Intelligent Diagnosis Technology Research Articles

Multimodal separation and cross fusion network based on Raman spectroscopy and FTIR spectroscopy for diagnosis of thyroid malignant tumor metastasis

The diagnosis of cervical lymph node metastasis from thyroid cancer is an essential stage in the progression of thyroid cancer. The metastasis of cervical lymph nodes directly affects the prognosis and survival rate of patients. Therefore, timely and early diagnosis is crucial for effective treatment and can significantly improve patients’ survival rate and quality of life. Traditional diagnostic methods, such as ultrasonography and radionuclide scanning, have limitations, such as complex operations and high missed diagnosis rates. Raman spectroscopy and FTIR spectroscopy can well reflect the molecular information of samples, have characteristics such as sensitivity and specificity, and are simple to operate. They have been widely used in clinical research in recent years. With the development of intelligent medical diagnosis technology, medical data shows a multi-modal trend. Compared with single-modal data, multi-modal data fusion can achieve complementary information, provide more comprehensive and valuable diagnostic information, significantly enhance the richness of data features, and improve the modeling effect of the model, helping to achieve better results. Accurate disease diagnosis. Existing research mostly uses cascade processing, ignoring the important correlations between multi-modal data, and at the same time not making full use of the intra-modal relationships that are also beneficial to prediction. We developed a new multi-modal separation cross-fusion network (MSCNet) based on deep learning technology. This network fully captures the complementary information between and within modalities through the feature separation module and feature cross-fusion module and effectively integrates Raman spectrum and FTIR spectrum data to diagnose thyroid cancer cervical lymph node metastasis accurately. The test results on the serum vibrational spectrum data set of 99 cases of cervical lymph node metastasis showed that the accuracy and AUC of a single Raman spectrum reached 63.63% and 63.78% respectively, and the accuracy and AUC of a single FTIR spectrum reached 95.84% respectively and 96%. The accuracy and AUC of Raman spectroscopy combined with FTIR spectroscopy reached 97.95% and 98% respectively, which is better than existing diagnostic technology. The omics correlation verification obtained correlation pairs of 5 Raman frequency shifts and 84 infrared spectral bands. This study provides new ideas and methods for the early diagnosis of cervical lymph node metastasis of thyroid cancer.

Fault diagnosis method and experimental research of reciprocating seal based on CFD-GAN-AE

Abstract Hydraulic impactors are crucial for oil and gas exploration, but seal failure is a common issue, having an effective technique for diagnosing sealing faults can provide dependable operational and maintenance assistance for Hydraulic impactors. However, identifying wear failures is challenging and there is limited data available, there has been significant interest in intelligent defect diagnosis technology that is based on deep learning in recent years. Therefore, we propose a method to enhance the data and identify faults through deep learning. Initially, the CFD method was used to simulate seal leakage and determine whether factors such as pressure can indicate varying levels of leaking in the seal, this approach provides a theoretical foundation for signal gathering experiments. Next, the EMD approach is used to separate the non-smooth pressure signal from the seal experiment, revealing fault features that indicate the extent of leakage. Finally, the improved GAN method is suggested to balance imbalanced samples by utilizing the sample overlap rate, it is paired with the AE algorithm to categorize different levels of leakage. Furthermore, a comparative analysis is conducted between the proposed methodology and several classical fault diagnosis methods. This work investigates seal damage through the lens of computational fluid dynamics and the fault identification of uneven seal samples is accomplished.

Research on transformer fault intelligent diagnosis technology based on improved random forest algorithm

Transformer oil can dissolve a certain amount of gas, which can provide an important basis for transformer fault diagnosis. The relationship between the characteristic amount of dissolved gas in transformers and the type of transformer fault was analyzed, and the main influencing factors were extracted. We studied a transformer fault diagnosis method based on the improved random forest algorithm and combined it with the identification requirements of abnormal data in multiple parts of transformers. We improved the random forest from four aspects: dataset construction, bootstrap sampling, decision tree generation, and multi-node voting. We established an improved random forest diagnosis model with oil chromatogram feature gas ratio as the characteristic parameter and analyzed different input parameter combinations. The effectiveness of improving the random forest diagnosis model under different training times was compared with various machine learning methods. The experimental results show that the diagnostic accuracy of the improved random forest diagnostic model is significantly improved compared to other machine learning methods.

