Method For Diagnosis Research Articles

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Abstract Considering the issue of capturing the local and global contextual information and enhancing the parallel capability of bearing fault diagnosis in variable load and noise environments, a fault diagnosis method of rolling bearing based on PE-DCM and ViT is proposed. Firstly, the one-dimensional vibration signal is converted into a two-dimensional time-frequency diagram by continuous wavelet transform in the data processing module, and the model can understand the characteristics of the vibration signal more comprehensively. Secondly, a pyramid exponential expansion convolution module is established to extract the local features of fault information. Then, the global features of the fault information are learnt through the ViT (Vision Transformer) network, and the adaptive multi-attention is used to dynamically adjust the attention weights according to the features of the input data so as to inhibit noise or unimportant information. Finally, the experimental verification is carried out by using Case Western Reserve University and self-made MFS-bearing data set. The experimental results show that the method can better reflect the powerful image classification ability of the ViT network and has better noise resistance and generalization compared with other fault diagnosis methods.

A Method for Ocular Disease Diagnosis through Visual Prediction Explainability

Ocular diseases can range in severity, with some being more serious than others. As a matter of fact, there are several common and severe eye diseases, for instance, glaucoma, i.e., a group of eye conditions that damage the optic nerve, often associated with elevated intraocular pressure. Effective management and prevention strategies require a multifaceted approach, involving healthcare providers, public health officials and community education. Regular screenings and early interventions are crucial in reducing the impact of eye diseases on individuals and populations. In this paper, we propose a method aimed to detect the presence of ocular disease from the automatic analysis of eye fundus photographs. We consider deep learning; in detail, we adopt several convolutional neural networks aimed to train several models to be able to discriminate between different eye diseases. Furthermore, to boost the application of deep learning in real-world everyday medical practice, we adopt a method to understand which areas of the images are of interest from the model’s point of view; this allows us to detect disease by providing in this way disease localization by explainability. In the experimental analysis, we provide a set of four different experiments: in the first one, we propose a model to discern between age-related macular degenerations and normal fundus (obtaining an accuracy of 0.91); in the second one, the model is able to discriminate between cataract and normal fundus (obtaining an accuracy of 0.92); the third experiment is related to a model aimed to discriminate between glaucoma and normal ocular fundus (obtaining an accuracy of 0.88); and the last experiment is related to a model aimed to discern between pathological myopia and normal ocular fundus (obtaining an accuracy of 0.95). Thus, the experimental analysis confirms the effectiveness of the proposed method from a quantitative point of view (analysis aimed at understanding whether the model is able to correctly identify the disease) but also from a qualitative one, with a detailed and reasoned analysis aimed at understanding whether the model is able to correctly localize the disease.

Obtaining and Characterization of Experimental Diagnostic Brucellosis Rabbit Sera for the Development of a National Standard for Brucellosis Serum

