Framework For Diagnosis Research Articles

Self-Supervised Simple Siamese Framework for Fault Diagnosis of Rotating Machinery With Unlabeled Samples.

Fault diagnosis is vital to ensuring the security of rotating machinery operations. While fault data obtained from mechanical equipment for this issue are often insufficient and of no labels. In this case, supervised algorithms cannot come into play. Hence, this article proposes a self-supervised simple Siamese framework (SSF) for bearing fault diagnosis based on the contrastive learning algorithm SimSiam which uses a simplified Siamese network to find the distinguishable features of different fault categories. SSF consists of a weight-sharing encoder applied on two inputs, a nonlinear predictor and a linear classifier. SSF learns invariant characteristics of fault samples via maximizing the similarity between two views of each inputted sample. Several data augmentation (DA) methods for vibration signals, which provide different sample views for the model, are also studied, for it is crucial for contrastive learning. After fine-tuning the learned encoder and a linear layer classifier with a small subset of labeled data (1%-5% of the total samples), the network achieves satisfactory performance for bearing fault diagnosis. A series of experiments based on the data from three different scenarios are used to verify the proposed methods, getting 100%, 99.38%, and 98.87% accuracy separately.

A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion

In large-scale power systems, accurately detecting and diagnosing the type of faults when they occur in the grid is a challenging problem. The classification performance of most existing grid fault diagnosis methods depends on the richness and reliability of the data, in addition, it is difficult to obtain sufficient feature information from unimodal circuit signals. To address these issues, we propose a deep residual convolutional neural network (DRCNN)-based framework for grid fault diagnosis. First, we design a comprehensive information entropy value (CIEV) evaluation metric that combines fuzzy entropy (FuzEn) and mutual approximation entropy (MutEn) to integrate multiple decomposition subsequences. Then, DRCNN and heterogeneous graph transformer (HGT) are constructed for extracting multimodal features and considering modal variability. In addition, to obtain the implicit information of multimodal features and control the degree of their performance, we propose to incorporate the cross-modal attention fusion (CMAF) mechanism in the synthesis framework. We validate the proposed method on the three-phase transmission line dataset and VSB power line dataset with accuracies of 99.4 % and 99.0 %, respectively. The proposed method also achieves superior performance compared to classical and state-of-the-art methods.

Disentangle Then Calibrate With Gradient Guidance: A Unified Framework for Common and Rare Disease Diagnosis.

The computer-aided diagnosis (CAD) for rare diseases using medical imaging poses a significant challenge due to the requirement of large volumes of labeled training data, which is particularly difficult to collect for rare diseases. Although Few-shot learning (FSL) methods have been developed for this task, these methods focus solely on rare disease diagnosis, failing to preserve the performance in common disease diagnosis. To address this issue, we propose the Disentangle then Calibrate with Gradient Guidance (DCGG) framework under the setting of generalized few-shot learning, i.e., using one model to diagnose both common and rare diseases. The DCGG framework consists of a network backbone, a gradient-guided network disentanglement (GND) module, and a gradient-induced feature calibration (GFC) module. The GND module disentangles the network into a disease-shared component and a disease-specific component based on gradient guidance, and devises independent optimization strategies for both components, respectively, when learning from rare diseases. The GFC module transfers only the disease-shared channels of common-disease features to rare diseases, and incorporates the optimal transport theory to identify the best transport scheme based on the semantic relationship among different diseases. Based on the best transport scheme, the GFC module calibrates the distribution of rare-disease features at the disease-shared channels, deriving more informative rare-disease features for better diagnosis. The proposed DCGG framework has been evaluated on three public medical image classification datasets. Our results suggest that the DCGG framework achieves state-of-the-art performance in diagnosing both common and rare diseases.

DecFFD: A Personalized Federated Learning Framework for Cross-Location Fault Diagnosis

DecFFD: A Personalized Federated Learning Framework for Cross-Location Fault Diagnosis

IMWMOTE: A novel oversampling technique for fault diagnosis in heterogeneous imbalanced data

In the realm of smart manufacturing, the urgent necessity of surmounting the technical hurdles associated with developing data-driven fault monitoring and intelligent diagnostic technologies for high-end equipment is imminent. In practical engineering applications, the challenging operating conditions and unfavorable environments for data collection often lead to the acquisition of health data that is not only constrained in terms of sample size but also exhibits imbalances and heterogeneity, collectively referred to as Small-Sample, Imbalanced, and Mixed-Class Scenarios. This poses a considerable challenge to the interpretability modeling of faults in high-end equipment, as identified by the scientific community. Prior investigations have highlighted the distinct benefits of resampling techniques in addressing the complexities of imbalanced fault modeling. Nevertheless, the suboptimal performance of current techniques remains a concern, primarily due to ambiguous representations of sample feature heterogeneity and the lack of sophisticated sampling balance strategies. For instance, the absence of tailored strategies and concomitant optimization approaches specifically designed for each heterogeneous feature remains a notable gap. In this study, we undertake a comprehensive analysis of the heterogeneity mechanisms inherent in fault feature data, with a particular emphasis on components like turbine rotor faults. Building upon these insights, we introduce a fault diagnosis framework utilizing the novel oversampling technique based-MWMOTE (IMWMOTE), to address these challenges effectively. This comprehensive framework not only ensures interpretability in classification but also seamlessly integrates an optimization mechanism. Notably, the efficacy and innovation of our proposed approach have been thoroughly verified through an extensive evaluation involving 20 fault diagnosis instances across three distinct categories: turbine rotor, bearings, and gears. The experimental outcomes clearly illustrate that IMWMOTE exhibits remarkable advantages in tackling the intricate challenges associated with imbalanced fault modeling, particularly when dealing with small sample heterogeneity. Given its demonstrated proficiency, the proposed approach holds significant potential in guiding interpretable fault modeling efforts in related domains, thus contributing to the advancement of fault diagnosis techniques.

