Method For Diagnosis Research Articles

Association between metabolic syndrome and the risk of glaucoma: a meta-analysis of observational studies

BackgroundThe potential link between metabolic syndrome (MetS) and the risk of glaucoma has been proposed but remains inconclusive. This meta-analysis aimed to systematically evaluate the association between MetS and the risk of glaucoma.MethodsWe conducted a comprehensive search of PubMed, Embase, and Web of Science from inception to August 12, 2024, for observational studies assessing the relationship between MetS and glaucoma risk. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the association. Heterogeneity was assessed using I² statistics, and a random-effects model was applied.ResultsNine studies involving 2,258,797 participants were included. The pooled results showed that MetS was significantly associated with an increased risk of glaucoma (OR: 1.34, 95% CI 1.15–1.55, p < 0.001; I² = 75%). Subgroup analyses according to the individual component of MetS suggested that hypertension and hyperglycemia were significantly associated with glaucoma, but not for obesity or dyslipidemia, although the difference among subgroups was not significant (p = 0.05). Further subgroup and meta-regression analyses suggested that the results were not significantly affected by study design, average age, sex, method of glaucoma diagnosis, or glaucoma subtype (primary open-angle glaucoma or normal-tension glaucoma). Sensitivity analysis confirmed the robustness of the findings.ConclusionsThis meta-analysis suggests that MetS is significantly associated with an increased risk of glaucoma. These findings highlight the need for heightened awareness and potential screening strategies for glaucoma in individuals with MetS. Further studies are required to elucidate underlying mechanisms and causality.

Scapholunate advanced collapse of the wrist: a review

Background. Scapholunate advanced collapse (SLAC) is a common type of the wrist osteoarthritis. It is characterized by progressive deformity, instability and arthritis due to chronic dissociation of the scaphoid and lunate bones with chronic scapholunate interosseous ligament (SLIL) injuries. Quite often, in this pathology, it is difficult to alleviate the pain syndrome and preserve the range of motion. The aim of the review — based on the modern literature data, to analyse the concept and pathogenesis of scapholunate advanced collapse, current methods of radiological diagnosis and treatment modalities of the pathology. Methods. The search was performed in eLIBRARY and PubMed databases using the following keywords: scapholunate advanced collapse, SLAC, scapholunate instability, proximal row carpectomy. We selected 86 original articles and reviews out of 110 papers found. The selected ones reveal the issues of pathogenesis, radiological diagnosis and treatment of the pathology. Results. Scapholunate carpal collapse is secondary arthritis of the carpal bones resulting from rupture of the SLIL, which can be traumatic or non-traumatic. Conservative methods of the SLAC treatment can be used in the early stage of arthritis, but there is no data on their effectiveness in the world literature. Surgical treatment methods are varied, the choice depends on the clinical stage of the disease. Conclusions. The efficiency of conservative treatment modalities of the SLAC syndrome has not been proved yet. There is a plenty of surgical treatment methods and their modifications developed to alleviate the pain syndrome and preserve the motion and strength of the wrist grip as far as it is possible. However, the choice of the treatment method depending on the clinical stage of the disease is still problematic. For young active patients especially important is the development of adjuvant techniques aimed to increase the treatment efficiency. To date, there is accumulating evidence of successful use of pyrocarbon implants and arthroscopy techniques in the wrist surgery.

A light-weight factorized convolutions based dual-input fuzzy-CNN for efficient motor bearing fault diagnosis

ABSTRACT Efficient and timely identification of bearing faults is imperative to ensure operational normalcy, reduced down-times and health hazards in motor fault tolerant control systems. This paper proposes a fault diagnosis method that combines the vibration information with time-varying rotational speed for effective fault diagnosis under non-stationary conditions. The complex wavelet transform is used to encode both the vibrational and rotational signals in 2d representations for spatial feature extraction. An efficient algorithm is proposed to select the mother wavelet with the least average entropy. Moreover, a spatial decomposition-based approach using factorised convolutions is used to create a light-weight fuzzy convolutional neural network named Split-Operation Fuzzy Convolutional Neural Network (SOF-CNN) for semi rule-based feature extraction and classification. The performance was evaluated on the University of Ottawa (UOO) dataset for multiple speed conditions and cross-condition validation with the highest accuracy yield being 99.92% from the fourth condition and 3rd trial acquired via averaged 10-Fold Cross Validation. The average accuracy yield across all the scenario was 99.89% with 64.73% being the highest accuracy for cross-condition validation. The performance was evaluated across a diverse range of evaluation criterion including both quantitative and statistical tests.