Relevance. The production of brucellosis diagnosticums is currently being established in the medical and veterinary practice of the Republic of Uzbekistan. However, due to the lack of a national standard serum in the country and the high cost of analog standard serums on the market, problems may arise in the production of diagnostic tools. In addition, the lack of national standard serums causes problems in quality control of commercial and local antigens, hinders the development of local drugs – brucellosis diagnostics, which reduces the effectiveness of serological diagnosis of brucellosis in humans and animals. Given the above, it becomes necessary to develop and produce a standard serum with a high level of safety, which will improve the methods of laboratory diagnosis of various forms of brucellosis in patients and animals to identify hidden foci of infection in Uzbekistan. The aim of the study is to obtain and characterize experimental standard diagnostic brucellosis rabbit sera for the development of national and industry standard serum with appropriate titers. Materials and Methods. The experiments were carried out in full compliance with the European Council Directive 2010/63/EU on compliance with ethical principles in working with laboratory animals and the experiments were carried out on the basis of the methodological manual «Methods and rules of work» approved by the Ministry of Health of the Republic of Uzbekistan in 2016 with laboratory animals in experimental microbiological and immunological studies. To obtain positive standard sera against brucellosis pathogens, 12 rabbits weighing from 2.2 kg to 4.6 kg, aged 6 to 12 months, were used. The animals were kept under identical conditions on a standard vivarium diet. For the experiment, animals adapted to the conditions of the study were selected and kept in quarantine for 21 days. The maintenance and conduct of the studies followed international rules for the humane treatment of animals. Results and Discussion. During the evaluation of the heterologic specificity of the obtained sera for 23 collection and hospital strains, a weakly positive response (+) was observed in 16.7% of cases. Rabbit sera positive for heterologic specificity were adsorbed on suspensions of inactivated microorganisms (Citrobacter spp. and Enterobacteriaceae spp.) - no significant drop in titer was observed, and cross-agglutination was eliminated. A significant increase in the total protein level was noted after the first and fourth immunizations. Before the first immunization, the total protein level was 59.57 g/l, and after the fourth immunization, it increased threefold to 157.17 g/l. There was also an increase in albumin and globulin (33.2 g/L and 26.37 g/L, respectively) to 79.37 g/L and 77.43 g/L, respectively. IgM levels remained almost unchanged, increasing 3.5-fold after the second immunization and decreasing to 0.24 mg/ml after the fourth immunization. An increase in the level of IgG after the second and third immunizations was revealed, followed by a decrease after the fourth immunization. Conclusion. The obtained national and industry standard diagnostic polyvalent brucellosis sera, as well as the developed methods of laboratory control of its quality and efficiency testing will make it possible to produce candidate standard serum samples for agglutination reaction test and detection of Brucella strains. The obtained standard serum samples will be further used for control of the produced immunobiological diagnostic preparations and provision of unified requirements to their quality. The goal was achieved – improving the serological diagnosis of brucellosis, laying the foundation for further study and standardization of serum.

Expert consensus on the terminology, diagnostics and management of persisting symptoms after concussion with a focus on mental health, postural stability, electroencephalogram and balance testing: A cross-sectional Delphi-like survey

Background: Persisting symptoms after concussion (PSaC) are a pathological manifestation of head injuries that present with symptoms after the acute phase of head trauma has subsided. Insufficient research about PSaC has led to gaps in knowledge and incorrect terminology being applied. Furthermore, gaps exist in standardised assessment protocols and understanding of mental health symptoms associated with sports. Objectives: The study aimed to; 1) Determine expert consensus on appropriate terminology for symptoms lasting >4 weeks, 2) Investigate associations with mental health and postural stability symptoms, 3) Evaluate experts’ views on quantitative balance and electroencephalogram (EEG) testing. Methods: A Delphi-like survey was designed in REDCap and sent to identified experts in the field of sports-related concussions (SRC). Expert consensus was defined as ≥ 75% agreement. Results: Expert consensus identified the following mood and motor control symptoms being associated with PSaC: increases in emotional state (80%), irritability (87%), nervousness (87%), sadness (80%), balance impairment (80%), dizziness (87%) and feeling slow (80%). Numbness and tingling were not considered longer-term effects (80%). Additionally, 93% of respondents acknowledged mental health symptoms as potential longer-term effects, with 80% agreeing on inadequate current management. Respondents indicated PSaC are only somewhat adequately managed (73%) or not managed well enough (27%). The use of EEG and quantitative balance testing remains open for debate. The survey response rate was 21%. Conclusion: Improving mental health management for athletes with PSaC and standardising terminology is crucial. Future research is required to establish effective diagnosis and treatment methods. Addressing these issues may result in better care and safer return to play for athletes.

Nutritional Diagnosis of Potato Crops Using the Multivariate Method

The compositional nutrient diagnosis (CND) method considers the multiple relationships among nutrients and has been proposed to evaluate the nutritional status of plants in place of the univariate and bivariate methods. As it is mathematically based and considers the interactions among all nutrients at the same time, it avoids the errors and trends observed in the calculations of other methods estimating nutritional status, enabling a greater relationship with productivity. The objective of this study was to obtain the CND norms for high-yielding populations of potato crops. For this, 587 samples were used from 21 experimental areas in the state of São Paulo, Brazil to correlate the leaf nutrient contents and the yields of potato crops. Crops with yields higher than 48,993.24 kg ha−1 were considered to have high yields, and the Mahalanobis distance separated the balanced samples from the nutritionally unbalanced ones. Thus, the CND-ilr method generated the norms and classified the 587 samples as nutritionally balanced with a high yield (5% of the total), nutritionally unbalanced with a low yield (92%), nutritionally unbalanced with a high yield (0.3%), or nutritionally balanced with a low yield (2.7%), with accuracy, sensitivity, specificity, NPV, and PPV scores of 96.9, 97.1, 93.6, 64.4, and 99.6%, respectively.

