Method For Diagnosis Research Articles

Relation between fault characteristic frequencies and local interpretability shapley additive explanations for continuous machine health monitoring

Recently, the Shapley additive explanations models have been extensively studied to enhance explainability of artificial intelligence algorithms, while most of them simply use Shapley additive explanations to rank or measure the importance of different features. In this study, a novel methodology that studies the relation between fault characteristic frequencies and Shapley values generated by local interpretability Shapley additive explanations for machine health monitoring is proposed. Firstly, a simulation model is introduced to generate vibration signals at different health conditions and their spectral amplitudes transformed from Fourier transform are used to investigate the relationship between fault characteristic frequencies and local interpretability Shapley values. It is interestingly found that Shapley values can be used to locate fault characteristic frequencies. Moreover, most of them have negative values in a normal stage and have positive values in an abnormal stage. Based on this finding and Shapley additive explanations, a health indicator construction methodology is proposed to continuously monitor incipient machine faults. Subsequently, an automatic signal filtering method is proposed to remove and eliminate burrs and noise in Shapley values so that fault characteristic frequencies can be clearly revealed by Shapley values for physical fault diagnosis. Two run-to-failure cases are conducted to demonstrate the effectiveness of the proposed methodology and then the superiority of this study is demonstrated by comparing with existing methods for health indicator construction and fault diagnosis, including sparsity parameters, Hjorth parameters, and fast Kurtogram. Comparison results show that the proposed health indicator is more sensitive to the time of incipient fault initiation and interpretable fault diagnosis based on Shapley values has a robust performance. This study first sheds a light on the relationship between fault characteristic frequencies and Shapley values under the scenario of continuous machine health monitoring and seamlessly guides applicants to realize Shapley additive explanations based incipient fault detection and diagnosis.

A knowledge-based fault diagnosis method for rolling bearings without fault sample training

A knowledge-based fault diagnosis method for rolling bearings without fault sample training

Applications of Fourier transforms in engineering

The Fourier transform was proposed by Fourier in 1807. Fourier transform is a method of analyzing signals, which can analyze the components of the signal or synthesize the signal using these components. Many waveforms can be used as signal components, such as sine waves, square waves, sawtooth waves, etc. The Fourier transform uses sine waves as components of the signal. Due to its excellent properties, the Fourier transform has a wide range of applications in physics, number theory, combinatorial mathematics, signal processing, probability, statistics, cryptography, acoustics, optics, and other fields. This paper focuses on the study of fractional-order Fourier transform in the engineering field, for the equipment of the tiny fault diagnosis method, and according to some existing diagnostic methods, put forward the idea of diagnostic method enhancement.

Penicillin Allergy in China: Consequences of Inappropriate Skin Testing Practices and Policies.

Penicillins are the most frequently prescribed class of medications worldwide and first-line antibiotic of choice for most bacterial infections. They are also commonly labelled as the culprit of drug 'allergy'; leading to obligatory use of second-line antibiotics, suboptimal antibiotic therapy and increased antimicrobial resistance. However, the majority of reported penicillin 'allergy' labels are found to be incorrect after allergy testing, emphasising the importance of proper drug allergy testing and evaluation. Penicillin skin testing (PST) remains an important component of drug allergy diagnosis; however, its practice and policies significantly differ across the world. Inappropriate and non-evidence-based PST practices can lead to consequences associated with allergy mislabelling. Even within different regions of China, with a population exceeding 1.4 billion, there are marked differences in the implementation, execution and interpretation of PST. This review aims to examine the differences in PST between Mainland China, Hong Kong and the rest of the world. We critically analyse the current practice of 'pre-emptive' PST in Mainland China, which has a significant false-positive rate leading to high levels of penicillin allergy mislabelling. Non-evidence-based practices further compound the high false-positive rates of indiscriminatory PST. We postulate that inappropriate PST policies and practices may exacerbate the mislabelling of penicillin allergy, leading to unnecessary overuse of inappropriate second-line antibiotics, increasing antimicrobial resistance and healthcare costs. We advocate for the importance of more collaborative research to improve the contemporary workflow of penicillin allergy diagnosis, reduce mislabelling and promote the dissemination of evidence-based methods for allergy diagnosis.