A comparative analysis of fault detection and process diagnosis methods based on a signal processing paradigm

An important paradigm in industrial engineering for fault detection and diagnosis purposes is signal processing. The various methods consider methods in the time, frequency, or time–frequency domain for signal processing as state and output signals from the considered process. The objective of this work is to perform a comparative analysis of the most used methods based on a signal processing paradigm and in the context of fault detection and process diagnosis. The electromechanical equipment that generates mechanical vibrations—as an effect of bearing faults—is considered and analyzed. The recorded data are explored with smaller and sliding frames, adapted to the processing criteria used. Seven methods are considered for evaluation: two in the time domain, two in the frequency domain and three in the time–frequency domain. The main problem is to extract and select the right features to use in the classification stage. The methods of the time domain are based on statistical moments and signal modeling. The methods in the frequency domain use either the discrete components of power spectra or the features of the frequency domain. In the time–frequency domain, the coefficients of the time–frequency transforms define digital images, which are further processed. For testing, the methods are evaluated with real recorded data from bearings with several types and sizes of faults, i.e., incipient, medium, advanced, and large. Finally, the considered methods are compared from the point of view of five criteria, namely, the recognition rate, window length, response time, computational resources, and complexity of the algorithms. A global quality criterion is built and used to assess the quality of the methods. The results of the computer-based experiments show acceptable performance for all methods for the test case of bearings but the potential to detect more complex faults and change detection in the behavior of the machines, in general. Time–frequency methods offer an optimum.

A Rolling Bearing Fault Diagnosis Method Combining MSSSA-VMD with the Parallel Network of GASF-CNN and BiLSTM

Once the rolling bearing fails, it will threaten the normal operation of the whole rotating machinery. Therefore, it is very necessary to conduct research on rolling bearing fault diagnosis. This paper proposes a rolling bearing fault diagnosis method combining MSSSA-VMD (variational mode decomposition optimized by the improved salp swarm algorithm based on mixed strategy) with the parallel network of GASF-CNN (convolutional neural network based on Gramian angular summation field) and bi-directional long short-term memory (BiLSTM) to solve the problem of poor diagnostic performance for the rolling bearing faults caused by the respective limitations of existing fault diagnosis methods based on signal processing and deep learning. Firstly, MSSSA-VMD is proposed to solve the problem where the decomposition effect of VMD is not ideal due to improper parameter selection. Then, MSSSA-VMD is employed to preprocess and extract characteristics. Finally, the extracted characteristics are input into the parallel network of GASF-CNN and BiLSTM for diagnosis. In one channel of the parallel network, GASF is used to convert the characteristic vectors into a two-dimensional image, which is then fed into CNN for spatial characteristic extraction. In the other channel of the parallel network, the characteristic vectors are directly input into BiLSTM for temporal characteristic extraction. Experimental results demonstrate that the proposed method has good performance in terms of fault diagnosis performance under constant operating conditions, generalization ability under variable operating conditions and noise resistance.

Multi-Sensor Adaptive Fusion and Convolutional Neural Network-Based Acoustic Emission Diagnosis for Initial Damage of the Engine

Abstract Aiming at the problems of traditional fault diagnosis means that are difficult to identify initial damage, as well as the poor reliability and fault tolerance with a single sensor, an acoustic emission (AE) diagnosis method for initial damage of the engine based on multi-sensor adaptive fusion and convolutional neural network (CNN) is proposed. Firstly, under the premise of utilizing parametric analysis to characterize the multi-sensor AE signals, the feature parameter entropy is used to determine the primary and secondary relationships between multi-sensor signals, and then the AE feature parameter matrix is formed by adaptive fusion. Secondly, CNN is employed to mine and learn the fault feature combinations from the AE feature parameter matrix by multi-layer fusion to realize the identification and diagnosis for initial damage of the engine. Finally, the proposed method is validated on the engine test bench designed for initial damage identification and is compared with conventional methods in terms of diagnostic performance. The results demonstrate that the proposed method can achieve an identification accuracy of 98.83% for initial damage, and has advantages in various aspects such as TAMSE, K, F1mic, and F1mac, which explicitly provides a theoretical and methodological basis for identifying initial faults comprehensively and accurately. This research not only enriches the theory and methods in the field of structural health monitoring, but also provides strong technical support for engine health management, which is expected to play a key role in the maintenance and guarantee of aviation engines in the future.&amp;#xD;