Fault diagnosis method for lithium-ion batteries based on the combination of voltage prediction and Z-score

ABSTRACT Safety accidents in new energy electric vehicles caused by lithium-ion battery failures occur frequently, and the timely and accurate diagnosis of failures in battery packs is crucial. Voltage, as one of the primary characterization parameters of lithium-ion battery malfunctions, is widely utilized in fault diagnosis. This article proposes a lithium-ion battery fault diagnosis method Fault diagnosis method based on the combination of voltage prediction and Z-score. Firstly, the stable trend component is extracted from the battery voltage data using variational mode decomposition, which avoids the influence of noisy signals and random perturbations to the greatest extent. Subsequently, a TCN-BiLSTM-attention model is designed to estimate the average voltage of the battery under normal conditions. Finally, the residuals between the estimated and individual cell voltages are calculated, and the Z-score is utilized to locate and judge whether the battery is caused by the occurrence of a fault. Through verification with real vehicle data and experimental data, the proposed method effectively identifies abnormal battery cells. Compared to the correlation coefficient method, this approach exhibits superior applicability.

Course of Niemann – Pick disease type A/B in the context of hematopoietic stem cell transplantation

Niemann – Pick disease type A/B (NPAB) is a rare severe inherited disease from the group of accumulation diseases with a defect in the acid sphingomyelinase gene (Niemann-Pick disease types B, A/B) (ASMD). Symptoms of damage to the nervous system and internal organs manifest in infancy, leading to disability, fatalities in childhood. NPAB is so far incurable. Optimal management of the disease requires a multidisciplinary team of physicians, specialists. The basis of therapy is the elimination of existing/forming complications, symptomatic treatment. Enzyme replacement therapy as a means of modifying the course of this disease is expected to slow down the progression of pathologic manifestations of the disease not related to the central nervous system lesions. Single cases of hematopoietic stem cell transplantation (HSCT) have been described in the treatment of ASMD, which is one of the new methods aimed at normalizing the level of acid sphingomyelinase, blood parameters, as well as reducing the severity of pathological visceral manifestations. However, the development of complications during HSCT, absence of positive therapeutic effect in severe CNS lesions does not allow to widely implement this method. Taking into account the contradictory data on the efficacy of HSCT in ASMD, further clinical studies are required. Analysis of 2 clinical cases of NPAB in children from the same family allowed us to reveal differences in the course and outcomes of the disease at verification of the diagnosis at birth followed by HSCT. Difficulties in diagnosing this extremely rare pathology, which requires a multidisciplinary approach, justify the need to improve methods of early diagnosis, including the organization of genetic risk determination, introduction of prenatal genetic testing before pregnancy.

Development and Application of IoT Monitoring Systems for Typical Large Amusement Facilities

The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors’ expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults.

Course of Niemann – Pick disease type A/B in the context of hematopoietic stem cell transplantation

Niemann – Pick disease type A/B (NPAB) is a rare severe inherited disease from the group of accumulation diseases with a defect in the acid sphingomyelinase gene (Niemann-Pick disease types B, A/B) (ASMD). Symptoms of damage to the nervous system and internal organs manifest in infancy, leading to disability, fatalities in childhood. NPAB is so far incurable. Optimal management of the disease requires a multidisciplinary team of physicians, specialists. The basis of therapy is the elimination of existing/forming complications, symptomatic treatment. Enzyme replacement therapy as a means of modifying the course of this disease is expected to slow down the progression of pathologic manifestations of the disease not related to the central nervous system lesions. Single cases of hematopoietic stem cell transplantation (HSCT) have been described in the treatment of ASMD, which is one of the new methods aimed at normalizing the level of acid sphingomyelinase, blood parameters, as well as reducing the severity of pathological visceral manifestations. However, the development of complications during HSCT, absence of positive therapeutic effect in severe CNS lesions does not allow to widely implement this method. Taking into account the contradictory data on the efficacy of HSCT in ASMD, further clinical studies are required. Analysis of 2 clinical cases of NPAB in children from the same family allowed us to reveal differences in the course and outcomes of the disease at verification of the diagnosis at birth followed by HSCT. Difficulties in diagnosing this extremely rare pathology, which requires a multidisciplinary approach, justify the need to improve methods of early diagnosis, including the organization of genetic risk determination, introduction of prenatal genetic testing before pregnancy.