Comparison of diagnostic methods for laboratory diagnosis of the zoonotic tapeworm Dipylidium caninum in cats

The tapeworm Dipylidium caninum is the most widely distributed cestode infecting dogs, cats, and sometimes humans, worldwide. The diagnosis of the infection caused by D. caninum is achieved via the visualization of proglottids in feces or with traditional microscopic tests, but both lack sensitivity. The present study has evaluated and compared the diagnostic performance of a PCR protocol on different feline biological samples to detect D. caninum. A sample of feces, a Scotch tape test from the perianal area, and a rectal swab were collected from a total of 100 privately owned cats from Italy and Greece. All fecal samples were subjected to macroscopic examination and to floatation. Based on the results of the above tests the cats were divided in three groups, i.e. (i) cats positive for D. caninum (regardless of positivity for other endoparasites (Group A; n = 50 cats), (ii) cats negative for D. caninum but infected by other helminths (Group B; n = 25 cats), and (iii) cats negative for intestinal endoparasites (Group C; n = 25 cats). For each sample, the DNA was extracted from feces, floatation supernatant, Scotch tape test and rectal swabs and subjected to PCR. For 33 cats from Group A, at least one sample type scored positive at PCR. Of these, all were PCR-positive in the floatation aliquot, while nine and one cats were positive by PCR on feces and Scotch tape test, respectively. Swabs were negative by PCR for all the cats. None of the samples from cats of Groups B and C was positive by any PCR. Sequences obtained from amplicons generated from samples of cats enrolled in Italy had 99–100 % identity with the recently described D. caninum feline genotype. The data presented here suggest that PCR could be a useful tool for diagnosing D. caninum infections, under certain circumstances, e.g. when proglottids are unidentified, unseen or overlooked, even though it has limitations, e.g. false negative results due to fecal PCR inhibitors, uneven distribution of parasitic elements, or to intermittent proglottid and/or egg shedding. Thus, it may not be, currently, the best diagnostic choice for dipylidiosis.

Comparison of Explainable Artificial Intelligence Model and Radiologist Review Performances to Detect Breast Cancer in 752 Patients.

Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis. In this study, we proposed an artificial intelligence (AI)-based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast-XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022. Six classifiers, support vector machine (SVM), K-nearest neighbor (K-NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast-XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (X2GAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model. The results obtained by radiologists and the X2GAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results.

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Abstract Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sample labeling, unsupervised learning leverages its inherent learning capabilities for detection. This paper introduces an innovative method for smart steel diagnosis, integrating joint optimization of feature extraction and clustering. The proposed approach merges mini-batch K-Means clustering with a feature extraction network to acquire pseudo-label information for current images. It employs a multi-view transformation strategy, enabling classification through the optimized feedback from pseudo-labels. This method allows the network to self-optimize the distinction of image features through backpropagation. The method exhibits a mere 4% classification failure rate for steel surface images. This significant reduction in additional data processing requirements enhances the inspection system's efficiency and accuracy. Furthermore, the versatility of this method extends beyond steel defect diagnosis. It holds potential for application in various engineering domains, particularly in scenarios characterized by data imbalance.

Key updates and interpretation of the ninth version of AJCC staging system for neuroendocrine tumors of the stomach

The cancer staging system of the American Joint Committee on Cancer (AJCC) is the most widely used clinical basis for tumor staging. In October 2023, AJCC released the staging system (ninth version) for the neuroendocrine tumors of stomach (NET), which has been implemented in January 2024. The ninth version of NET staging system mainly updated the histopathologic classification, diagnosis and staging methods, clinical and pathological staging, prognosis grade, tumor and non-tumor prognostic features. The update and implementation of the staging system provide a more detailed reference for the accurate diagnosis, staging and precise treatment of gastric neuroendocrine tumors. Moreover, it is convenient for clinicians to carry out clinical practice. The purpose of our article is to provide a high-level overview of the major changes in AJCC staging system (version 9) for gastric NET based on the latest evidence-based medical research.