Mathematical Mechanism of Gini Index Used for Multiple-Impulse Phenomenon Characterization

The Gini index (GI) is widely used for measuring the sparsity of signals and has been proven to be effective in the extraction of fault features. A fault-induced vibration, which involves the obvious phenomenon of multiple impulses, is a kind of sparse signal and the GI has been widely used in the diagnosis of rotating machine faults. However, why the GI can be used to evaluate the sparsity or impulsiveness of a signal has not been revealed directly. In this study, the mathematical mechanism of the GI, used for the representation of the multiple-impulse phenomenon, is deeply researched based on the theoretical deviation of the GI with regard to several typical signals. The theoretical results show that the GI increases with the increment in the number of impulses in the signal when the signal is interrupted by relatively low degrees of white noise. The bigger the difference between the amplitude of the impulse and the variance in the noise, the bigger the value of the GI. Namely, the signal-to-noise ratio has a great influence on the value of the GI. However, the GI is still a powerful tool for the characterization of the impulsive intensity of the multiple-impulse phenomenon. Both simulation and experimental data analysis are introduced to show the application of the GI in practice. It is shown that the fault diagnosis method based on the maximization of the GI is more powerful than that of kurtosis in terms of the extraction of fault features of rolling element bearings (REBs).

Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model

Electric shock protection is critical for ensuring power safety in low-voltage grids, and robust fault diagnosis methods provide an essential foundation for the accurate operation of such protection devices. However, current low-voltage electric shock protection devices often suffer from limitations in operational precision and in their ability to effectively recognize electric shock types. To address these challenges, this paper proposes a fault diagnosis method for low-voltage electric shocks based on an attention-enhanced parallel CNN-BiLSTM model. The method first utilizes CNN to extract local spatial features of the electric shock signal and BiLSTM to capture temporal features. An attention mechanism is then introduced to fuse the local spatial and temporal features with weighted emphasis. Finally, a fully connected layer maps the fused features to the output layer, generating diagnostic results. Visualization through T-SNE analysis validates the improvement in model performance due to the attention mechanism. Comparative experiments show that the proposed model outperforms single models and other combined models in terms of accuracy, precision, recall, F1 score, and convergence speed. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 99.55%.

A pitting fault diagnosis method for planetary gear in automatic transmission based on multiple advantageous sub-band signal fusion

Abstract Localized pitting in planetary gears is a critical fault in the automatic transmission (AT) systems of heavy-duty vehicles, and identifying this fault is an important aspect of vehicle maintenance. The single sensor often fails to capture comprehensive fault information from the housing of the AT. To fully capture fault characteristics from multi-sensor data and improve the accuracy of intelligent fault diagnosis, this paper proposes a pitting fault diagnosis method based on the fusion of multiple advantageous sub-band signals. Initially, the proposed method employs variational mode decomposition based on K-optimization to demodulate the raw fault signals acquired by various sensors. Subsequently, the advantageous sub-band signals, which contain the pitting fault information, are selected by integrating two indices: Variance Accounted For (VAF) and Spectral Entropy (H(F)). These selected sub-band signals are then fused using a data fusion method based on the dissimilarity measure. Finally, a 1D-AlexNet model is constructed to diagnose the fused signals. The validity and superiority of this method are confirmed through a pitting fault injection experiment in a AT test rig. This paper also compares other diagnosis methods to further demonstrate the superiority of the proposed method.

Targeted thoracic ultrasonography shows high diagnostic potential for interstitial pneumonia in feedyard cattle.

To use a novel approach of targeted thoracic point-of-care ultrasound (TT-POCUS) of the caudodorsal right lung to determine potential associations between TT-POCUS measurements and the presence of interstitial pneumonia (IP) in feedyard cattle. Cross-sectional study evaluated feedyard animals (n = 62; 358.4 ± 10.5 kg; heifer, n = 38; steer, n = 24) using TT-POCUS at time of respiratory disease treatment and microscopic assessment of lesions at subsequent mortality. Evaluations took place from July through December 2023. Pulmonary samples were collected at necropsy for histopathologic assessment, and lesions were used to diagnose IP or non-IP. Statistical models determined potential associations between IP, cattle demographic, and TT-POCUS measured variables. Evaluations of animals enrolled (n = 62) displayed significant associations with IP, including ultrasound lung score (ULS), B-line count, A-line count, and days on feed at treatment. Cattle with a ULS of 5 had the lowest probability of IP (0.12 ± 0.16) compared to cattle with a ULS of 2, 3, or 4 (0.72 ± 0.18, 0.93 ± 0.06, and 0.85 ± 0.08, respectively). Cattle with B-line counts > 5 had a higher probability of IP (0.86 ± 0.11) compared to cattle with 3 to 5 B-lines (0.38 ± 0.14). The probability of IP was higher in cattle with A-line counts < 3 (0.83 ± 0.10) compared to cattle with ≥ 3 A-lines (0.51 ± 0.15). Strategic targeted thoracic ultrasonography has potential a value of timely IP diagnosis in feedyard cattle, thereby offering important insights into therapeutic and management decisions. Targeted thoracic ultrasonography can be utilized as a method for IP diagnosis in feedyard cattle.

