Fault diagnosis method of link control system for gravitational wave detection

To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.

Early diagnosis of esophageal cancer:How to put“early detection”into effect?

This editorial comments on the article by Qu et al in a recent edition of World Journal of Gastrointestinal Oncology,focusing on the importance of early diagnosis in managing esophageal cancer and strategies for achieving“early detection”.The five-year age-standardized net survival for esophageal cancer patients falls short of expectations.Early detection and accurate diagnosis are critical strategies for improving the treatment outcomes of esophageal cancer.While advancements in endoscopic technology have been significant,there seems to be an excessive emphasis on the latest high-end endoscopic devices and various endoscopic resection techniques.Therefore,it is imperative to redirect focus towards proactive early detection strategies for esophageal cancer,investigate the most cost-effective screening methods suitable for different regions,and persistently explore practical solutions to improve the five-year survival rate of patients with esophageal cancer.

Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

Fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach

For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

Combined detection tumor markers for diagnosis and prognosis of gallbladder cancer

AIM: To clarify the value of combined use of markers for the diagnosis of gallbladder cancer and prediction of its prognosis. METHODS: Serum cancer antigens (CA) 199, CA242, carcinoembryonic antigen (CEA), and CA125 levels were measured in 78 patients with gallbladder cancer (GBC), 78 patients with benign gallbladder diseases, and 78 healthy controls using electrochemiluminescence. CA199, CA242, CEA, and CA125 levels and positive rates were analyzed and evaluated pre-and post-operatively. Receiver operator characteristic curves were used to determine diagnostic sensitivity and specificity of GBC. Survival time analysis, including survival curves, and multivariate survival analysis of a Cox proportional hazards model was performed to evaluate independent prognostic factors. RESULTS: Serum CA242, CA125, and CA199 levels in the GBC group were significantly higher when compared with those in the benign gallbladder disease and healthy control groups (P < 0.01). With a single tumor marker for GBC diagnosis, the sensitivity of CA199 was the highest (71.7%), with the highest specificity being in CA242 (98.7%). Diagnostic accuracy was highest with a combination of CA199, CA242, and CA125 (69.2%). CA242 could be regarded as a tumor marker of GBC infiltration in the early stage. The sensitivity of CA199 and CA242 increased with progression of GBC and advanced lymph node metastasis (P < 0.05). The 78 GBC patients were followed up for 6-12 mo (mean: 8 mo), during which time serum CA199, CA125, and CA242 levels in the recurrence group were significantly higher than in patients without recurrence (P < 0.01). The post-operative serum CA199, CA125, and CA242 levels in the non- recurrence group were significantly lower than those in the GBC group (P < 0.01). Multivariate survival analysis using a Cox proportional hazards model showed that cancer of the gallbladder neck and CA199 expression level were independent prognostic factors. CONCLUSION: CA242 is a marker of GBC infiltration in the early stage. CA199 and cancer of the gallbladder neck are therapeutic and prognostic markers. (C) 2014 Baishideng Publishing Group Co., Limited. All rights reserved.

Molecularly specific detection of bacterial lipoteichoic acid for diagnosis of prosthetic joint infection of the bone

Discriminating sterile inflammation from infection, especially in cases of aseptic loosening versus an actual prosthetic joint infection, is challenging and has significant treatment implications. Our goal was to evaluate a novel human monoclonal antibody（mAb） probe directed against the Gram-positive bacterial surface molecule lipoteichoic acid（LTA）. Specificity and affinity were assessed in vitro. We then radiolabeled the anti-LTA mAb and evaluated its effectiveness as a diagnostic imaging tool for detecting infection via immuno PET imaging in an in vivo mouse model of prosthetic joint infection（PJI）. In vitro and ex vivo binding of the anti-LTA mAb to pathogenic bacteria was measured with Octet, ELISA, and flow cytometry. The in vivo PJI mouse model was assessed using traditional imaging modalities, including positron emission tomography（PET） with [^（18）F]FDG and [^（18）F]Na F as well as X-ray computed tomography（CT）, before being evaluated with the zirconium-89-labeled antibody specific for LTA（[^（89）Zr]SAC55）.The anti-LTA mAb exhibited specific binding in vitro to LTA-expressing bacteria. Results from imaging showed that our model could reliably simulate infection at the surgical site by bioluminescent imaging, conventional PET tracer imaging, and bone morphological changes by CT. One day following injection of both the radiolabeled anti-LTA and isotype control antibodies, the anti-LTA antibody demonstrated significantly greater（P 〈 0.05） uptake at S. aureus-infected prosthesis sites over either the same antibody at sterile prosthesis sites or of control non-specific antibody at infected prosthesis sites. Taken together, the radiolabeled anti-LTA mAb, [^（89）Zr]SAC55, may serve as a valuable diagnostic molecular imaging probe to help distinguish between sterile inflammation and infection in the setting of PJI. Future studies are needed to determine whether these findings will translate to human PJI.

Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis(FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder,a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.

Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN

Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’health.Under the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly demanded.Recent studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy rates.With the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various fields.However,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification processes.In this study,we propose to perform chiller FDD using a CNN-based approach.The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods.First,the CNN-based approach does not require the feature selection/extraction process.Since CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional approaches.Second,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.

Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks

In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.

A Reinforcement Learning System for Fault Detection and Diagnosis in Mechatronic Systems

With the increasing demand for the automation of operations and processes in mechatronic systems,fault detection and diagnosis has become a major topic to guarantee the process performance.There exist numerous studies on the topic of applying artificial intelligence methods for fault detection and diagnosis.However,much of the focus has been given on the detection of faults.In terms of the diagnosis of faults,on one hand,assumptions are required,which restricts the diagnosis range.On the other hand,different faults with similar symptoms cannot be distinguished,especially when the model is not trained by plenty of data.In this work,we proposed a reinforcement learning system for fault detection and diagnosis.No assumption is required.Feature exaction is first made.Then with the features as the states of the environment,the agent directly interacts with the environment.Optimal policy,which determines the exact category,size and location of the fault,is obtained by updating Q values.The method takes advantage of expert knowledge.When the features are unclear,action will be made to get more information from the new state for further determination.We create recurrent neural network with the long short-term memory architecture to approximate Q values.The application on a motor is discussed.The experimental results validate that the proposed method demonstrates a significant improvement compared with existing state-of-the-art methods of fault detection and diagnosis.

Novel Approach for Fault Detection and Diagnosis of Sensors in Air Handling Units Using Wavelet Energy Entropy

A novel fault diagnosis method for sensors in air handling unit（AHU） using wavelet energy entropy was presented. Instead of directly comparing the numerous data under noise conditiom, the wavelet energy entropy residual was compared in the proposed method. Three.level wavelet analysis was used to decompose the measurement data under both fault-free and faulty operation conditions. The concept of Shannon entropy was referred to define wavelet energy entropy of the wavelet coefficients. The sensor faults were diagnosed by comparing the deviation of the wavelet energy entropy of the measured signal and the estimated one with the preset threshold. Testing results showed that the wavelet energy entropy was sensitive to diagnose the biased faults. The wavelet energy entropy residuals exceed the threshold significantly when faults occur. In addition, the severer the faults were, the larger the residuals would be. The results prove that the proposed method is feasible and effective for the fault detection and diagnosis of the sensors.

An Ensemble Application of Conflict-Resolving ART-Based Neural Networks to Fault Detection and Diagnosis

Accurate fault detection and diagnosis is important for secure and profitable operation of modern power systems.In this paper,an ensemble of conflict-resolving Fuzzy ARTMAP classifiers,known as Probabilistic Multiple Fuzzy ARTMAP with Dynamic Decay Adjustment(PMFAMDDA),for accurate discrimination between normal and faulty operating conditions of a Circulating Water(CW)system in a power generation plant is proposed.The decisions of PMFAMDDA are reached through a probabilistic plurality voting strategy that is in agreement with the Bayesian theorem.The results of the proposed PMFAMDDA model are compared with those from an ensemble of Probabilistic Multiple Fuzzy ARTMAP(PMFAM)classifiers.The outcomes reveal that PMFAMDDA,in general,outperforms PMFAM in discriminating operating conditions of the CW system.

Robust fault detection and diagnosis for uncertain nonlinear systems

This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.

INVESTIGATION ON FAULT DETECTION & DIAGNOSIS FOR POSITION SERVO SYSTEM OF AIRCRAFT ACTUATOR

A new approach to fault dignosis dealing with nonlinear system Hopfieldneural networks (HNN) is presented. The model parameters of the nonlinear systemtreated as functions of measured operating points and faults are estimated by HNN. Boththe nominal model of the healthy system and HNN training models corresponding to everyoperating point are recognized. In addition, the anticipated fault models corresponding toevery kind of fault and every operating point are obtaind in advance. The real systemmodel parameters of the system estimated by generalization process of HNN are matchedwith the nominal models of the healthy system and anticipated fault models. Consequent-ly, the final result of fault detection and diagnosis is acquired. The approach to fault diag-nosis is used in an aircraft actuating poisition servo system and the simulation resu1t is re-ported.

Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine

Artificial intelligence(AI)refers to the simulation of human intelligence in machines programmed to convert raw input data into decision-making actions,like humans.AI programs are designed to make decisions,often using deep learning and computer-guided programs that analyze and process raw data into clinical decision making for effective treatment.New techniques for predicting cancer at an early stage are needed as conventional methods have poor accuracy and are not applicable to personalized medicine.AI has the potential to use smart,intelligent computer systems for image interpretation and early diagnosis of cancer.AI has been changing almost all the areas of the medical field by integrating with new emerging technologies.AI has revolutionized the entire health care system through innovative digital diagnostics with greater precision and accuracy.AI is capable of detecting cancer at an early stage with accurate diagnosis and improved survival outcomes.AI is an innovative technology of the future that can be used for early prediction,diagnosis and treatment of cancer.

Detection and Diagnosis of Gear Fault By the Single Gear Tooth Analysis Technique

This paper presents a procedure of sing le gear tooth analysis for early detection and diagnosis of gear faults. The objec tive of this procedure is to develop a method for more sensitive detection of th e incipient faults and locating the faults in the gear. The main idea of the sin gle gear tooth analysis is that the vibration signals collected with a high samp ling rate are divided into a number of segments with the same time interval. The number of signal segments is equal to that of the gear teeth. The analysis of i ndividual segments reveals more sensitively the changes of the vibration signals in both time and frequency domain caused by gear faults. In addition, the locat ion of a failed tooth can be indicated in terms of the position of the segment t hat deviates from the normal segments. An experimental investigation verified th e advantages of the single gear tooth analysis.

Fault Detection and Isolation in Industrial Systems Based on Spectral Analysis Diagnosis

The diagnoses in industrial systems represent an important economic objective in process industrial automation area. To guarantee the safety and the continuity in production exploitation and to record the useful events with the feedback experience for the curative maintenance. We propose in this work to examine and illustrate the application ability of the spectral analysis approach, in the area of fault detection and isolation industrial systems. In this work, we use a combined analysis diagram of time-frequency, in order to make this approach exploitable in the proposed supervision strategy with decision making module. The obtained results, show clearly how to guarantee a reliable and sure exploitation in industrial system, thus allowing better performances at the time of its exploitation on the supervision strategy.

Application of Otopoint Detection to Qualitative Diagnosis of Tumour

ＡｐｐｌｉｃａｔｉｏｎｏｆＯｔｏｐｏｉｎｔＤｅｔｅｃｔｉｏｎｔｏＱｕａｌｉｔａｔｉｖｅＤｉａｇｎｏｓｉｓｏｆＴｕｍｏｕｒ￥ＳｈｉＹｕｌｉｎｇ；ＬｉｕＸｕｎ；ＺｈａｏＴｉｅ；ＹａｎＸｉａｎｋａｉ（ＬａｂｏｒａｔｏｒｙｏｆＷｕｈａｎＧｅｎｅｒａｌＨｏｓｐ...

DETECTION OF CANCER-ASSOCIATED ANTIGEN IN FECES USING MONOCLONAL ANTIBODIES IN THE DIAGNOSIS OF COLON CARCINOMA

Monoclonal antibodies against colon and pancreatic cancer, CL-2, CL-3, PS-9, PS-10, were used to detect the associated antigens in feces of patients with gastrointestinal carcinoma and non-cancer diseases. Binding inhibition test by SABC-ELISA method were performed for the measurement of the antigen level. Results showed that the associated antigen detected in feces of patients with colon cancer were significantly higher than that of non-cancer disease or normal subjects. The positive rates were 61.1% as detected with CL-2; 53.4% with CL-3; 55.0%, PS-9; and 53.3% PS-10 in cancer patients while that in normal subjects were 7%; 9%; 8%; and 8% respectively. When 'cocktail' of CL-2, PS-9 and PS-10 were used, the positive rates were 92.5% in colon cancer and 14% in normal subjects. In seven out of the sixty patients with colon cancer studied who were graded as Dukes A, the results were all positive. The results seem superior to the serologic detection and may provide a promising new approach in the early diagnosis of colon cancer.

APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS

Based on radial basis function (RBF) neural networks, the healthy working model of each sub system of robot in FMS is established. A new approach to fault on line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.