Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks

The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intelligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.

Molecular diagnosis and direct quantification of cereal cyst nematode(Heterodera filipjevi) from field soil using TaqMan real-time PCR

Heterodera filipjevi continues to be a major threat to wheat production worldwide.Rapid detection and quantification of cyst nematodes are essential for more effective control against this nematode disease.In the present study,a TaqManminor groove binder(TaqMan-MGB)probe-based fluorescence quantitative real-time PCR(qPCR)was successfully developed and used for quantifying H.filipjevi from DNA extracts of soil.The primers and probe designed from the obtained RAPD-SCAR marker fragments of H.filipjevi showed high specificity to H.filipjevi using DNA from isolatesconfirmed species of 23 Heterodera spp.,1 Globodera spp.and 3 Pratylenchus spp.The qPCR assay is highly sensitive and provides improved H.filipjevi detection sensitivity of as low as 4^(-3) single second-stage juvenile(J2)DNAs,10^(-3) female DNAs,and 0.01μgμL^(-1) genomic DNAs.A standard curve relating to the threshold cycle and log values of nematode numbers was generated and validated from artificially infested soils and was used to quantify H.filipjevi in naturally infested field soils.There was a high correlation between the H.filipjevi numbers estimated from 32 naturally infested field soils by both conventional methods and the numbers quantified using the qPCR assay.qPCR potentially provides a useful platform for the efficient detection and quantification of H.filipjevi directly from field soils and to quantify this species directly from DNA extracts of field soils.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

Comparison of dynamic Bayesian network approaches for online diagnosis of aircraft system

The online diagnosis for aircraft system has always been a difficult problem. This is due to time evolution of system change, uncertainty of sensor measurements, and real-time requirement of diagnostic inference. To address this problem, two dynamic Bayesian network(DBN) approaches are proposed. One approach prunes the DBN of system, and then uses particle filter(PF) for this pruned DBN(PDBN) to perform online diagnosis. The problem is that estimates from a PF tend to have high variance for small sample sets. Using large sample sets is computationally expensive. The other approach compiles the PDBN into a dynamic arithmetic circuit(DAC) using an offline procedure that is applied only once, and then uses this circuit to provide online diagnosis recursively. This approach leads to the most computational consumption in the offline procedure. The experimental results show that the DAC, compared with the PF for PDBN, not only provides more reliable online diagnosis, but also offers much faster inference.

Restoring force correction based on online discrete tangent stiffness estimation method for real-time hybrid simulation

In real-time hybrid simulation（RTHS）, it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation（online DTSE） to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that：（1） the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and（2） the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.

Direct immune-SCIR public-opinion propagation model based on real-time online users

Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

A Non-Contact Original-State Online Real-Time Monitoring Method for Complex Liquids in Industrial Processes

Failures are very common during the online real-time monitoring of large quantities of complex liquids in industrial processes, and can result in excessive resource consumption and pollution. In this study, we introduce a monitoring method capable of non-contact original-state online real-time monitoring for strongly coated, high-salinity, and multi-component liquids. The principle of the method is to establish the relationship among the concentration of the target substance in the liquid （C）, the color space coor- dinates of the target substance at different concentrations （L＊, a＊, b＊）, and the maximum absorption wave- length （λmax）; subsequently, the optimum wavelength λT of the liquid is determined by a high-precision scanning-type monitoring system that is used to detect the instantaneous concentration of the target substance in the flowing liquid. Unlike traditional monitoring methods and existing online monitoring methods, the proposed method does not require any pretreatment of the samples （i.e., filtration, dilution, oxidation/reduction, addition of chromogenic agent, constant volume, etc.）, and it is capable of original- state online real-time monitoring. This method is employed at a large electrolytic manganese plant to monitor the Fe3. concentration in the colloidal process of the plant＇s aging liquid （where the concentra- tions of Fe3＋, Mn2＋, and （NH4）2SO4 are 0.5-18 mg.L 1, 35-39 g.L 1, and 90-110 g.L 1, respectively）. The relative error of this monitoring method compared with an off-line laboratory monitoring is less than 2%.

Online Test and Fault Diagnosis of Yarn Quality Using Wavelet Analysis and FFT

A new online system of monitoring yarn quality and fault diagnosis is presented. This system integrates the technologies of sensor, signal process, communication, network, computer, control, instrument structure and mass knowledge of experts. Comparing with conventional off-line yarn test, the new system can find the quality defects of yarn online in time and compensate for the lack of expert knowledge in manual analysis. It can save a lot of yarn wasted in off-line test and improve product quality. By using laser sensor to sample the diameter signal of yarn and doing wavelet analysis and FFT to extract fault characteristics, a set of reasoning mechanism is established to analyze yarn quality and locate the fault origination. The experimental results show that new system can do well in monitoring yarn quality online comparing with conventional off-line yarn test. It can test the quality of yarn in real-time with high efficiency and analyze the fault reason accurately. It is very useful to apply this new system to upgrade yarn quality in cotton textile industry at present.

