Search Results (129)

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions. Full article

Ships play a crucial role in modern society, serving purposes such as marine transportation, tourism, and exploration. Malfunctions or defects in the main engine, which is a core component of ship operations, can disrupt normal functionality and result in substantial financial losses. Consequently, early fault diagnosis of abnormal engine conditions is critical for effective maintenance. In this paper, we propose a deep hybrid model for fault diagnosis of ship main engines, utilizing exhaust gas temperature data. The proposed model utilizes both time-domain features (TDFs) and time-series raw data. In order to effectively extract features from each type of data, two distinct feature extraction networks and an attention module-based classifier are designed. The model performance is evaluated using real-world cylinder exhaust gas temperature data collected from the large ship low-speed two-stroke main engine. The experimental results demonstrate that the proposed method outperforms conventional methods in fault diagnosis accuracy. The experimental results demonstrate that the proposed method improves fault diagnosis accuracy by 6.146% compared to the best conventional method. Furthermore, the proposed method maintains superior performanceeven in noisy environments under realistic industrial conditions. This study demonstrates the potential of using exhaust gas temperature using a single sensor signal for data-driven fault detection and provides a scalable foundation for future multi-sensor diagnostic systems. Full article

Background and Clinical Significance: Evaluating wide complex rhythms in patients with permanent pacemakers can be a diagnostic challenge, particularly when the rhythm is irregular. While pacemaker-mediated rhythms are typically regular and predictable, the appearance of wide complex irregular rhythms raises concerns ranging from lead malfunction to life-threatening arrhythmias, such as ventricular tachycardia. Understanding the interplay between intrinsic cardiac activity and device function is crucial for timely and accurate diagnosis in this increasingly common clinical scenario. Case presentation: We report on a 74-year-old female with a VVI pacemaker implanted for binodal disease, who presented with intermittent palpitations and an irregular rhythm. The patient has a recent history of falling on her right shoulder, which is also the site of the device implantation. We used a clinical step-by-step approach to rule out pacemaker malfunction and to establish the need for an unscheduled device interrogation. Conclusions: This case presentation highlights the important role of clinical reasoning and the approach to such a patient, especially when a key method of pacemaker evaluation, such as device interrogation, is not readily available. Full article

In B-cell chronic lymphocytic leukemia (B-CLL), hypodiploidy is a rare but aggressive subtype of the disease with a very bad prognosis. Hypodiploidy, in contrast to normal B-CLL chromosomal aberrations, is marked by widespread genomic instability, which promotes treatment resistance and quick illness development. Its persistence after treatment implies that chromosomal loss gives cancerous clones a selection edge, which is made worse by telomere malfunction and epigenetic changes. Since thorough genetic profiling has a major impact on patient outcomes, advanced diagnostic methods are crucial for early detection. Treatment approaches must advance beyond accepted practices because of its resistance to traditional medicines. Hematopoietic stem cell transplantation (HSCT) and chimeric antigen receptor (CAR) T-cell therapy are two potential new therapeutic modalities. Relapse and treatment-related morbidity continue to be limiting concerns, despite the noteworthy improvements in outcomes in high-risk CLL patients receiving HSCT. Although more research is required, CAR T-cell treatment is effective in treating recurrent B-ALL and may also be used to treat B-CLL with hypodiploidy. Novel approaches are essential for enhancing patient outcomes and redefining therapeutic success when hypodiploidy challenges established treatment paradigms. Hypodiploidy is an uncommon yet aggressive form of B-CLL that has a very bad prognosis. Hypodiploidy represents significant chromosomal loss and structural imbalance, which contributes to a disordered genomic environment, in contrast to more prevalent cytogenetic changes. This instability promotes resistance to certain new drugs as well as chemoimmunotherapy and speeds up clonal evolution. Its persistence after treatment implies that hypodiploid clones have benefits in survival, which are probably strengthened by chromosomal segregation issues and damaged DNA repair pathways. Malignant progression and treatment failure are further exacerbated by telomere erosion and epigenetic dysregulation. The need for more sensitive molecular diagnostics is highlighted by the fact that standard karyotyping frequently overlooks hypodiploid clones, particularly those concealed by endoreduplication, despite the fact that these complications make early and correct diagnosis crucial. Hypodiploidy requires a move toward individualized treatment because of their link to high-risk genetic traits and resistance to conventional regimens. Although treatments like hematopoietic stem cell transplantation and CAR T-cells show promise, long-term management is still elusive. To improve long-term results and avoid early relapse, addressing this cytogenetic population necessitates combining high-resolution genomic technologies with changing therapy approaches. Full article