CSDANet: a new lightweight fault diagnosis framework towards heavy noise and small samples

CSDANet: a new lightweight fault diagnosis framework towards heavy noise and small samples

A multi-output fault diagnosis framework for hydraulic system using a CNN-SVM hierarchical learning strategy

Achieving asymptotic and concurrent fault diagnosis in hydraulic system remains a challenging endeavor due to the inherent attributes of the hidden occurrence, simultaneous manifestation, coupling, and limited sample size. To address the above issues, this paper proposes a hierarchical multi-output fault detection and diagnosis framework, namely, HMDF, based on a hierarchical learning strategy to leverage an improved convolutional neural network (CNN) and support vector machine (SVM). Both a multi-channel CNN and a multi-branch CNN are employed to extract and downscale features collected by the sensors at diverse sampling frequencies first, and then, such features are subsequently subjected to classification using SVM. The hierarchical learning strategy enables the identification of different fault states, both at the component and the intra-component level. Additionally, a modified whale optimization algorithm is also utilized to optimize the classification process of SVM. Extensive experiments are conducted to test the proposed HMDF with the hydraulic system datasets. Results show that HMDF achieves a diagnostic accuracy of up to 98.9% for the dataset, surpassing traditional methods reliant on manual extraction of time–frequency features, and it also exhibits superior classification performances with a small sample size. The HMDF is expected to offer a generalized framework for the multi-output fault detection and diagnosis in hydraulic systems and other complex components.

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

SummaryNode faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts.

A novel diagnostic framework based on vibration image encoding and multi-scale neural network

Intelligent fault diagnosis employing CNN-based techniques has demonstrated promising results in rotating machinery maintenance and management. However, most approaches that rely on time-series vibration signals fail to incorporate the physical knowledge of faults into feature learning, and are subject to obtaining the correlation between input and output characteristics. Furthermore, it is challenging for the neural network to improve feature extraction due to the rigid convolution kernel and the limited receptive field. To address these issues, an intelligent fault diagnosis framework is developed based on vibration image encoding and multi-scale neural networks. Firstly, a novel image encoding rule is designed to convert time-series signals into vibration images, which incorporate the impact characteristics of faults into the model inputs to enhance fault information. Secondly, a multi-scale feature fusion network with dilatated and irregular sampling convolution block is constructed for feature extraction and classification. In addition, a new regularization term is designed to avoid training overfitting. Finally, Grad-CAM is introduced to visualize the region of interest of the model. The proposed method is verified on the datasets with different fault types from our laboratory and DIRG bearing dataset with different fault severities from Politecnico di Torino. The experimental results demonstrate that our diagnostic framework exhibits good generalization and noise robustness, and can achieve high-precision fault diagnosis.

DTN-MTLF: A dual-teacher network based multi-task learning framework for unbiased glaucoma diagnosis

DTN-MTLF: A dual-teacher network based multi-task learning framework for unbiased glaucoma diagnosis

Craniocervical dissociation: A framework for diagnosis and management of a rare but never miss injury

Craniocervical dissociation (CCD) is a potentially devastating injury resulting from osseoligamentous injury to the craniovertebral junction and is associated with significant neurologic morbidity and mortality. CCD may be difficult to diagnose due to subtle imaging findings, and delayed diagnosis can lead to further neurologic decline. CCD is a surgical emergency and should be securely stabilized until definitive surgical rigid internal fixation can be performed. The current article will provide a framework for the management of CCD, with a particular emphasis on the critical first step of diagnosis.

Advanced digital twin-enabled fault diagnosis framework for unmanned vehicle systems

The control system of unmanned vehicles must demonstrate strong capability to promptly diagnose and address system faults. Such a capability can improve transportation efficiency, ensure the smooth execution of production tasks, and to a certain extent, mitigate the risk of human casualties. To ensure the upkeep of unmanned vehicles and address the diagnostic requirements of control systems, this study integrates traditional wheeled vehicle control systems with digital twin (DT) technology to establish a framework for control system fault diagnosis and maintenance, with the primary objective of fulfilling the fault diagnosis task. By this framework, a method for detecting faults in unmanned vehicle control systems based on DT technology has been developed. This method involves the design of a data-driven model using multiple sensors and the application of a DT-improved particle filter fault diagnosis algorithm, utilizing a multi-domain model approach. A case study of the proposed method and simulation results are presented to illustrate its feasibility.

