method in polar coordinate system and the grayscale matrix mapping (GMM) method in Cartesian coordinate system. The local features of all samples are fully extracted using image processing methods in different coordinate systems to better characterize the relationship between different samples. Second, the normalized cross-correlation coefficient (NCC) is used to calculate the images similarity. Using each sample as a node, the graph structure is constructed by judging the adjacency relationship between all nodes through the similarity threshold. Finally, the SSGCN model is established to realize fault diagnosis. A bearing dataset and a rotor dataset with few data labels are used for validation, and the proposed method can get satisfactory fault diagnosis accuracy. The results show that the method can effectively improve the accuracy of model fault diagnosis with few labeled data.

Ocular following Eye Movements in Marmosets Follow Complex Motion Trajectories.

Ocular following eye movements help stabilize images on the retina and offer a window to study motion interpretation by visual circuits. We use these ocular following eye movements to study motion integration behavior in the marmosets. We characterize ocular following responses in the marmosets using different moving stimuli such as dot patterns, gratings, and plaids. Marmosets track motion along different directions and exhibit spatial frequency and speed sensitivity, which closely matches the sensitivity reported in neurons from their motion-selective area MT. Marmosets are also able to track the integrated motion of plaids, with tracking direction consistent with an intersection of constraints model of motion integration. Marmoset ocular following responses are similar to responses in macaques and humans with certain species-specific differences in peak sensitivities. Such motion-sensitive eye movement behavior in combination with direct access to cortical circuitry makes the marmoset model well suited to study the neural basis of motion integration.

Additively Manufactured 2D Matrix Code Direct Part-Marking Casting Requirements

Direct part-markings (DPMs) can be formed into metal castings, using additively manufactured two-dimensional matrix encoded tags (AM2D) placed in a sand or shell mold. It has been unclear how thin a part can be and yet form a readable DPM. There must be sufficient molten metal to burn away the tag, and sufficient feeding pressure to form the 2D matrix code dot pattern. Here the formability limit for the casting of any AM2D (polymer) tag is shown to be the smallest heat energy from the latent heat of condensation needed to raise the temperature of the tag to ignition to burn the tag from the mold. The minimal part thickness that can be utilized is thereby derived. The minimum thickness is calculated to predict a part of various materials and compared positively with experiments. This provides a means to compute required part metal thickness to positively form a DPM tag before casting.

Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks

Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks

Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation

A fault diagnosis method for the rotating machinery based on improved Convolutional Neural Network (CNN) with Gray-Level Transformation (GLT) is proposed to increase the accuracy of the recognition adopting the multiple sensors. The Symmetrized Dot Pattern (SDP) in this method is applied to fuse the data of the multiple sensors, and the multi-color value method is adopted to increase the feature dimension. The grayscale and GLT are used to reduce the dimension of the SDP image. The SDP grayscale image is finally input to the CNN network for training recognition. The research results show that the diagnosis accuracy of the rolling bearing system based on the novel method is up to 98.6 %. Compared with the method without the multi-color value and GLT, the recognition accuracy of the proposed method is improved by 22.3 %, and the training time is reduced by about one third. The research work reveals that the developed method has the potential application value under the multi-sensor working conditions for the fault diagnosis.

Multisensor-Driven Motor Fault Diagnosis Method Based on Visual Features

Generalization ability is a critical property for practical motor fault diagnosis (FD). By converting time-series to images, several studies have made certain achievements. However, they still have following limitations. First, multisensor information fusion is rarely considered. Second, it is time consuming. To deal with the abovementioned problems, a multisensor-driven FD method based on visual features is proposed. Specifically, a color symmetrized dot pattern method is newly designed to infuse three multisensor signals to image. Next, a coarse and refined diagnosis framework is designed. In the coarse part, the color histogram features and a support vector machine (SVM) are utilized, and a threshold is selected to decide the coarse diagnostic samples. In the refined part, the gist (GIST) descriptor and another SVM are used to diagnose remaining samples. The results on induction motor and permanent magnet synchronous motor show that the proposed method achieved reliable diagnosis with relatively efficiency, and can generalize to different working conditions and noise.