Construction and Clinical Application of Digital Intelligent Diagnosis and Treatment System for Hepatocellular Carcinoma

Abstract: In the past 20 years, with the emergence and update of digital intelligent technology, the diagnosis and treatment of hepatocellular carcinoma (HCC) have undergone profound changes. Three-dimensional visualization technology has revolutionized the traditional two-dimensional diagnosis and treatment model of HCC and realized preoperative visualization of tumors and complex liver anatomy. The emergence of ICG fluorescence imaging has realized intraoperative tumor boundary visualization from the molecular and cellular levels. Augmented reality (AR) and mixed reality (MR) technology can realize the three-dimensional visualization of anatomical structures in surgical navigation. Traditional experiential surgery has been transformed into modern intelligent navigation surgery, and surgery has stepped into a new era of digital intelligent technology. In addition, artificial intelligence, molecular imaging and nanoprobes are also expected to achieve early diagnosis and treatment of HCC and improve the prognosis of patients. This article reviews the latest application of digital intelligent diagnosis and treatment technology related to diagnosing and treating HCC, hoping to help achieve accurate diagnosis and treatment of HCC.

Cutting Weights of Deep Learning Models for Heart Sound Classification: Introducing a Knowledge Distillation Approach.

Cardiovascular diseases (CVDs) are the number one cause of death worldwide. In recent years, intelligent auxiliary diagnosis of CVDs based on computer audition has become a popular research field, and intelligent diagnosis technology is increasingly mature. Neural networks used to monitor CVDs are becoming more complex, requiring more computing power and memory, and are difficult to deploy in wearable devices. This paper proposes a lightweight model for classifying heart sounds based on knowledge distillation, which can be deployed in wearable devices to monitor the heart sounds of wearers. The network model is designed based on Convolutional Neural Networks (CNNs). Model performance is evaluated by extracting Mel Frequency Cepstral Coefficients (MFCCs) features from the PhysioNet/CinC Challenge 2016 dataset. The experimental results show that knowledge distillation can improve a lightweight network's accuracy, and our model performs well on the test set. Especially, when the knowledge distillation temperature is 7 and the weight α is 0.1, the accuracy is 88.5 %, the recall is 83.8 %, and the specificity is 93.6 %.Clinical relevance- A lightweight model of heart sound classification based on knowledge distillation can be deployed on various hardware devices for timely monitoring and feedback of the physical condition of patients with CVDs for timely provision of medical advice. When the model is deployed on the medical instruments of the hospital, the condition of severe and hospitalised patients can be timely fed back and clinical treatment advice can be provided to the clinicians.

WSAFormer-DFFN: A model for rotating machinery fault diagnosis using 1D window-based multi-head self-attention and deep feature fusion network

Fault diagnosis is of great importance for rotating machinery maintenance. Deep learning is an intelligent diagnosis technology that attracts more attention at present. The ability to learn fault features of pure CNN and RNN models is limited due to inherent structural defects. Therefore, we build a fault diagnosis model (WSAFormer-DFFN) combining CNN and self-attention structure to enhance learning ability. Meanwhile, propose 1D window-based multi-head self-attention (1D W-MSA) for vibration signal and its representation to introduce local inductive bias, which improves the robustness of the model. A series of experiments on three rotating machinery datasets show that the proposed model has better recognition accuracy and less performance attenuation in interference conditions. On the bearing dataset of Case Western Reserve University, the accuracy rate increases by 21.29%–37.12% compared with several classic models when the signal-to-noise ratio is −6 dB.

Research on bearing vibration signal generation method based on filtering WGAN_GP with small samples

In the practical application of bearing fault diagnosis, the data imbalance problems caused by the lack of available fault data lead to inaccurate diagnosis. The high cost and difficulty of obtaining fault samples has become an obstacle to the development of intelligent diagnosis technology. Aiming at the problem of data imbalance caused by small samples, this paper proposes a data generation method called FEF_WGAN_GP based on Wasserstein generative adversarial networks with gradient penalty (WGAN_GP) and feature Euclidean distance filtering (FEF) theory. Firstly, WGAN_GP is used to obtain signals with similar distribution to the small sample data, which can alleviate the imbalance of the dataset. Then, the FEF method is used to filter the generated data in order to obtain a higher quality of the samples. In the test validation part, not only the used dataset is evaluated to obtain a more reasonable dataset, but also the generated signals are evaluated from multiple perspectives. In addition, this paper evaluates the effects of the number, length and signal-to-noise ratio of the parent data on the quality of the generated signals, as well as the effect of the setting of the threshold of the data filtering method on the accuracy of the classifier. The experimental results indicate that this method performs well in processing unbalance fault data. It has better stability and diagnostic accuracy than the current stable method.