Background: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfunctioning, so its timely fault prediction and accurate diagnosis are imperative. Traditional fault detection methods rely on manual feature extraction, limiting their ability to identify complex faults, and deep learning methods suffer from unstable recognition accuracy. To address these issues, a three-class fault detection method for water pumps based on a convolutional neural network, transformer, and bidirectional gated recurrent unit (CNN-transformer-BiGRU) is proposed here. Methods: It first uses the continuous wavelet transform to convert one-dimensional vibration signals into time–frequency images for input into a CNN to extract the time-domain and frequency-domain features. Next, the transformer enhances the model’s hierarchical learning ability. Finally, the BiGRU captures the forward/backward feature information in the signal sequence. Results: The experimental results show that this method’s accuracy in fault detection is 91.43%, significantly outperforming traditional machine learning models. Using it improved the accuracy, precision, and recall by 1.86%, 1.97%, and 1.86%, respectively, relative to the convolutional neural network and long short-term memory (CNN-LSTM) model. Conclusions: Hence, the proposed model has superior performance indicators. Applying it to aquaculture systems can effectively ensure their stable operation. Full article

Background/Objectives: The prediction of cancer types is primarily reliant on driver genes and their specific mutations. The advancement in novel omics technologies has led to the acquisition of additional genetic data. When integrated with artificial intelligence models, there is considerable potential for this to enhance the accuracy of cancer diagnosis. As mutational signatures can provide insights into repair mechanism malfunctions, they also have the potential for more accurate cancer diagnosis. Methods: First, we compared unsupervised and supervised machine learning approaches to predict cancer types. We employed deep and artificial neural network architectures with an explainable component like layerwise relevance propagation to extract the most relevant features for the cancer-type prediction. Ten-fold cross-validation and an extensive grid search were used to optimize the neural network architecture using driver gene mutations, mutational signatures and topological mutation information as input. The PCAWG dataset was used as input to discriminate between 17 primary sites and 24 cancer types. Results: Overall, our approach showed that the most relevant mutation information to discriminate between cancer types is increased by >10% using the whole genome or intergenic and intronic genome regions instead of exome information. Furthermore, the most relevant features for most cancer types, except for two, are in the mutational signatures and not the topological mutation information. Conclusions: Informative mutational signatures outperformed the prediction of cancer types in comparison to driver gene mutations and added a new layer of diagnostic information. As the degree of information within the mutational signatures is not solely based on the frequency of occurrence, it is even possible to separate cancer types from the same primary site by the different relevant mutations. Furthermore, the comparison of informative mutational signatures allowed the cancer-type assignment of specific impaired repair mechanisms. Full article

Nowadays, electric motors are an integral part of most modern electromechanical systems that are used in industry. It follows that industrial processes are becoming more dependent on their efficiency. If faults in electric motors are not rectified, they can lead to malfunctions and accidents, as well as production downtime. Symmetry of a three-phase system means that the voltage and current in the three phase conductors are equal to each other, with a period of 120°. Asymmetry occurs if one of these conditions or both conditions are violated at the same time. In most cases, asymmetry is caused by loads. Predictive diagnostics is the most effective way to identify motor faults while the motor is in operation and prevent the likelihood of failure. Predictive diagnostics can identify problems that could lead to major failures, thus reducing production downtime and maintenance costs. The paper discusses the control and diagnosis of electric motors using prediction techniques. In particular, the use of neural network models and predictive control to improve accuracy and reliability is investigated. The main objective of this research is to develop a neural network controller based on predictive model predictive control (MPC), which will improve the quality of the control and diagnostics system of electric motors, ensuring their stability and preventing possible malfunctions. Full article

The Constrained Disorder Principle (CDP) offers a new framework for understanding how biological systems use and manage noise to maintain optimal functionality. This review explores the relationship between noise and biological systems at various scales, including genetic, cellular, and organ levels, and its implications for system malfunctions. According to the CDP, all systems require an optimal range of noise to function appropriately, and disease states can arise when these noise levels are disrupted. This review presents evidence supporting this principle across different biological contexts, such as genetic variability, cellular behavior, brain functions, human behavior, aging, evolution, and drug administration. For accurate clinical assessments, it is essential to distinguish between technical variability and intrinsic biological variability. When noise is adequately constrained, it serves as a fundamental mechanism for system adaptation and optimal functioning rather than simply a source of disruption. These findings have important implications for developing more effective therapeutic strategies and understanding biological systems’ dynamics. CDP-based second-generation artificial intelligence systems can help regulate noise levels to address malfunctions. These systems have improved clinical outcomes in various conditions by incorporating controlled randomness. Understanding these patterns of variability has significant implications for diagnosis, treatment monitoring, and the development of more effective therapeutic strategies across various medical conditions. Full article