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Using Magnetic Leakage Signals

In most industrial applications, it is difficult to obtain complete demagnetization fault signals of all conditions with labels for permanent magnet synchronous motor (PMSM), and motors are not allowed to be disassembled, so non-contact diagnostic methods are essential. A non-contact fault diagnosis method using magnetic leakage signal based on wavelet scattering convolution network (WSCN) and semi-supervised deep rule-based (SSDRB) classifier is proposed. Through magnetic equivalent circuit model analysis, the magnetic leakage signal on motor surface is selected as fault signal. To avoid complex signal processing, the symmetrized dot pattern method is introduced to convert fault signals into two-dimensional images. Then, WSCN is applied to extract features from images, and SSDRB classifier is adopted to diagnose demagnetization fault. Finally, faulty motor prototypes are manufactured for experiment. By comparing with other methods, the superiority and effectiveness of the proposed method using a small number of labeled samples under different conditions are verified.

SARS-CoV-2 viral RNA detection using the novel CoVradar device associated with the CoVreader smartphone app

The COVID-19 pandemic has highlighted the need for innovative approaches to its diagnosis. Here we present CoVradar, a novel and simple colorimetric method that combines nucleic acid analysis with dynamic chemical labeling (DCL) technology and the Spin-Tube device to detect SARS-CoV-2 RNA in saliva samples. The assay includes a fragmentation step to increase the number of RNA templates for analysis, using abasic peptide nucleic acid probes (DGL probes) immobilized to nylon membranes in a specific dot pattern to capture RNA fragments. Duplexes are formed by labeling complementary RNA fragments with biotinylated SMART bases, which act as templates for DCL. Signals are generated by recognizing biotin with streptavidin alkaline phosphatase and incubating with a chromogenic substrate to produce a blue precipitate. CoVradar results are analysed by CoVreader, a smartphone-based image processing system that can display and interpret the blotch pattern. CoVradar and CoVreader provide a unique molecular assay capable of detecting SARS-CoV-2 viral RNA without the need for extraction, preamplification, or pre-labeling steps, offering advantages in terms of time (∼3 h/test), cost (∼€1/test manufacturing cost) and simplicity (does not require large equipment). This solution is also promising for developing assays for other infectious diseases.

Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Improved Canberra Distance in Symmetrized Dot Pattern Image

Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Improved Canberra Distance in Symmetrized Dot Pattern Image

A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern

Motor plays a core role in most industrial equipment. Accurate fault diagnosis of motor is a critical task and intelligent data-driven methods have gained significant advances. However, to obtain sufficient labeled data to train the models is expensive and laborious in industrial applications, and how to utilize three-phase current signals efficiently is a challenging task. To deal with these problems, a digital twin-enhanced semi-supervised framework is proposed for label-scarce motor fault diagnosis. First, a precise motor digital twin model is established based on multi-physics simulation and knowledge transfer is performed from the virtual space to the physical space. Second, a novel phase-contrastive current dot pattern (PCCDP) representation is proposed to transform three-phase motor stator current to a gray-scale image with an ordered arrangement and then characteristics of three phases can be contrasted in tight regions for efficient processing. Third, inter-space sample generation is proposed for continuous feature manifold learning to tackle discrepancy between spaces. Finally, intra-space sample generation and a clustering-based metric learning are also introduced to improve semi-supervised fault diagnosis performance. An induction motor fault experiment is conducted and a digital twin model is built correspondingly. Experiments verify the effectiveness and superiority of the proposed framework.

