176 results about "Diagnosis tool" patented technology

A wireless capsule as a disease diagnosis tool in vivo can be introduced into a biological body by a native and/or artificial open, or endoscope, or an injection. The information obtained from a micro-spectrometer, and/or an imaging system, or a micro-biosensor, all of which are built-in a wireless capsule, can be transmitted to the outside of the biological body for medical diagnoses. In addition, a real-time specimen collection device is integrated with the diagnostic system for the in-depth in vitro analysis

The invention provides a fault on-line diagnosis and early warning method of a flameproof dry-type transformer for mine in order to raise the accuracy and rapidity of fault diagnosis. The method comprises the following steps: determining monitoring quantity; extracting characteristic values: extracting a three-dimensional spectra parameter and two-dimensional statistical parameter in a partial discharge signal as characteristic quantities, with regard to operation voltage, current and iron core leakage current, extracting effective values of the operation voltage, the current and the iron core leakage current as characteristic values, and taking real-time values of a temperature parameter as characteristic quantities; using a normalization method to calculate a corresponding value of each characteristic quantity and taking the corresponding value as an input parameter of an intelligent diagnosis system; collecting values of each monitoring quantity under different environment, and obtaining training and testing samples of a nerve network under corresponding environment; establishing a nerve network: selecting a generalized RBF nerve network intelligent diagnosis method to establish a nerve network; utilizing a sample data training nerve network and forming a fault diagnosis tool; establishing a database; storing collected real time data and the above diagnosis result into a ground server real-time database and a real-time early warning information table, and carrying out diagnosis and early warning through an expert system.

A service diagnosis tool for performance analysis and failure remediation of electromechanical devices, e.g. printers or copiers. The tool communicates with an actual device of a specific type, and accesses data from a knowledge data set containing knowledge relating to properties of devices of the specific type, a device data set and a history data set. The device data set contains parameters of the actual device, the parameters including actual error data indicating errors that occurred in an actual period. The historical data includes historical error data of the actual device relating to errors, performance and/or remediation that occurred before the actual period. The tool has a calculation unit for analyzing and combining data from the data sets for generating a prioritized item list indicative of remedial steps to be executed. The items in the list relate to performance and/or failure of the actual device, and are organized according to priority rules.

The invention discloses a disease diagnosis system, which comprises an image acquiring module, a diagnosis model building module, and a disease diagnosis module; the image acquiring module is used foracquiring a marked medical image; the diagnosis model building module is used for structuring an in-depth neutral network on the basis of the marked medical image, performing the optimized training on the in-depth neutral network, and outputting the diagnosis model; the disease diagnosis module is used for acquiring the medical image of a patient to be treated, and applying the diagnosis model toanalyze the marked medical image and the medical image of the patient to be treated, and confirming the diagnosis result of the patient to be treated. The disease diagnosis system uses an artificialintelligence technology, helps a doctor to diagnose the disease and realizes the specification, standardization and homogenization of the disease screening and diagnosis.

The invention discloses a high-voltage circuit breaker mechanical state monitoring and fault diagnosis method. The method comprises the following steps of 1) according to the historical fault data statistics result of high-voltage circuit breakers, carrying out an artificial fault simulation experiment on some common high-voltage circuit breakers to obtain fault data; 2) analyzing the fault current data, and extracting the current data characteristic quantity of each mechanism fault to be used as one of state diagnosis classification basis; 3) analyzing a fault vibration signal, adopting wavelet packet decomposition and sample entropy for processing high-frequency and low-frequency components to obtain corresponding wavelet packet relative energy and sample entropy respectively to be usedas vibration signal characteristic quantities; and 4) performing dimensionality reduction on the vibration signal characteristic quantities through principal component analysis to be used as one of state diagnosis classification basis, and carrying out state diagnosis on the circuit breaker by a support vector machine. By adoption of the method, the multi-dimensional information of the fault statecan be effectively utilized, so that the development of multi-parameter multi-dimensional mapping fault diagnosis of the circuit breaker can be promoted.