Background/Objectives: Primary ciliary dyskinesia (PCD) (OMIM: 244400) is a hereditary, rare disorder with a high prevalence in Turkey due to a high rate of consanguinity. The disorder is caused by malfunctioning motile cilia and is characterized by a variety of clinical symptoms including sinusitis, otitis media and chronic obstructive pulmonary disease. This study presents the first assessment of the efficacy of immunofluorescence (IF) labeling for diagnosing PCD in Turkey by correlating IF with clinical observations when genetic data are scarce. Methods: We have a cohort of 54 PCD-suspected individuals with an age range of 5–27 years classified into two groups: group A with available genomic data (8 individuals) and group B with no available genomic data (46 individuals). We performed immunofluorescence analysis to confirm the pathogenicity of the variants in individuals with a prior genetic diagnosis and to confirm a PCD diagnosis in individuals with typical PCD symptoms and no genetic diagnosis. Results: All individuals had airway infections and displayed clinical symptoms of PCD. Our data revealed an absence of outer dynein arm dynein heavy chain DNAH5 in individuals with pathogenic variants in DNAH5 and DNAAF1 and in 17 other PCD-suspected individuals, an absence of nexin–dynein regulatory complex component GAS8 in 8 PCD-suspected individuals, an absence of outer dynein arm dynein heavy chain DNAH11 in 6 PCD-suspected individuals and an absence of radial spoke head component RSPH9 in 2 PCD-suspected individuals. Furthermore, the pathogenicity of ARMC4 variants was confirmed by the absence of the outer dynein arm docking complex component ARMC4 and the proximal localization of DNAH5. Conclusions: Immunofluorescence analysis, owing to its lower cost and quicker turnaround time, proves to be a powerful tool for diagnosing PCD even in the absence of genetic data or electron microscopy results. Full article

In practical engineering, due to the residual magnetism in the iron core of the converter transformer, the inrush current is generated in the case of no-load closing, and the inrush current leads to an oil flow surge inside the converter transformer. Under the influence of oil flow, the gas relay plate will be deflected, and when the deflection angle is too large, the gas relay will malfunction. Because of the lacking research on the influence of the misoperation of the gas relay under the excitation inrush of the converter transformer, it is difficult to effectively suppress the misoperation of gas relay under inrush current. Therefore, the finite element model of the electromagnetic hydraulic coupling of the converter transformer is established in this paper to obtain the oil flow velocity through the gas relay under the inrush current. A fluid–solid coupling model was established inside the gas relay to study the deflection characteristics of the baffle of the gas relay under the inrush current, and the relationship between the inrush current amplitude and the deflection angle of the baffle was mathematically fitted to effectively predict whether the gas relay has the risk of misoperation. From the experimental results, it can be seen that when the amplitude of the inrush current exceeds 6.37 kA, the gas relay will have the risk of misoperation. Finally, the influence of oil flow impact on the gas relay baffle under multiple cycles is considered. The results can provide an effective reference for analyzing the diagnosis of gas relay misoperation under the effect of inrush current. Full article

Aircraft sensors are crucial for ensuring the safe and efficient operation of aircraft. However, these sensors are vulnerable to external factors that can lead to malfunctions, making fault diagnosis essential. Traditional deep learning-based fault diagnosis methods often face challenges, such as limited data representation and insufficient feature extraction. To address these problems, this paper proposes an enhanced GraphSage-based fault diagnosis method that incorporates attention mechanisms. First, signal data representing the coupling characteristics of various sensors are constructed through data stacking. These signals are then transformed into graph data with a specific topology reflecting the overall sensor status of the aircraft using K-nearest neighbor and Radius classification algorithms. This approach helps fully leverage the correlations between data points. Next, node and neighbor information is aggregated through graph sampling and attention-based aggregation methods, strengthening the extraction of fault features. Finally, fault diagnosis is performed using multi-layer aggregation and transformation within fully connected layers. Experiments demonstrate that the proposed method outperforms baseline approaches, achieving better detection performance and faster computational speed. The method has been validated on both simulated and real-flight data. Full article