A Study on FMS's Real-time Fault Diagnosis Expert System

ＡＳｔｕｄｙｏｎＦＭＳ＇ｓＲｅａｌ－ｔｉｍｅＦａｕｌｔＤｉａｇｎｏｓｉｓＥｘｐｅｒｔＳｙｓｔｅｍＸＩＡＯＸｉａｎｇｓｈｅｎｇ；ＬＩＵＷｅｎｊｉａｎ；ＳＵＢａｏｈｕａ；ＭＡＹｕｌｉｎ（肖祥胜，刘文剑，苏宝华，马玉林）（ＦＭＳＲｅｓｅａｒｃｈＣｅｎｔｅｒ...

STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

From the requirements of industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool ,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment, strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the real-time requirement of DC hoist,but also can give correct diagnosis results.

Value of Real-Time Bedside Ultrasonography in the Etiologic Diagnosis of Acute Dyspnea

Objective: To explore the value of real-time bedside ultrasonography in the etiologic diagnosis of acute dyspnea. Methods: Sixty-two patients with acute dyspnea who were treated in our hospital from January 2016 to December 2020 were randomly selected and their clinical data were retrospectively analyzed. All patients were randomly divided into a control group for routine examinations (n = 31) and an observation group for real-time beside ultrasonography (n = 31). The costs of medical examinations, examination duration, and diagnostic results of severe pneumonia, acute cardiogenic pulmonary edema, pulmonary embolism, chronic obstructive pulmonary disease, and pneumothorax (including sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy) of the two groups of patients were compared and analyzed. Results: Compared with the control group, the observation group had significantly shorter examinations (P < 0.05). Although the cost of medical examinations of the observation group tended to be higher, the difference between groups was not significant (P > 0.05). Moreover, there were no significant differences in left ventricular ejection fraction, left ventricular end-diastolic diameter, or brain natriuretic peptide between the two groups (P > 0.05). Comparison of the etiologic diagnosis results between the two groups showed that the observation group had significantly higher diagnostic sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy for various causes compared with the control group (P < 0.05). Conclusion: Real-time bedside ultrasonography for the etiologic diagnosis of patients with acute dyspnea was quicker and had higher diagnostic accuracy;thus providing accurate guidance for the disease treatment, and having a higher promotional value in clinical practice compared with routine examinations.

Design on Online Fault Diagnosis System of Stealmaking Gas Dedust Fan

The study introduces the meanings of the technology on dedust fan′s online detection and fault diagnosis,the ways of fault diagnosis,the common fault analysis and the design of stealmaking gas dedust fan′s online fault diagnosis.It shows the whole system′s design,establishment and functional test.XM series modules have been used to realize the online fault diagnosis.The system′s functional requirements are proved by experiment.

Online Diagnosis

＂HERE is your medicine,＂ said the delivery man, handing over two boxes of prescription drugs to Huang. a medical history-making chronic cardiovascular disease patient, identified only by her family name.

Online Supervision of Penicillin Cultivations Based on Rolling MPCA

To reduce the variations of the production process in penicillin cultivations, a rolling multivariate statis-tical approach based on multiway principle component analysis (MPCA) is developed and used for fault diagnosis of penicillin cultivations. Using the moving data windows technique, the static MPCA is extended for use in dy-namic process performance monitoring. The control chart is set up using the historical data collected from the past successful batches, thereby resulting in simplification of monitoring charts, easy tracking of the progress in each batch run, and monitoring the occurrence of the observable upsets. Data from the commercial-scale penicillin fer-mentation process are used to develop the rolling model. Using this method, faults are detected in real time and the corresponding measurements of these faults are directly made through inspection of a few simple plots (t-chart, SPE-chart, and T2-chart). Thus, the present methodology allows the process operator to actively monitor the data from several cultivations simultaneously.

Tools for the diagnosis of hepatitis C virus infection and hepatic fibrosis staging

Hepatitis C virus(HCV)infection represents a major public health issue.Hepatitis C can be cured bytherapy,but many infected individuals are unaware of their status.Effective HCV screening,fast diagnosis and characterization,and hepatic fibrosis staging are highly relevant for controlling transmission,treating infected patients and,consequently,avoiding end-stage liver disease.Exposure to HCV can be determined with high sensitivity and specificity with currently available third generation serology assays.Additionally,the use of point-of-care tests can increase HCV screening opportunities.However,active HCV infection must be confirmed by direct diagnosis methods.Additionally,HCV genotyping is required prior to starting any treatment.Increasingly,high-volume clinical laboratories use different types of automated platforms,which have simplified sample processing,reduced hands-on-time,minimized contamination risks and human error and ensured full traceability of results.Significant advances have also been made in the field of fibrosis stage assessment with the development of non-invasive methods,such as imaging techniques and serum-based tests.However,no single test is currently available that is able to completely replace liver biopsy.This review focuses on approved commercial tools used to diagnose HCV infection and the recommended hepatic fibrosis staging tests.