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Abstract Planetary gearboxes often experience multiple component failures during service, which can accelerate the degradation and failure of industrial equipment. Accurate separation and identification of multiple faults is an important means of ensuring the safe and stable operation of equipment. However, different faults can interact with each other, along with the influence of background noise, making it challenging to accurately extract faults with relatively weak energy among multiple faults. This difficulty leads to the problems of potential misdiagnosis and underdiagnosis. To address this issue, an adaptive mode decomposition method based on fault feature orientation (AMD-FF) is proposed in this paper. Initially, a fault impact indicator (FII) is constructed based on period-weighted kurtosis of envelope spectral and correlated combination negentropy to effectively characterize the impulsiveness and periodicity of fault features. Furthermore, with the objective of maximizing the FII, an adaptive decomposition of the original signal is designed based on blind convolution theory using a finite-impulse response filter group. Subsequently, a variable weight particle swarm optimization is employed to adaptively optimize the key decomposition parameters. Finally, the data of industrial-grade planetary gear transmission test rig are collected to validate the proposed method for compound fault diagnosis of planetary gearboxes. The results indicate that the AFMD-FF can effectively separate and extract compound faults in planetary gearboxes, demonstrating superior fault separation and diagnostic performance compared to the fault mode decomposition (FMD) and adaptive FMD. This method offers a novel approach to diagnosing compound faults in rotating equipment in industrial scenarios.

Gasoline Engine Misfire Fault Diagnosis Method Based on Improved YOLOv8

In order to realize the online diagnosis and prediction of gasoline engine fire faults, this paper proposes an improved misfire fault detection algorithm model based on YOLOv8 for sound signals of gasoline engines. The improvement involves substituting a C2f module in the YOLOv8 backbone network by a BiFormer attention module and another C2f module substituted by a CBAM module that combines channel and spatial attention mechanisms which enhance the neural network’s capacity to extract the complex features. The normal and misfire sound signals of a gasoline engine are processed by wavelet transformation and converted to time–frequency images for the training, verification, and testing of convolutional neural network. The experimental results show that the precision of the improved YOLOv8 algorithm model is 99.71% for gasoline engine fire fault tests, which is 2 percentage points higher than for the YOLOv8 network model. The diagnosis time of each sound is less than 100 ms, making it suitable for developing IoT devices for gasoline engine misfire fault diagnosis and driverless vehicles.

Detection of breast cancer by deep belief network with improved activation function

SummaryBreast cancer is the most prevalent kind of tumor to occur in females and the primary cause of death for women. Early detection is perhaps the most successful strategy to minimize breast cancer mortality. Early diagnosis necessitates a consistent and efficient diagnostics method that allows doctors to differentiate benign from malignant breast cancers without a surgical sample. The goal of this endeavor is to develop a sophisticated breast cancer diagnosis method. The primary goal of the paper is to reduce the death rate among women by promoting early detection of breast cancer. First, pre‐processing techniques such as median filtering and contrast limiting adaptive histogram equalization are used to the obtained raw images. By doing this, the machine‐learning model's computational complexity is decreased and the image quality is enhanced. K‐means clustering is used to segregate the pre‐processed image. Additionally, features including the enhanced local vector pattern, grey‐level co‐occurrence matrix and local vector patterns are produced in the course of the feature extraction stage. Finally, an optimized deep belief network (DBN) is carrying out the classification process. To boosts the classification accuracy, activation function of DBN (tanh, softmax, ReLu) as well as its weight function is optimized by the proposed grey wolf updated whale optimization algorithm The accuracy of the greywolf updated whale optimization algorithm+DBN is above 93% for datasets 1 and 2 when compared to extant models. Finally, calculation of the performance validates the proposed model's performance.