A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer

Background/Objectives: Age-related macular degeneration (AMD) is a significant cause of vision loss in older adults, often progressing without early noticeable symptoms. Deep learning (DL) models, particularly convolutional neural networks (CNNs), demonstrate potential in accurately diagnosing and classifying AMD using medical imaging technologies like optical coherence to-mography (OCT) scans. This study introduces a novel CNN-based DL method for AMD diagnosis, aiming to enhance computational efficiency and classification accuracy. Methods: The proposed method (PM) combines modified Inception modules, Depthwise Squeeze-and-Excitation Blocks, and ConvMixer architecture. Its effectiveness was evaluated on two datasets: a private dataset with 2316 images and the public Noor dataset. Key performance metrics, including accuracy, precision, recall, and F1 score, were calculated to assess the method’s diagnostic performance. Results: On the private dataset, the PM achieved outstanding performance: 97.98% accuracy, 97.95% precision, 97.77% recall, and 97.86% F1 score. When tested on the public Noor dataset, the method reached 100% across all evaluation metrics, outperforming existing DL approaches. Conclusions: These results highlight the promising role of AI-based systems in AMD diagnosis, of-fering advanced feature extraction capabilities that can potentially enable early detection and in-tervention, ultimately improving patient care and outcomes. While the proposed model demon-strates promising performance on the datasets tested, the study is limited by the size and diversity of the datasets. Future work will focus on external clinical validation to address these limita-tions.

Research on a Bearing Fault Diagnosis Method Based on a CNN-LSTM-GRU Model

In view of the problem of the insufficient performance of deep learning models in time series prediction and poor comprehensive space–time feature extraction, this paper proposes a diagnostic method (CNN-LSTM-GRU) that integrates convolutional neural network (CNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) models. In this study, a convolutional neural network (CNN) model is used to process two-dimensional image data in both time and frequency domains, and a convolutional core attention mechanism is introduced to extract spatial features, such as peaks, cliffs, and waveforms, from the samples. A long short-term memory (LSTM) network is embedded in the output processing of the convolutional neural network (CNN) to analyze the long-sequence variation characteristics of rolling bearing vibration signals and enable long-term time series prediction by capturing long-term dependencies in the sequence. In addition, a gated recurrent unit (GRU) is used to refine long-term time series predictions, providing local fine-tuning and improving the accuracy of fault diagnosis. Using a dataset obtained from Case Western Reserve University (CWRU), the average accuracy of CNN-LSTM-GRU fault vibration is greater than 99%, and its superior performance in a noisy environment is demonstrated.

The prevalence of obstructive sleep apnea-hypopnea syndrome in patients with multiple sclerosis: a systematic review and meta-analysis

ObjectiveObstructive sleep apnea-hypopnea syndrome is the most common respiratory disorder in patients with multiple sclerosis. The purpose of this meta-analysis was to evaluate the prevalence of OSAHS in MS patients and to analyze their sleep monitoring indicators of patients.MethodsOnline databases such as PubMed, EMBASE, Web of Science, CNKI, and WanFang were used to review the Chinese and English literature about OSAHS in MS patients in detail. Two researchers analyzed the Quality of included studies based on the Quality Assessment of Diagnostic Accuracy Studies. The prevalence and sleep monitoring data were analyzed using STATA 11.0 software. Based on the I2 values, pooled analyses were performed using either random (I2 &amp;gt; 50%) or fixed-effect models (I2 ≤ 50%).ResultsFourteen articles were selected for the final analysis. Our study shows that different diagnosis methods of OSAHS lead to different incidences. When the screening method was PSG, the incidence of OSAHS in MS patients was 36%; when the method was STOP-BANG, the incidence of OSAHS in MS patients was 26%; when the method was Berlin questionnaire, the incidence of OSAHS in MS patients was 30%. We performed subgroup analyses based on race, age, OSAHS severity, and BMI of patients with MS. The results suggested that the incidence of OSAHS was different in different subgroups of MS patients. In addition, we found that patients with MS generally had poorer sleep monitoring indicators.ConclusionThe current literature shows that the incidence of OSAHS is higher in MS patients. MS may affect the progression of OSAHS.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=551500, CRD42024551500.