The Application of Interdisciplinary In Airborne Electromechanical System and Its Enlightenment to the Cultivation of Graduate Students’ Innovative Ability

With the progress of science and technology, intelligent monitoring and diagnosis system has developed rapidly. Intelligent diagnosis technology, as an engineering application of artificial intelligence, has developed rapidly both at home and abroad in recent years. Research shows that intelligent diagnosis technology is a comprehensive industry integrating multiple technologies and interdisciplinary disciplines, and it is also a partial epitome of contemporary scientific and technological progress. Combined with the development of intelligent diagnosis technology of airborne electromechanical system and the important task of colleges and universities as the undertaker of high-end talent training, this paper puts forward that the current talent training mode needs to be adjusted according to the needs of science and technology, and teaching practice reform should be carried out from professional fields, discipline categories, practical training platforms and other aspects, so as to provide reserve talents for China's scientific and technological progress.

Intelligent quantification of natural gas pipeline defects using improved sparrow search algorithm and deep extreme learning machine

Accurate damage degree prediction is important to ensure the safety, stability, and economic operation of the natural gas pipeline in service, especially those in long-term service. In order to maintain the efficiency and safety of natural gas pipeline transportation, the intelligent diagnosis and evaluation technology of pipeline is very important. In this paper, The opposition-based learning strategy and adaptive T-distribution mutation operator are introduced to optimize the sparrow search algorithm (SSA),improve the search ability,convergence speed and accuracy of SSA algorithm. 12 classical benchmark functions are used to evaluate the performance of the improved algorithm. Experimental results demonstrate the feasibility and validity of ISSA compared with the original Sparrow search algorithm. Based on its excellent performance, ISSA is used to optimize the input layer weight and bias parameters of deep extreme learning machine (DELM). Therefore, a comprehensive ISSA-DELM network model is constructed for intelligent quantitative evaluation of natural gas pipeline defects. The results show that the model can effectively overcome the problem that the DELM effect is affected by the random input weight and random bias of each ELM-AE, and improve the quantitative prediction performance. This will facilitate the assessment of the integrity and safety status of natural gas pipelines.

Review on Intelligent Diagnosis Technology of Electronically Controlled Fuel Injection System of ME Diesel Engine

ME diesel engine plays an important role in realizing energy conservation and environmental protection and improving the intelligent level of ship engine room. Because of its high technical content and high added value, using intelligent diagnosis technology to ensure safe and reliable work is an important means to realize intelligent engine room. Taking the ship electronic fuel injection system as an example, this paper introduces its working principle and common faults, analyzes and summarizes the research status of intelligent diagnosis technology at home and abroad in four aspects: fault mechanism, data measurement and feature extraction, fault mode classification and residual life prediction. Then, it analyzes the problems and future development trend of intelligent diagnosis technology, and points out that developing a health management system integrating weak fault signal extraction, multi-source data analysis, quantitative judgment of fault mode, remaining life prediction and maintenance suggestions is an important development goal in the future.

Ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion

PurposeThis paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters.Design/methodology/approachTo express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model.FindingsThe experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications.Originality/valueThis method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.

Application Analysis of Artificial Intelligent Neural Network Based on Intelligent Diagnosis

In recent years, the continuous development of computer science and AI technology makes the application prospect of artificial intelligence in fault diagnosis emerge. As a simulation technology of human thinking pattern, intelligent diagnosis technology can check and manage the monitoring target in real time to ensure the accuracy of data information. This paper introduces the basic principles of key artificial intelligence technologies in the field of sports, such as convolutional neural network, object detection, object tracking and action recognition. Then it analyzes the application status of intelligent diagnosis technology and artificial intelligence network under intelligent diagnosis, and puts forward the application of artificial intelligence neural network in automobile fault diagnosis based on examples. In the construction of the neural network system, the real-time collection of vehicle operation data can be analyzed, once the fault is found, the driver can be notified in time to avoid safety accidents. The author summarizes the existing research results on the application of artificial intelligence algorithm in intelligent diagnosis, in order to provide help for the subsequent research.

Application of Equipment Intelligent Diagnosis Technology System in Smart Power Plant

Application of Equipment Intelligent Diagnosis Technology System in Smart Power Plant

Application of computer tongue image analysis technology in the diagnosis of NAFLD

Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882–0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.