Digitization, Visualization and Accessibility of Globe Virtual Collection: Case Study Jüttner’s Globe

The aim of the article is to prepare a model for making available metadata and digital objects of the new Globe Virtual Collection for the Map Collection of the Faculty of Science of Charles University. The globes are special cartographic documents; therefore, they are also described in a special way. The article deals with the digitization, visualization and accessibility of an old globe by Josef Jüttner from 1839, which comes from the depository of one of the most important central European collections. A simple model for a new virtual processing of the globe collection at Charles University is presented. SfM-MVS photogrammetry was chosen for digitization of the globe. The basic elements of the copperplate were set as basic parameters for image acquisition. Contrast, density, black line, line, dash and dot patterns and their complex use were observed for a good graphic design of the globe. Other parameters included a closer determination of the users for whom the resulting product is intended, as well as the profile of the users’ behavior on the site so far. New metadata were extracted from the bibliographic description. The virtual 3D globe was integrated into the database using the Cesium JavaScript library. Metadata and a 3D model of the globe were linked together and made available to the general public on the Globe page of the Map Collection of the Faculty of Science of Charles University. A comparison of web browsers was performed focusing on the loading time of the 3D model on the website. New graphic elements were identified with the new processing. It was possible to read the factual information written on the globe. Different possibilities and limitations of metadata description, photogrammetric methods and web presentation are described. This good practice can be applied by other virtual map collections.

A variable-speed-condition bearing fault diagnosis methodology with recurrence plot coding and MobileNet-v3 model.

To improve the quality of the non-stationary vibration features and the performance of the variable-speed-condition fault diagnosis, this paper proposed a bearing fault diagnosis approach with Recurrence Plot (RP) coding and a MobileNet-v3 model. 3500 RP images with seven fault modes were obtained with angular domain resampling technology and RP coding and were input into the MobileNet-v3 model for bearing fault diagnosis. Additionally, we performed a bearing vibration experiment to verify the effectiveness of the proposed method. The results show that the RP image coding method with 99.99% test accuracy is superior to the other three image coding methods such as Gramian Angular Difference Fields, Gramian Angular Summation Fields, and Markov Transition Fields with 96.88%, 90.20%, and 72.51%, indicating that the RP image coding method is more suitable for characterizing variable-speed fault features. Compared with four diagnosis methods such as MobileNet-v3 (small), MobileNet-v3 (large), ResNet-18, and DenseNet121, and two state-of-the-art approaches such as Symmetrized Dot Pattern and Deep Convolutional Neural Networks, RP and Convolutional Neural Networks, it is found that the proposed RP+MobileNet-v3 model has the best performance in all aspects with diagnosis accuracy, parameter numbers, and Graphics Processing Unit usage, overcoming the over-fitting phenomenon and increasing the anti-noise performance. It is concluded that the proposed RP+MobileNet-v3 model has a higher diagnostic accuracy with fewer parameters and is a lighter model.

3D single shot lensless incoherent optical imaging using coded phase aperture system with point response of scattered airy beams

Interferenceless coded aperture correlation holography (I-COACH) techniques have revolutionized the field of incoherent imaging, offering multidimensional imaging capabilities with a high temporal resolution in a simple optical configuration and at a low cost. The I-COACH method uses phase modulators (PMs) between the object and the image sensor, which encode the 3D location information of a point into a unique spatial intensity distribution. The system usually requires a one-time calibration procedure in which the point spread functions (PSFs) at different depths and/or wavelengths are recorded. When an object is recorded under identical conditions as the PSF, the multidimensional image of the object is reconstructed by processing the object intensity with the PSFs. In the previous versions of I-COACH, the PM mapped every object point to a scattered intensity distribution or random dot array pattern. The scattered intensity distribution results in a low SNR compared to a direct imaging system due to optical power dilution. Due to the limited focal depth, the dot pattern reduces the imaging resolution beyond the depth of focus if further multiplexing of phase masks is not performed. In this study, I-COACH has been realized using a PM that maps every object point into a sparse random array of Airy beams. Airy beams during propagation exhibit a relatively high focal depth with sharp intensity maxima that shift laterally following a curved path in 3D space. Therefore, sparse, randomly distributed diverse Airy beams exhibit random shifts with respect to one another during propagation, generating unique intensity distributions at different distances while retaining optical power concentrations in small areas on the detector. The phase-only mask displayed on the modulator was designed by random phase multiplexing of Airy beam generators. The simulation and experimental results obtained for the proposed method are significantly better in SNR than in the previous versions of I-COACH.