Modeling question difficulty for unbiased cognitive diagnosis: A causal perspective

Cognitive diagnosis is an intelligent education task that aims to learn students’ cognitive states on knowledge concepts based on historical answering logs over questions. Existing studies focus on modeling the interactions between students and questions through either manual-designed functions (e.g., logistic function) or complex neural network structures. However, such studies neglect the question difficulty bias, i.e., questions exhibit uneven distribution on the answering frequency, as simple questions are answered more times than difficult ones for the given concept. To tackle this issue, we present a Causal Cognitive Diagnosis Framework (CausalCDF), which considers the question difficulty bias and could be readily integrated with traditional diagnostic models for better cognitive diagnosis. Specifically, we first analyze the effect of question difficulty (acting as the confounder) on student performance via a causal graph. Then we eliminate the bad effect of the confounding difficulty bias via causal intervention in model training. We instantiate CausalCDF on five representative diagnostic models and perform extensive experiments on two real-world datasets. Empirical studies prove the effectiveness of CausalCDF compared to existing studies.

Hybrid Optimization Model Integrating Gradient Descent and Stochastic Descent for Enhanced Osteoporosis and Osteopenia Recognition

Osteoporosis and osteopenia, prevalent bone diseases affecting millions of people globally, necessitate accurate early diagnosis for effective treatment and fracture prevention. This paper proposes a novel hybrid optimization algorithm tailored for classifying these conditions based on Bone Mineral Density (BMD) measurements. The algorithm, a customized Mini-Batch Gradient Descent (MBGD), blends the advantages of Gradient Descent (GD) and Stochastic Gradient Descent (SGD), addressing specific needs for osteoporosis and osteopenia classification. Utilizing a dataset comprising BMD measurements and clinical risk factors from the Osteoporotic Fractures in Men (MrOS), Study of Osteoporotic Fractures (SOF), and Fracture Risk Assessment (FRAX), the model achieves an impressive accuracy of 99.01%. The proposed model outperforms existing methods, demonstrating superior accuracy compared to the accuracy obtained in Gradient Descent of 97.26%, Stochastic Gradient Descent of 97.23%, and other optimization algorithms such as Adam of 96.45% and the RMSprop of 96.23%. This hybrid model presents a robust framework for early diagnosis of Osteoporosis and osteopenia, and hence there is an enhancement in quality of life.

A universal fault diagnosis framework for marine machinery based on domain adaptation

In the digital and automated era, the significance of machine health monitoring has been amplified, especially for marine machinery, which plays a pivotal role in the sustainable advancement of modern industries. Two salient challenges currently hampering fault diagnostics are spotlighted: firstly, the variability in mechanical operating conditions gives rise to inconsistent data distributions, thereby prompting domain shift. Secondly, unobserved in training datasets, testing scenarios may unveil unpredictable, unknown fault modes, culminating in class discrepancies. To effectively tackle these intertwined challenges, we introduce the Two-Stage Universal Domain Adaptation (TSUDA) — a novel fault diagnosis methodology. TSUDA adeptly differentiates new fault types from labeled known counterparts by leveraging a composite transferability metric. Furthermore, it deploys a parameter-adaptive unsupervised algorithm to discern the count of emerging fault types. Rigorous experiments on datasets from marine diesel engines, emblematic of marine machinery, demonstrated TSUDA's superiority over conventional domain adaptation techniques in diverse diagnostic challenges, registering accuracy rates surpassing 90%. While the effectiveness of TSUDA is authenticated within marine machinery contexts in this investigation, its architecture suggests potential applicability in a broader spectrum of mechanical fault diagnostic scenarios.

EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning.

Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.e., different symptoms or varying stages of severity) among patients with ASD, and the non-explainability of the decision process. To cover these limitations, we propose a novel explainability-guided region of interest (ROI) selection (EAG-RS) framework that identifies non-linear high-order functional associations among brain regions by leveraging an explainable artificial intelligence technique and selects class-discriminative regions for brain disease identification. The proposed framework includes three steps: (i) inter-regional relation learning to estimate non-linear relations through random seed-based network masking, (ii) explainable connection-wise relevance score estimation to explore high-order relations between functional connections, and (iii) non-linear high-order FC-based diagnosis-informative ROI selection and classifier learning to identify ASD. We validated the effectiveness of our proposed method by conducting experiments using the Autism Brain Imaging Database Exchange (ABIDE) dataset, demonstrating that the proposed method outperforms other comparative methods in terms of various evaluation metrics. Furthermore, we qualitatively analyzed the selected ROIs and identified ASD subtypes linked to previous neuroscientific studies.

An Enhanced Modular-Based Neural Network Framework for Effective Medical Diagnosis

An Enhanced Modular-Based Neural Network Framework for Effective Medical Diagnosis

Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.

Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported. Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy. We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi-network fusion object detection framework based on the object detection technique Faster R-CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi-network fusion object detection framework was compared with that between dermatologists. A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi-network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses. Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi-network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis.

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.

A deep learning framework for accurate diagnosis of colorectal cancer using histological images

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.