Tongue image quality assessment based on a deep convolutional neural network

BackgroundTongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM.MethodsMachine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score.ResultsThe experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA.ConclusionsOur research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible.

Intelligent Diagnosis Technology of Wind Turbine Drive System based on Neural Network

The seriousness of air pollution appears to be the importance of wind energy as a non-polluting energy source. Today, the use of wind power has become a trend for new countries to develop new energy sources. Wind turbines are the key equipment for converting wind energy into electrical energy, the quality of the state directly affects the efficiency of wind power generation. Therefore, how to effectively diagnose the wind turbine drive system is the guarantee of wind power generation. This paper establishes a fault diagnosis method for wind turbine drive based on vibration characteristics, by wavelet packet decomposition of vibration signals. The feature extraction is carried out and back propagation neural network is used for classification research. Finally, the simulation results show that the recognition rate is over 90%, which verify effectiveness of the proposed method.

Research Progress in Intelligent and Precise Optical Diagnosis and Treatment Technology

Research Progress in Intelligent and Precise Optical Diagnosis and Treatment Technology

Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial‐aware booster

With the development of artificial intelligence and image processing technology, more and more intelligent diagnosis technologies are used in cervical cancer screening. Among them, the detection of cervical lesions by thin liquid-based cytology is the most common method for cervical cancer screening. At present, most cervical cancer detection algorithms use the object detection technology of natural images, and often only minor modifications are made while ignoring the specificity of the complex application scenario of cervical lesions detection in cervical smear images. In this study, the authors combine the domain knowledge of cervical cancer detection and the characteristics of pathological cells to design a network and propose a booster for cervical cancer detection (CCDB). The booster mainly consists of two components: the refinement module and the spatial-aware module. The characteristics of cancer cells are fully considered in the booster, and the booster is light and transplantable. As far as the authors know, they are the first to design a CCDB according to the characteristics of cervical cancer cells. Compared with baseline (Retinanet), the sensitivity at four false positives per image and average precision of the proposed method are improved by 2.79 and 7.2%, respectively.

Intelligent diagnosis of natural gas pipeline defects using improved flower pollination algorithm and artificial neural network

With the increasing service life of pipelines, natural gas pipelines can gradually age and produce various corrosion defects. Hence, in order to ensure the efficiency and safety of pipeline transportation in the peak period of natural gas consumption, the intelligent diagnosis technology of the pipelines is of vital importance. In this paper, iterative chaotic map with infinite collapses (ICMIC) and comprehensive opposition (CO) learning strategy are used to optimize flower pollination algorithm (FPA) to enhance search abilities of original FPA algorithm. Among them, the ICMIC enhances the diversity of population, and the local CO learning strategy enhances its exploitation ability. Fifteen classical benchmark functions are used to test the optimization performance of the improved flower pollination algorithm (IFPA). Considering the category, complexity and application of test functions, a more reasonable evaluation formula is proposed. The test shows that the performance of IFPA algorithm is obviously better than other classical intelligent algorithms. Based on the excellent performance of IFPA, the IFPA algorithm is used to optimize the initial weights and thresholds of back propagation (BP) neural network. Therefore, a comprehensive IFPA-BP network model is constructed for the intelligent diagnosis of natural gas pipeline defects. The results show that the proposed model can effectively overcome the problem that BP neural network is prone to fall into local optimal value, and it can accurately identify the pipelines defects. This will facilitate intelligent diagnosis of natural gas pipelines defects.

Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels

In recent years, the intelligent diagnosis technology of wind turbines has made great progress. However, in practical engineering applications, the operating states of wind turbine are various, accompanied by a large number of noise interference, which leads to the decline of the discrimination accuracy of intelligent diagnosis. In order to solve this problem, inspired by the Google team Inception model, this paper proposes a concurrent convolution neural network (C–CNN), the raw data is fed into the network without any prior knowledge, and the characteristics are learned directly and adaptively from the input. Even if the data is accompanied by noise, the model still has high accuracy and strong generalization ability. The model is composed of a CNN with multiple branches. Meanwhile, the convolutional layer of different branches selects the kernels with different scales in same level, thus improving the learning ability of entire network. In this paper, the feasibility of this method for fault diagnosis of bearings in wind turbines is demonstrated by three bearing datasets. The results show that the proposed method can extract discriminative features and classify bearing data accurately under the disturbance of different rotating speed, different load and random noise.