Single-shot 3D shape measurement based on RGB dot patterns and stereovision: retraction.

The referenced article [Opt. Express30, 28220 (2022)10.1364/OE.466148] has been retracted by the authors.

Dermoscopic Prediction of Therapeutic Response in Psoriatic Lesions Treated with Methotrexate and Apremilast: An Observational Study on Skin Color

Background: Psoriasis is a chronic auto-inflammatory condition. Treatment modalities include methotrexate (MTX) and apremilast (APL). Although the efficacy of MTX and APL is well studied, evidence of their superiority of one over the other is lacking. Dermoscopy is helpful in diagnosis and treatment evaluation. Therefore, dermoscopy is utilized to assess the treatment response. Objectives: This study aimed to evaluate dermoscopic assessment in psoriatic lesions treated by MTX and APL and compare their efficacy by calculating the respective Psoriasis Area and Severity Index (PASI) scores and study the effect of MTX and APL on different dermoscopic patterns in psoriasis at different levels of treatment. Methods: This randomized interventional comparative study was conducted in a tertiary care hospital. A total of 98 patients were randomly divided into two groups of 50 and 48 patients, given oral/intramuscular MTX 7.5 mg once weekly and APL 30 mg twice daily for 3 months, respectively. The patients were assessed clinically and dermoscopically using a videodermoscope, and comparisons were performed between the two groups. The PASI score was calculated and evaluated. Results: The PASI score was reduced by 83% and 65% from the baseline in MTX and APL groups, respectively. The patients with patchy (61.5%) and minimal (23%) patterns of vessels and red dots (92.3%) showed faster achievement of PASI-100. However, the patients with a regular distribution of vessels and globular vessels failed to obtain PASI-100, suggesting a poor response to treatment. Conclusions: Vascular structures play a significant role in the diagnosis and prediction of the treatment response. Particular patterns and type vessels under dermoscopy can give a hint about choosing appropriate drugs for psoriasis.

Combination of Digital Image Correlation (DIC) and in situ 3D-μ-CT in the analysis of the relationship between strains and porosity under creep loading

Abstract In situ 3D μ-XCT allows the time and space resolved measurement and analysis of material damage in the component volume, whereas the Digital Image Correlation (DIC) is a 2D method for the analysis of deformation measured on the surfaces of components. In situ 3D μ-XCT measurements were performed on cylindrical specimen made of SiC particle reinforced titanium MMC (MMC: Metal Matrix Composite) (15 % SiC particles) during creep load. The formation and evolution of voids were subsequently analyzed. Due to the rotationally symmetric sample geometry, the analysis of the deformation in the interior of the material by DIC using 2D slices is possible and evident. The dot pattern required to calculate the strain using DIC (speckle pattern) is provided by the intrinsic particle reinforcement of the MMCs. Temporally and locally changing and time-variant strain fields in both the tensile as well as the compressive range could be detected correlating with void formation and development area.

Research on Fault Diagnosis of Wind Turbine Gearbox with Snowflake Graph and Deep Learning Algorithm

Wind power generation is one of the important development projects for renewable energy worldwide. As wind turbines operate in harsh environments, failure of the wind turbines often occurs, thus leading to lower power generation efficiency and high maintenance cost. Earlier detection of the fault type can reduce the maintenance cost. This study proposed a hybrid recognition algorithm based on the symmetrized dot pattern (SDP) and convolutional neural network (CNN) for wind turbine gearbox fault diagnoses. In addition to a fault-free type, four fault types were discussed in this paper, including gear rustiness, broken tooth, wear, and aging. A vibration sensor was used for measurement. The original vibration signals of the gearbox were captured by a NI-9234 high-speed data acquisition card, filtered by a fast Fourier transform, and imported into the SDP to create the snowflake image features. Afterward, CNN diagnosed the gearbox fault type. There were 1500 test data in this study. A total of 200 data items for each fault type were used as training samples, and 100 data of each type were used as test samples. The test result shows that the training accuracy was 98.8%. The proposed method can diagnose the fault condition of the gearbox effectively and identify the fault type of the gearbox accurately. This is favorable for the quick maintenance of wind turbines.