Gasoline Engine Misfire Fault Diagnosis Method Based on Improved YOLOv8

Gasoline Engine Misfire Fault Diagnosis Method Based on Improved YOLOv8

Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin

Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine’s operating status. This facilitates quick fault location and enables more accurate and efficient maintenance.

Element-wise parallel deep learning for structural distributed damage diagnosis by leveraging physical properties of long-gauge static strain transmissibility under moving loads

The Transmissibility Function (TF) has gained considerable interest in structural damage detection because of its relatively high sensitivity to damage and robustness to excitation. This study proposed a distributed damage diagnosis method for beam-like structures based on a long-gauge static strain TF defined as the ratio of the Fourier transform of static strain response under moving loads from a target long-gauge sensor to that from the reference sensor. It has been discovered that each long-gauge static strain TF is independent of moving loads, making it an ideal damage indicator. It exhibits direct proportionality to the ratio of bending stiffness between the two measured zones covered by the target and reference long-gauge strain sensors while being unaffected by the stiffness of any other zones not covered by these two sensors. Moreover, the TF at zero frequency was proved to be equivalent to the ratio of static strain time history areas, relying solely on the stiffness of the covered zones. Leveraged by the physical properties of long-gauge static strain TF, an element-wise parallel deep neural network architecture was developed to decompose damage diagnosis into independent sub-tasks on the element level, utilizing a variational autoencoder (VAE) in the Bayesian inference framework for extracting features from the long-gauge strain TF and a regressor for mapping the features to elemental stiffness reduction. The divide-and-conquer strategy and element-wise parallel learning architecture allow for a significant reduction in the number of labeled training samples generated based on the updated numerical baseline model as it only requires simulated scenarios involving a single damage element. The efficiency and robustness of the proposed method were demonstrated through a numerical simply supported beam and a laboratory experiment on a two-span bridge.

Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin

Fault diagnosis of wind turbines has always been a challenging problem due to their complexity and harsh working conditions. Although data-mining-based fault diagnosis methods can accurately and efficiently diagnose potential faults, the visibility is extremely poor. In this paper, digital twin technology is introduced into the fault diagnosis of wind turbine drive train systems, and a wind turbine drive train fault diagnosis method based on digital twin technology is proposed, which monitors and simulates the actual operating condition in real-time by establishing a digital twin model of the wind turbine drive train. In addition, an improved variational modal decomposition combined with particle swarm optimization least squares support vector machine (IVMD-PSO-LSSVM) fault diagnosis method is proposed, which not only improves the accuracy of fault diagnosis but also effectively shortens the diagnosis time and strengthens the response speed of the system. Finally, a digital twin system for condition monitoring and fault diagnosis of wind turbine drive trains is developed based on the Unity 3D platform. Experiments show that the proposed IVMD-PSO-LSSVM can accurately identify fault types with an accuracy rate of 99.1%, which is an improvement of 3.4% compared to before. The proposed digital twin model can be used for real-time monitoring of wind turbine vibration data and provide a more intuitive real-time simulation of the wind turbine’s operating status. This facilitates quick fault location and enables more accurate and efficient maintenance.

A novel domain adaptive method for gearbox fault diagnosis using maximum multiple-classifier discrepancy network

Abstract Domain adaptive gearbox fault diagnosis methods have made impressive achievements for the past several years. However, most of the traditional domain adaptive methods have significant limitations under fluctuating operating conditions. The acquired acceleration signals will result in different signal vibration spectra and peak vibration amplitudes due to the different working conditions between the source and target domains. There is an obvious discrepancy between the distribution of fault samples in the source domain and the target domain, which in turn makes it difficult to classify the target domain samples with fuzzy fault category boundaries. Therefore, how to measure the discrepancy between two distributions has been an important research direction in machine learning. A good metric helps to discover better features and build better models. In this paper, a novel domain adaptive method for gearbox fault diagnosis using maximum multiple-classifier discrepancy network (MMCDN) is proposed. The sparse stack autoencoder (SSAE) is used by the MMCDN as a feature extractor for fault feature extraction, and a kind of composite distance is adopted for domain discrepancy measurement of source and target domain features for domain alignment. Then the extracted features are input into a three-classifier of the model for adversarial training. The trained model classifiers have high performance in fault classification. The combination of domain adaptation and multi-classifier discrepancy output can effectively solve the impact of working condition changes and the misclassification problem for fuzzy samples with class boundaries. Experimental validation with two planetary gearbox datasets shows that the MMCDN has more favorable diagnostic accuracy and performance than other methods.