Multi-source domain self-supervised enhanced transfer fault diagnosis approach with source sample refinement strategy

Existing research in multi-source transfer learning often overlooks the differences among source domain samples and fails to effectively utilize unknown target domain information. To address these limitations, this paper proposes a multi-source domain self-supervised enhanced transfer fault diagnosis method with source sample refinement strategy. Firstly, a joint training mechanism is introduced to train all source predictors, leveraging knowledge from all source domains. Subsequently, a source sample refinement strategy is devised, utilizing a category classifier in conjunction with predefined thresholds to filter out source samples highly similar to the target samples, thereby obtaining weights for each source domain. Then, a multi-level distribution matching mechanism is designed to reduce the distribution discrepancy between refined source samples and target samples, followed by the development of a self-supervised training mechanism to further enhance the cross-domain performance of source predictors. Finally, integrating the weights from each source domain, the trained source predictors are combined to derive the target predictor, yielding the final diagnostic results. Experimental results indicate that the proposed method can achieve better target alignment by leveraging fault information from the unknown target domain, effectively addressing cross-domain diagnosis challenges.

Joint diagnosis and validation of lightning risk for transmission lines by using multisource data

This study proposes an improved method for assessing lightning risk for typical 750-kV transmission lines in Northwest China. The method involves enhanced preprocessing of multisource parameters and a joint diagnosis of lightning risk; on the basis of the diagnosis results, a cause analysis is conducted to identify towers at high lightning risk. In this study, raw data on the following parameters were obtained and processed: line lightning withstand capability calculation parameters, lightning localization parameters, microtopography parameters, and distributed monitoring parameters. These data were then integrated into a comprehensive multisource database. Furthermore, this study established a model for calculating lightning withstand capability; the study considered the influence of altitude and optimized the classic electrogeometric model. The joint diagnosis method was validated using distributed monitoring data. Through this method, this study identified the characteristics of typical high-risk towers and the underlying factors contributing to the high lightning risk on transmission lines.

Incidentally detected asymptomatic splenic artery aneurysm

Splenic artery aneurysm (SpAA) is the most common among visceral artery aneurysms and accounts for approximately 60% of visceral artery aneurysms. Generally, 80% of cases with SpAA are asymptomatic. The most common symptoms are abdominal pain, nausea and intra-abdominal bleeding due to aneurysm rupture, hypotension and hemorrhagic shock. The most serious complication is aneurysm rupture and related complications. The most useful method for diagnosis is abdominal CT angiographic imaging. The treatment is mostly surgical, but in appropriate cases, endovascular intervention can also be applied. Medical follow-up can be performed in true splenic artery aneurysms less than 2 cm in diameter. In this article, we wanted to present a fifty-two-year-old female patient with an asymptomatic splenic artery aneurysm that was detected incidentally.

Automatic detection of pleural line and lung sliding in lung ultrasonography using convolutional neural networks

BackgroundLung ultrasonography (LUS) is a valuable diagnostic tool, but there is a shortage of LUS experts with extensive knowledge and significant experience in the field. Convolutional neural networks (CNNs) have the potential to mitigate this issue by facilitating computer-aided diagnosis. MethodsWe propose computer-aided system by a CNN-based method for LUS diagnosis. As the first consideration, we investigated pleural line and lung sliding. The pleural line indicates the position of pleura in an ultrasound image, and LUS is performed after first confirming the position of pleural line. Lung sliding defined as the movement of the pleural line, and the absence of this feature is associated with pneumothorax. ResultsOur proposed method accurately detected pleural line and lung sliding, demonstrating its potential to provide valuable diagnostic information on lung lesions.

A novel simulation-assisted transfer method for bearing unknown fault diagnosis

Abstract Supervised data-driven bearing fault diagnosis methods rely on completed datasets of faults, which can be challenging for signals collected in real engineering. Recognizing unknown faults using a data-driven approach is particularly difficult, as purposefully modeling these faults is complex. To address this challenge, this study proposes a new simulation-assisted transfer bearing unknown fault diagnosis method for realizing unknown compound fault diagnosis of rotating machinery. Firstly, finite element method is used to obtain the compound fault data that does not exist in the historical data, and wavelet packet transform is performed on the simulated and measured signals to enhance the detailed features of the signals. Then, a deep convolutional feature fusion network based on hybrid multi-wavelet spatial attention is constructed to fuse the time-frequency information processed by different wavelet bases. Finally, by integrating the concepts of intra-class splitting and transfer learning, the model is fine-tuned using simulation data to recognize unknown compound faults of rolling bearings. The method validates the simulated signals’ feasibility and the unknown faults’ diagnostic validity under the publicly available rolling bearings dataset. Compared to the comparison methods, the method’s accuracy increased by 2.86%, 2.61%, 5.41%, 4.77%, and 7.07%, respectively.