A Novel Fault Feature Selection and Diagnosis Method for Rotating Machinery With Symmetrized Dot Pattern Representation

Fault diagnosis methods based on machine learning have made great progress for rotating machinery. The main steps of the machine learning process involve feature extraction, selection, and classification. Feature selection improves classification accuracy and reduces diagnosis time by selecting the better features. Due to the difficulty of traditional feature selection methods to rank the feature importance of each class, the best subset of features could hardly be obtained. Therefore, this article proposes a new feature selection method to address the shortcomings of the above traditional methods, called Feature Ranking based on Optimal Class Distance Ratio (FROCDR), which can choose the optimal features between every two classes of samples to obtain feature ranking that is conducive to classification. In order to comprehensively extract the fault information in the signal, the multiscale analysis and the variational mode decomposition (VMD) method are applied to process the vibration signals under different scales and frequency bands, and the processed signals are visualized by symmetrized dot pattern (SDP). In addition, features are extracted from the obtained SDP images, and the proposed FROCDR method is used to select the best subset of features. The final diagnosis task is accomplished by a random forest (RF) classifier. Experimental cases of bearing and gear data show that the proposed method has higher diagnostic accuracy and stability.

Dots, circles and horses: New rock art evidence through image-based digital methods in Moro Cave (Tarifa, Spain)

In the mid-1990s, Moro Cave rock-shelter became a key site to understand the rock art phenomenon in southernmost Iberia, as the discovery of Solutrean engravings settled former arguments about the presence of Upper Palaeolithic art in the region. Since then, discontinuous and unsystematic interventions in this site not only left many questions unanswered but also reduced its scientific relevance in broader reviews and discussions. In this paper, we highlight how current digital applications of image-based spatial methods are improving the study of rock art in Campo de Gibraltar and La Janda ancient lagoon area (Cádiz province, Spain).Here we present the first results of the introduction of ‘free and open-source’ image-based recording methods to research at Moro Cave, such as photogrammetric Structure from Motion, orthophotomosaics, microtopography, and statistical band-processing of raster imagery. As a result, spatial modelling of the rock-shelter, microtopography of panels, and the discovery of numerous unpublished motifs across the whole site provide promising insights for future research. This paper focuses on new faint and particular dotted painted patterns (spirals/concentric circles, alignments, and aggregations) related to the large horse on the lower floor; cultural parallels, Upper Palaeolithic adscription, taphonomic issues and future directions are discussed. As a conclusion, a Late Solutrean/Early Magdalenian chronology is proposed due to dot size and shape, arrangement and composition, comparison with other sites, and degree of degradation.

A Regular Pattern of Timestamps Between Machines with Built-in System Time

This paper studied the effect of 15.6 ms time resolution where the collected timestamps are in a form of parallel dotted lines, instead of one straight line like in classical case. The dotted lines made the clock skew measurement of two devices to become incorrect as the measurement which normally follow the cluster of offsets but now follow the parallel dotted lines. Dotted lines pattern is required in order to understand how to correct the clock skew measurement on data containing dotted lines. To model the dotted lines pattern is through Dotted lines Grouping Method, a tools to find the characteristics of the dotted lines. The dotted lines grouping method was then tested data obtained from wired and wireless communication of two similar devices. The dotted line grouping method results equal maximum number of dot of 10 for both data, which indicated the robustness of the dotted lines grouping method.