A Coarse and Fine-Grained Deep Multi View Subspace Clustering Method for Unsupervised Fault Diagnosis of Rolling Bearings

Abstract To address the issue of insufficient characterization of fault features in inherent vibration data that affects the performance of unsupervised learning-based fault diagnosis, a coarse and fine-grained deep multi view subspace clustering method (CFG-DMVSC) for unsupervised fault diagnosis of rolling bearings is proposed. The proposed method designs a convolutional autoencoder network based on the Gramian angular field transformation for multi-signal analysis domains. A multi-view coarse-grained self-expressive method based on information entropy is designed to handle differences in information across different views. Furthermore, a fine-grained common and independent information separation loss function based on mutual information is proposed to ensure compactness among multiple views. Both the Case Western Reserve University rolling bearing dataset and privately built bearing fault test bench data demonstrate that, compared to existing methods, the proposed method can perform coarse and fine-grained division in multi-view subspaces, achieving better clustering diagnosis performance on the extracted common information among views.

A Coarse and Fine-Grained Deep Multi View Subspace Clustering Method for Unsupervised Fault Diagnosis of Rolling Bearings

Abstract To address the issue of insufficient characterization of fault features in inherent vibration data that affects the performance of unsupervised learning-based fault diagnosis, a coarse and fine-grained deep multi view subspace clustering method (CFG-DMVSC) for unsupervised fault diagnosis of rolling bearings is proposed. The proposed method designs a convolutional autoencoder network based on the Gramian angular field transformation for multi-signal analysis domains. A multi-view coarse-grained self-expressive method based on information entropy is designed to handle differences in information across different views. Furthermore, a fine-grained common and independent information separation loss function based on mutual information is proposed to ensure compactness among multiple views. Both the Case Western Reserve University rolling bearing dataset and privately built bearing fault test bench data demonstrate that, compared to existing methods, the proposed method can perform coarse and fine-grained division in multi-view subspaces, achieving better clustering diagnosis performance on the extracted common information among views.

Evaluating Two Sampling Methods for Mycoplasma Bovis Diagnosis in American Bison (Bison bison).

Mycoplasma bovis is a bacterial pathogen endemic to cattle. In the early 2000s, M. bovis emerged as a cause of respiratory disease in American bison (Bison bison), causing significant morbidity and mortality. Bison herds that experience an outbreak of M. bovis are at higher risk for subsequent outbreaks, suggesting that chronic, subclinical infections can be established. Antemortem testing is therefore crucial to disease management; however, the precise sampling method to maximize detection of M. bovis in bison is unknown. We evaluated two sample types-superficial nasal swabs and deep nasopharyngeal swabs-collected from apparently healthy or symptomatic bison from January 2021 through December 2022. We used real-time PCR to detect M. bovis in 76/938 bison (8.1%) from 11 herds. For bison testing positive on at least one swab type, M. bovis was detected in 63/76 (82.8%) deep nasopharyngeal swabs and 29/73 (38.1%) superficial nasal swabs. Agreement between swabs for positive bison was 21% (n=16, kappa coefficient 0.319). We conclude that deep nasopharyngeal swabbing is more sensitive than superficial nasal swabbing for detection of M. bovis in bison and that low agreement between methods may be related to stage of infection. We further tested pooled samples by PCR and found that pooling of up to five samples can be effective to increase throughput and minimize costs. Management of wild bison relies on the ability to relocate animals to maintain gene flow and healthy populations. Sensitive and specific diagnostic tests are needed to inform decisions and minimize risk of transmission, especially from subclinical carriers. This study provides valuable insight that will inform best practices for M. bovis testing, thereby supporting the conservation of bison as healthy wildlife, which in turn promotes ecological restoration, safeguards cultural practices of Tribal Nations, and upholds the bison as a unique American icon.