Spectral feature informed variational model and its applications to machinery fault diagnosis

Variational mode extraction (VME), a novel signal decomposition method based on a frequency-domain filter in essence, has recently become a potential tool in fault diagnosis. However, the original VME algorithm is not provided with full self-adaptation, and its performance in the extraction of fault features is subject to predefining the initial parameters, including initial center frequency (ICF) and balance parameter. To address these issues, a spectral feature informed variational model (SFIVM) algorithm is constructed to overcome the defects of parameters setting and efficiently realize the fault diagnosis without prior knowledge. Specifically, a spectral feature detector inspired by the convergence property of ICF is first developed to reveal the spectral features, including the detected center frequencies and boundary frequencies. Then, a balance parameter estimation formula is designed to adaptively determine the target balance parameter by taking advantage of the above spectral features. Finally, a highly efficient decomposition model is proposed to extract the fault-related mode from the vibration signal, where iterative optimization is unnecessary. The effectiveness of the proposed SFIVM method is verified by one simulated and two experimental cases. Moreover, its superiority and high efficiency are demonstrated by comparing it with some advanced and classical fault diagnosis methods.

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

For the poor model generalization and low diagnostic efficiency of fault diagnosis under imbalanced distributions, a novel fault diagnosis method using variational autoencoder generation adversarial network and improved convolutional neural network, named VGAIC-FDM, is proposed in this paper. First, to capture local features of vibration signals, continuous wavelet transform is employed to convert the original one-dimensional fault signals into wavelet time–frequency images. Second, for the data dimensionality reduction and model simplification, the time–frequency wavelet images are processed in grayscale to generate single-channel grayscale time–frequency images. Then, sample augmentation is performed on grayscale time–frequency images to balance the dataset by using a variational autoencoder generation adversarial network. Finally, the generated images and the original images are fused and trained by using a focus-loss-optimized CNN classifier to achieve fault diagnosis under unbalanced conditions. The experimental results show that the VGAIC-FDM effectively captures the potential spatial distribution of real samples and alleviates the impact caused by the inconsistent difficulty of sample classification. As a result, it enhances the fault diagnosis performance of the model when dealing with unbalanced datasets, leading to higher accuracy and F1-score values.

An adaptive few-shot fault diagnosis method based on virtual samples generated by fault characteristics of rotating machines

The existing intelligent diagnostic methods based on the machine learning achieve good diagnostic results under the condition of large amount of the failure data available. However, in most cases, the diagnosis model is difficult to be constructed under the few-shot problem, which only normal data of the equipment is available. Therefore, a novel Adaptive Diagnosis with Fault Characteristics (ADFC) method, which builds the diagnosis model by generating personalized virtual fault samples, is proposed in the paper. The fault common characteristics are obtained based on the failure mechanism and the law of transmission paths, and the personalized fault virtual samples are generated by combining health state data contain the individual characteristics of the equipment. Then, the frequency domain features reflecting the operating status of the equipment are extracted as training samples. Finally, the fault diagnosis model based on the Convolutional Neural Network (CNN) is constructed for the equipment. The ADFC method is validated by the rotating machinery data, including the public data, the laboratory data and the field application data. The results indicate that the ADFC method achieved an average diagnostic accuracy of 92.16%, and the accuracy has been improved by at least 0.78% compared to the comparison method.