The invention provides a vehicle fault diagnosis method. The vehicle fault diagnosis method comprises the following steps: a whole vehicle controller can be used to receive fault messages transmitted by a plurality of part electronic controllers; the whole vehicle controller can be used to analyze the fault information of the plurality of part electronic controllers, and the fault information comprises fault symbols; the whole vehicle controller can be used to manage the fault information of the plurality of part electronic controller, and can be used to determine whether the time of receiving one fault symbol reaches the predetermined value in an accumulated manner, when yes, the fault information can be recorded; a diagnosis tool can be used to read the fault information of a plurality of part electronic controllers from the whole vehicle controller. According to the vehicle fault diagnosis method, the whole vehicle controller can be used for the unified management of the vehicle faults, and the after-sales maintenance of the vehicle can be facilitated. The invention also provides a vehicle fault diagnosis system.

The present invention discloses a medical big data based disease automatic assistance diagnosis system and method. The system comprises: a background data storage unit; an information processing unit, which specifically comprises: a statistical classification module, used for acquiring case data in the background data storage unit, and performing statistical classification on the case data, so as to obtain a symptom set and a definitively diagnosed disease type set; a diagnosed disease set calculation module, used for calculating a definitively diagnosed symptom set of various diseases according to the symptom set and the disease type set that are obtained by the statistical classification module; and a disease automatic diagnosis module, used for acquiring disease symptom data provided by a user, generating a selection symptom set, comparing the selection symptom set with the definitively diagnosed symptom set of various diseases and performing calculation, so as to obtain a disease determination result; and a man-machine interaction unit, used for displaying an interface of selecting a disease by the user, and outputting a disease diagnosis result. The method disclosed by the present invention is simple, easy and strong in operatability, and provides a new clinic assistant diagnosis tool for the medical field, and reduces an error/miss diagnosis rate.

Invention concerns a machine fault diagnostic system. Sensors are provided for sensing an operational parameter of the machine over time in use and outputting corresponding sensor signals. A machine operation diagnosis tool is arranged to receive the sensor signals and has a data store for operational parameter data indicative of a normal mode of machine operation and operational parameter data indicative of one or more known machine faults. The diagnosis tool has one or more processor arranged to compare the received sensor signals with the operational parameter data in the data store in order to determine a match with either the normal mode of operation or the one or more known machine fault, and wherein the processor sentences an unknown fault in the event that said match is not established. The system may record unknown faults and update the diagnosis tool as unknown faults become recognizable faults.

The invention discloses power transformer fault intelligent diagnosis technology, method and device, and relates to power transformer fault intelligent diagnosis technology and method and the intelligent diagnosis device based on the technology and the method. The intelligent diagnosis device comprises a data acquisition and transmission part and a management and display part. The intelligent diagnosis device is characterized in that power transformer fault intelligent diagnosis technology and method comprise a dissolved gas in oil analysis and diagnosis technology, a partial discharge online monitoring and diagnosis technology, an infrared temperature measurement diagnosis technology, a vibration analysis and diagnosis method, a frequency response analysis and diagnosis method, a winding temperature indication diagnosis method and a low voltage impulse response test diagnosis method, and fault intelligent diagnosis is carried out. According to the invention, a multi-parameter state monitoring method is used, and multiple fault intelligent diagnosis technologies and methods are integrated; valuable data which can be used to diagnose a fault and find out the position are given; the cause of the defect is eliminated; the shutdown time and the cost are reduced; and the purposes of power transformer intelligent monitoring and intelligent diagnosis are realized.

The invention relates to a group of anti-human CD146 molecules which are developed by utilizing the biological technology and an established high-sensitive sandwich ELISA method for detecting the solubility CD146. The invention includes anti-human CD146 mouse monoclonal antibodies of AA1, AA2, AA3, AA4, AA5 and AA7, and the method of utilizing the combination of the antibodies and the sandwich ELISA specificity for detecting the solubility CD146. The group of antibodies can identify the human source CD146 on the molecular, the cellular and the tissue levels, which can be divided into two categories based on the identified different epitopes. The sandwich ELISA method which is combined by the antibody AA1 and another strain of anti-human CD146 mouse monoclonal antibody AA98 can detect the solubility CD146 at each milliliter nano-gram level. The antibodies and the detection means can become the effective detection or diagnosis tools and method in the basic research or the clinical application and provide good carriers for the targeted therapy of the CD146-related diseases at the same time.