Application of FCEEMD-TSMFDE and adaptive CatBoost in fault diagnosis of complex variable condition bearings

The mode mixing problem and inherent mode function selection bias in Fast Ensemble Empirical Mode Decomposition (FEEMD) result in ineffective extraction of fault components during the denoising stage, the loss of coarse-grained information in Multiscale Fuzzy Dispersion Entropy (MFDE) reduces the stability of fault features, and the lack of adaptability of CatBoost hyperparameters leads to reduced diagnostic accuracy. Therefore, a complex variable operating condition fault diagnosis method based on Fast Complementary Ensemble Empirical Mode Decomposition (FCEEMD) - Time-shift Multiscale Fuzzy Dispersion Entropy (TSMFDE) and adaptive Optuna-CatBoost is proposed. We introduce paired white noise with opposite signs in the construction of FCEEMD, effectively suppressing mode aliasing by neutralizing the residual noise generated during decomposition. Then, the Maximum Information Coefficient / Gini Index was introduced to construct a composite screening strategy, retaining the Intrinsic Mode Function (IMF) components that are strongly correlated with the original signal and have a fault impact to reconstruct the denoised signal. Secondly, time-shift multiscale is introduced into the coarse-grained process, and the constructed TSMFDE effectively extracts complete and stable fault features. Finally, with the introduction of the Optuna hyperparameter optimization framework, the adaptive Optuna-CatBoost can accurately diagnose bearing faults. The average fault diagnosis accuracy of the proposed method reached 99.76% and 99.33%, indicating that FCEEMD based on white noise can quickly and accurately decompose non-aliasing vibration modes, and the composite screening strategy can further filter out irrelevant noise modes and improve signal quality; The proposed TSMFDE can extract stable fault features, and its combination with Optuna-CatBoost can further improve the accuracy of fault diagnosis. This model is expected to be applied in more fields of feature extraction and pattern recognition.

Fault diagnosis method of rolling bearing under variable operating conditions based on MFCCNN

A rolling bearing fault diagnosis method based on multi-scale feature fusion and cross-level connection convolutional neural network (MFCCNN) is proposed to address the issues of low accuracy and low generalization performance caused by the complex and variable actual working conditions of rolling bearings. Firstly, a Markov transition field (MTF) is used to convert a one-dimensional signal into a two-dimensional image with temporal correlation. Next, an improved channel attention mechanism is integrated into the multi-scale network to assign different weights to each channel information and extract initial features. Then, a position attention mechanism is created and incorporated into the cross-level connection to enhance the feature expression ability for deep feature extraction. The MFCCNN faults diagnosis model is developed based on the above module. Finally, the MTF images is input into the proposed model for training, and the Softmax classifier is utilized to achieve fault classification. To verify the effectiveness and superiority of the MFCCNN method, the CWRU and MFS rolling bearing dataset are selected for experimental verification. The results demonstrates that the MFCCNN model has higher diagnostic accuracy, stronger noise immunity, and generalization performance in varying operating conditions compared to other fault diagnosis methods.

Modified Kernel Global-Local Marginal Fisher Analysis for Rolling Bearing Feature Extraction

Abstract The vibration signals of rolling bearings usually exhibit high-dimensional, nonlinear, and non-Gaussian distribution characteristics due to long-term operation under complex working conditions. Therefore, we proposed a novel algorithm named Modified Kernel Global-Local Marginal Fisher Analysis (MKGLMFA) for bearing feature extraction and dimensionality reduction. The proposed MKGLMFA algorithm introduces the kernel function to map data into a high-dimensional space to represent data nonlinearly first. It enhances the within-class compactness and between-class dispersibility by considering spatial relationships and label information when constructing adjacency graphs and simultaneously exploits the local and global geometry of data. Furthermore, a bearing fault diagnosis approach is presented based on MKGLMFA. It first processes the original vibration signals through MKGLMFA to obtain low-dimensional manifold features. Then these characteristics were input into the K-nearest neighbor (KNN) classifier to achieve fault pattern recognition. The superiority of the proposed MKGLMFA algorithm in feature extraction is verified in comparison with some existing state-of-the-art machine learning methods on three rolling bearings datasets. And the subsequent classification diagnosis experiments indicate the effectiveness and high efficiency of the newly raised MKGLMFA algorithm. In comparison with the representative diagnosis methods, the proposed method can extract more sensitive discriminant features, and the classification accuracy of diagnosis is significantly improved in consequence.