Dental cavity analysis, prediction, localization, and quantification using computer vision

Dental health assessment is a critical component of overall well-being, and advancements in computer vision and deep learning have opened new avenues for automating and enhancing this process. In this study, we present a comprehensive approach to dental cavity analysis, spanning localization, quantification, and visualization. Our methodology leveraged a diverse dataset of colored dental images that had been meticulously augmented and annotated. The You Only Look Once model was employed for precise dental cavity localization, providing bounding box predictions. Remarkably, these results were obtained based on images from standard device cameras. Subsequently, we introduced the use of the segment anything model segmentation model, known for its zero-shot generalization capabilities, to focus on the exact areas of dental cavities. This approach enhanced the granularity of our analysis, providing dental professionals with detailed visualizations for precise diagnosis. During the quantification phase, we extracted cavity areas from bounding box coordinates, enabling accurate measurement of cavity sizes. The model achieved a notable mean average precision of 0.732, an accuracy of 0.789, and a recall of 0.701. Moreover, the model converged quickly, with most metrics achieving near-optimal results after 100 iterations. This quantitative data augments traditional diagnosis methods, facilitating more informed treatment decisions.

Digital twin-assisted intelligent fault diagnosis for bearings

Abstract Data-driven intelligent fault diagnosis methods generally require a large amount of labeled data and considerable time to train network models. However, obtaining sufficient labeled data in practical industrial scenarios has always been a challenge, which hinders the practical application of data-driven methods. A digital twin (DT) model of rolling bearings can generate labeled training dataset for various bearing faults, supplementing the limited measured data. This paper proposes a novel DT-assisted approach to address the issue of limited measured data for bearing fault diagnosis. First, a dynamic model of bearing with damages is introduced to generate simulated bearing acceleration vibration signals. A DT model is constructed in Simulink, where the model parameters are updated based on the actual system behavior. Second, the structural parameters of the DT model are adaptively updated using least squares method with the measured data. Third, a Vision Transformer (ViT) -based network, integrated with short-time Fourier transform, is developed to achieve accurate fault diagnosis. By applying short-time Fourier transform at the input end of the ViT network, the model effectively extracts additional information from the vibration signals. Pre-training the network with an extensive dataset from miscellaneous tasks enables the acquisition of pre-trained weights, which are subsequently transferred to the bearing fault diagnosis task. Experiments results verify that the proposed approach can achieve higher diagnostic accuracy and better stability.

Fault Diagnosis of Centrifugal fan Bearings Based on I-CNN and JMMD in the Context of Sample Imbalance

The fault diagnosis is an effective technical means to improve the reliability of centrifugal fan bearings. The serious imbalance of data is one of the important issues facing bearing fault diagnosis.In this paper, a transfer learning-based fault diagnosis method for Centrifugal fan bearings is proposed, utilizing the improved CNN (I-CNN) and Joint Maximum Mean Discrepancy (JMMD) algorithms. The raw vibration signals of the bearings are enhanced through fast Fourier transform for feature representation. The signals are then processed by parallel multi-scale CNNs with an embedded Squeeze-and-Excitation (SE) attention to focus on key features. Furthermore, the JMMD is introduced as a metric for quantifying the disparity between the source and target domains, thereby mitigating domain shift. In the loss function, weight factors and scaling factors are introduced to increase attention on minority samples and easily confused samples within the imbalanced dataset. The proposed method is validated on the Centrifugal fan bearing dataset from Jiangnan University and the CWRU dataset.

Audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional network in strong noise environments

Abstract Fault diagnosis of belt conveyors is crucial for coal mine production, but audio-based fault diagnosis in underground coal mines remains challenging due to the strong noise environment. To address this problem, a method for audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional networks in a strong noise environment is proposed. Firstly, the improved beluga whale optimization is designed by introducing the non-linear balance factor and non-linear probability and combining them with the proposed cyclical shock factor to optimize the variational modal decomposition parameters to achieve noise reduction and signal reconstruction. Secondly, an improved adaptive noise reduction convolutional network is developed using an adaptive threshold activation function and an improved loss function to enhance noise robustness and fault diagnosis accuracy. Finally, the proposed method's effectiveness is evaluated in low and strong noise environments, with experimental results demonstrating superior fault diagnosis performance. In low noise environments, the fault diagnosis accuracy is 98.61%, and in strong noise environments, it is 98.96%, outperforming existing fault diagnosis methods.