Diagnosis of Mycobacterium tuberculosis using molecular biology technology

Objective:To present an integrated molecular biology dedicated system for tuberculosis diagnosis.Methods:One hundred and five sputum specimens from patients strongly suspected by clinical parameters of tuberculosis were studied by Ziehl-Neelsen staining,by cultivation on solid medium and by a balanced hemincsted fluorometric PCR system(Orange C3TB) that could preserve worker safety and produce a rather pure material free of potential inhibitors. DNA amplification was performed in a low cost tuberculosis termocycler-fluorotneter.Produced double stranded DNA was flurometrically detected.The whole reaction was conducted in one single tube which would not be opened after adding the processed sample in order to minimize the risk of cross contamination with amplicons.Results:The assay was able to delect 30 bacillus per sample mL with 99.8%interassay variation coefficient.PCR was positive in 23(21.9%) tested samples(21 of them were smear negative).In our study it showed a preliminary sensitivity of 94.5%for sputum and an overall specificity of 98.7%.Conclusions:Total run time of the test is 4 h with 2.5 real working time.All PCR positive samples are also positive by microbiological culture and clinical criteria.Results show that it could be a very useful tool to increase detection efficiency of tuberculosis disease in low bacilus load samples.Furthermore,its low cost and friendly using make it feasible to run in poor regions.

Diagnosis of cytomegalovirus encephalitis using metagenomic nextgeneration sequencing of blood and cerebrospinal fluid:A case report

BACKGROUND Cytomegalovirus(CMV)infections in the population are mostly subclinical,inapparent,or latent.However,it is rare in brain tissue.Most reported CMV encephalitis cases were patients with immunodeficiency.The diagnosis and detection rate of CMV encephalitis in patients with normal immune function needs to be further improved.CASE SUMMARY An 86-year-old male was admitted due to a sudden onset of unconsciousness for 3 h.The patient developed status epilepticus and was relieved after antiepileptic treatment.Encephalitis was considered due to the high signals of diffusionweighted imaging sequences in the right central region by magnetic resonance imaging.Metagenomic next-generation sequencing(mNGS)of blood and cerebrospinal fluid revealed CMV,with unique reads number being 614 and 1,respectively.Simultaneous quantitative PCR results showed CMV positive in blood samples and negative in cerebrospinal fluid samples.The patient was finally diagnosed as CMV encephalitis with status epilepticus.After the antiviral,hormonal,andγ-globulin pulse therapy,the patient’s condition improved,and he was finally discharged.CONCLUSION mNGS could be a reliable approach for the diagnosis of CMV encephalitis,with high efficiency,sensitivity,and specificity.

Design on Vibration Monitoring and Fault Diagnosis System of Large Water Pump Motor

Large water pump motor,whose operation decides the reliability of the whole production line,plays a very important role.Therefore,its online condition monitoring can help companies better know its operation,process fault analysis and protection.The essay mainly studies and designs large water pump motor′s real time vibration monitoring and fault diagnosis system.The essay completes the systems project design,the establishment of the system and performance test.Eddy-currentsensor,XM-120 vibration module,XM-320 axial translation module,XM-362 temperature module,XM-360 process amount module and XM-500 gateway module are used to measure the axial vibration and displacement of main motors.Laboratory tests prove that the system can meet the requirements of motor vibration monitoring.

How reliable is online diffusion of medical information targeting patients and families?

AIM: To determine whether online diffusion of the "Ten Warning Signs of Primary Immunodeficiency Diseases(PID)'' adheres to accepted scientific standards.METHODS: We analyzed how reproducible is online diffusion of a unique instrument, the "Ten Warning Signs of PID", created by the Jeffrey Modell Foundation(JMF),by Google-assisted searches among highly visited sites from professional, academic and scientific organizations;governmental agencies; and patient support/advocacy organizations. We examined the diffusion, consistency of use and adequate referencing of this instrument.Where applicable, variant versions of the instrument were examined for changes in factual content that would have practical impact on physicians or on patients and their families.RESULTS: Among the first 100 sites identified by Google search, 85 faithfully reproduced the JMF model, and correctly referenced to its source. By contrast, the other15 also referenced the JMF source but presented one or more changes in content relative to their purported model and therefore represent uncontrolled variants, of unknown origin. Discrepancies identified in the latter included changes in factual content of the original JMF list(C), as well as removal(R) and introduction(I) of novel signs(Table 2), all made without reference to any scientific publications that might account for the drastic changes in factual content. Factual changes include changes inthe number of infectious episodes considered necessary to raise suspicion of PID, as well as the inclusion of various medical conditions not mentioned in the original.Together, these changes will affect the way physicians use the instrument to consult or to inform patients,and the way patients and families think about the need for specialist consultation in view of a possible PID diagnosis.CONCLUSION: The retrieved adaptations and variants,which significantly depart from the original instrument,raise concerns about standards for scientific information provided online to physicians, patients and families.

A Novel Real-Time Fault Diagnostic System for Steam Turbine Generator Set by Using Strata Hierarchical Artificial Neural Network

The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.