Exploring the impact of developmental dysplasia of the hip on refugees with temporary protected status in Türkiye.

In this study, we examined whether there was a change in the number of children who had been screened by hip ultrasound, the age of first diagnosis, and the number of invasive and conservative treatments applied due to developmental dysplasia of the hip between 2016 and 2022 among refugees who were in "Temporary Protection Status" in Türkiye? The records were collected via the e-health database of the Turkish Ministry of Health. Over 1 month old were included in the study. The number of ultrasonography (USG) that was performed for developmental dysplasia of the hip survey had significantly increased over time. The incidence for 5 years was 6 cases per 100 babies. However, the incidence of developmental dysplasia of the hip needing intervention was 0.4 cases per 1000 babies. In 2016, the most used diagnosis method was X-ray. By contrast, the use of USG has increased from 2016 to 2022. The mean age at the time of diagnosis was significantly high in 2016, 2017, and 2020. The number of invasive treatment modalities including closed reduction under anesthesia, open reduction alone, or open reduction with pelvic and/or femoral osteotomies had significantly decreased from 2016 to 2022. However, the number of abduction orthosis used for treatment also increased significantly. Free access to health services is effective in promoting families' compliance with screening programs for developmental dysplasia of the hip. But is not enough for initial periods of mass migrations. To increase sensitivity to screening programs for possible diseases, further efforts are needed to prevent low compliance in early cases of mass migrations.

Диагностика врожденных дефектов иммунитета в многопрофильном стационаре: обзор клинических наблюдений

Inborn errors of immunity (IEI) are rare heterogeneous diseases with a wide range of clinical manifestations that makes its diagnosis difficult for practitioners of different specialties. The purpose of this research was to describe a series of clinical case reports of primary immunodeficiencies (PIDs) diagnosis using the TREC/KREC assay in a multidisciplinary hospital. In order to verify the diagnosis, TREC/KREC determination, immunophenotyping and molecular genetic studies were performed. Thirty-nine patients with alarming signs of PIDs were examined within a period of 18 months. Primary immunodeficiencies were identified in 6 (31.6%) children with congenital malformations, in 5 (50%) with resistant cytopenias and in 3 (30%) with severe infections. Syndromal IDs were diagnosed in 8 (57.1%), combined immunodeficiency in a single patient (7.1%), immune regulation defects in 2 (14.3%) and antibody formation disorders in 3 (21.5%). Abnormal TREC/KREC values were established in 9 (64.3%) patients with IEI at the early stages of diagnostic search. Thus, TREC/KREC assay is an effective method for IEI diagnosis within a multidisciplinary hospital.

Research on rolling bearing fault diagnosis method based on improved multi-source fusion convolutional neural network

Abstract As sensor technology advances, the variety and number of sensors increase, leading to the capture of more signals. Existing multi-source fusion methods often face issues such as increased model complexity or the failure to fully utilize the potential correlations among multi-sensor data, thereby affecting the accuracy and reliability of fault diagnosis. To address this issue, this paper proposes a multi-source fusion convolutional neural network (MFCNN) that diagnoses bearing faults by integrating features from multi-source signals. Firstly, multiple convolution blocks with gradually increasing one-dimensional kernel sizes are utilized to extract features from the integrated multi-source data. This approach enhances feature extraction efficiency and simplifies the network architecture. Secondly, a feature fusion based on the CBAM attention mechanism is proposed, which refines feature representation through channel and spatial attention modules. This makes the model more focused on important information, thereby improving recognition accuracy. The diagnostic capabilities of the proposed MFCNN are evaluated utilizing two datasets.

Class-Imbalanced Machinery Fault Diagnosis Using Heterogeneous Data Fusion Support Tensor Machine

The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition, which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis. On the other hand, the vectorization of multi-source sensor signals may not only generate high-dimensional vectors, leading to increasing computational complexity and overfitting problems, but also lose the structural information and the coupling information. This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine (STM) driven by heterogeneous data fusion. The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters. A third-order heterogeneous feature tensor is designed based on multi sensors, frequency band components and statistical parameters. STM based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis. A series of comparative experiments verify the advantages of the proposed method. Conflict of Interest Statement The authors declare no conflicts of interest.