Thermoeconomic diagnosis methods are designed to identify faulty components and evaluate the economic implications of these faults. However, these diagnostic techniques often struggle to filter out interference from induced factors during the diagnosis process. When multiple components malfunction simultaneously, these methods may fail to effectively identify all the faulty components. To address these challenges, this article introduces an improved thermoeconomic diagnosis method that integrates the traditional diagnosis method with the operational characteristic curves of the components. This improved method facilitates a more precise differentiation between the impacts of faults on each component, categorizing them into intrinsic and induced parts. The intrinsic part arises from the component’s inherent failure, while the induced part results from interactions among different components or adjustments made by the control system. The improved method generates fault diagnosis indicators and economic assessment indicators based on this classification, allowing for the identification of faulty components and the evaluation of the economic consequences of these faults. The proposed method was tested on a MAN 6S50 MC-C8 diesel engine and validated under two real operating conditions, where multiple faults were intentionally introduced in various components. The results demonstrated that the new method accurately identified all faulty components within the marine diesel engine and assessed the economic impacts of these faults. Full article

Despite their significant environmental benefits, solar photovoltaic (PV) systems are susceptible to malfunctions and performance degradation. This paper addresses detecting and diagnosing faults from a dataset representing a 250 kW PV power plant with three types of faults. A comprehensive dataset analysis is conducted to improve the dataset quality and uncover intricate relationships between features and the target variable. By introducing novel feature importance averaging techniques, a two-phase fault detection and diagnosis framework employing tree-based models is proposed to identify faults from normal cases and diagnose the fault type. An ensemble of six tree-based classifiers, including decision trees, random forest, Stochastic Gradient Boosting, LightGBM, CatBoost, and Extra Trees, is trained in both phases. The results show 100% accuracy in the first phase, particularly with the Extra Trees classifier. In the second phase, Extra Trees, XGBoost, LightGBM, and CatBoost achieve similar accuracy, with Extra Trees demonstrating superior training and convergence speed. This study then incorporates Explainable Artificial Intelligence (XAI), utilizing LIME and SHAP analyzers to validate the research findings. The results highlight the superiority of the proposed approach over others, solidifying its position as an innovative and effective solution for fault detection and diagnosis in PV systems. Full article

Rolling bearings, as critical components of rotating machinery, directly affect the reliability and efficiency of the system. Due to extended operation under high load, harsh environmental conditions, and continuous use, bearings become more susceptible to failure, leading to a higher likelihood of malfunction. To prevent sudden failures, reduce downtime, and optimize maintenance strategies, early and accurate diagnosis of rolling bearing faults is essential. Although existing methods have achieved certain success in processing acoustic and vibration signals, they still face challenges such as insufficient feature fusion, inflexible weight allocation, lack of effective feature selection mechanisms, and low computational efficiency. To address these challenges, we propose a dynamic weighted multimodal fault diagnosis model based on the fusion of acoustic and vibration information. This model aims to enhance feature fusion, dynamically adapt to signal characteristics, optimize feature selection, and reduce computational complexity. The model incorporates an adaptive fusion method based on a multi-branch convolutional structure, enabling unified processing of both acoustic and vibration signals. At the same time, a cross-modal dynamic weighted fusion mechanism is employed, allowing the real-time adjustment of weight distribution based on signal characteristics. By utilizing an attention mechanism for dynamic feature selection and weighting, the robustness of classification is further improved. Additionally, when processing acoustic signals, a depthwise separable convolutional network is used, effectively reducing computational complexity. Experimental results demonstrate that our method significantly outperforms other algorithms in terms of convergence speed and final performance. Additionally, the accuracy curve during training showed minimal fluctuation, reflecting higher robustness. The model achieved over 99% diagnostic accuracy under all signal-to-noise ratio (SNR) conditions, showcasing exceptional robustness and noise resistance in both noisy and high-SNR environments. Furthermore, its superiority across different data scales, especially in small-sample learning and stability, highlights its strong generalization capability. Full article

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter’s ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability. This article proposes a fault diagnosis strategy for LLC resonant converters utilizing a reduced-order interval sliding mode observer. Initially, an augmented generalized system for the LLC resonant converter is developed to convert current sensor faults into generalized state vectors. Next, the application of matrix transformations plays a critical role in decoupling open-circuit faults from the inverter system’s state and current sensor faults. To achieve accurate estimation of phase currents and detection of current sensor faults, a reduced-order interval sliding mode observer has been designed. Building upon the estimation results generated by this observer, a diagnostic algorithm featuring adaptive thresholds has been introduced. This innovative algorithm effectively differentiates between current sensor faults and open switch faults, enhancing fault detection accuracy. Furthermore, it is capable of localizing faulty power switches and estimating various types of current sensor faults, thereby providing valuable insights for maintenance and repair. The robustness and effectiveness of the proposed fault diagnosis algorithm have been validated through experimental results and comparisons with existing methods, confirming its practical applicability in real-world inverter systems. Full article