The invention discloses a diagnosis device of a valve. The diagnosis device comprises a control device for outputting a control signal and collecting signals, wherein the control device is respectively connected with a valve executing mechanism control signal input device, a valve rod position feedback signal obtaining device, a valve rod strain signal obtaining device and a plurality of valve executing mechanism driving source signal obtaining devices; the control device is used for outputting the control signal and collecting a corresponding valve rod position feedback signal, a valve rod strain signal, and valve executing mechanism driving source signals. The invention further discloses a diagnosis method applying the diagnosis device of the valve. According to the technology disclosed by the invention, the valve can be diagnosed in real time online, so that the safe and convenient diagnosis method is provided for the maintenance of the valve, and foundation for automatic control is further provided.

The invention discloses an integrated navigation system fault diagnosis method based on Gaussian process regression. The integrated navigation system fault diagnosis method comprises five steps: 1, collecting a sample, and setting a fault detection threshold; 2, initializing a Gaussian process regression model; 3, training the Gaussian process regression model; 4, inputting a system measurement quantity to the Gaussian process regression model so as to obtain a predicted value of Kalman filtering information and a variance of the predicted value; 5, constructing a fault detection quantity, comparing the fault detection quantity with a fault detection threshold, and judging whether a fault occurs. The integrated navigation system fault diagnosis method has the advantages of being easy to realize, being capable of giving the variance of a predicted output value, and the like, and provides an assurance to the reliability and accuracy of combined navigation. Model parameters are remarkably reduced, and hyper-parameters can be conveniently calculated through a numerical analysis method, thus the integrated navigation system fault diagnosis method has the advantage of being easy to realize.

The invention discloses a fault diagnosis method of a traction transformer based on model diagnosis. The method comprises the steps of building a two-layer structure model of the traction transformer by knowledge of a structure model of the traction transformer, measuring actual operating parameters of the traction transformer by a voltage transformer and a current transformer, and inputting the parameters into a diagnostic system to diagnose. The diagnosis is divided into an offline diagnosis part and an online diagnosis part by the fault diagnosis method at the same time. Therefore, the diagnosis speed is improved, and a fault element and the fault type are finally diagnosed by consistency diagnosis and abductive diagnosis. A simulation result indicates that the faults such as short circuit, open circuit, and interturn grounding of a winding of the traction transformer can be rapidly and accurately diagnosed, and the fault diagnosis method is a new fault diagnosis method of the traction transformer.

The invention discloses a numerical control machine tool operation state health diagnosis system and diagnosis method. The system comprises an edge data acquisition module, a cloud platform and a client, the cloud platform is connected with the edge data acquisition module and the client. The cloud platform comprises a predictive diagnosis module, an information management and storage module and an application server; and the predictive diagnosis module comprises a real-time diagnosis module, a post-processing diagnosis module and a comprehensive diagnosis module. According to the system, a cloud platform with a diagnosis function is used for replacing an upper computer, the expandability is greatly improved, the diagnosis and prediction tasks can be completed only by deploying corresponding sensors on the machine tool and connecting the sensors to the cloud platform, the real-time state data of the machine tool are managed in a centralized mode through the cloud platform, and the health condition of the machine tool is evaluated and predicted through the diagnosis technology based on the neural network. The diagnosis method is simpler, and the health diagnosis accuracy is high.

The invention relates to a motor vehicle (5) diagnosis system provided with a first wireless communication module (6), said diagnosis system comprising: a diagnosis tool (2) configured to send a diagnosis request to the motor vehicle (5); a second wireless communication module (3) configured to transmit the diagnosis request sent by the diagnosis tool (2) to the first module (6); and a software module (8) acting as an interface between the diagnosis tool (2) and the second wireless communication module (3), said software module (8) being configured to: intercept the diagnosis request sent by the diagnosis tool (2), encapsulate the intercepted diagnosis request in a frame, and transfer the frame encapsulating the diagnosis request to the second wireless communication module (3).

The invention relates to an intelligent tool fault diagnosis method. The intelligent tool fault diagnosis method comprises the steps that a PLC is arranged between the CNC end of a machine tool and the machine tool end; a plurality of tool vibration signals x(t) produced in the machine tool machining process are obtained by the PLC, all the collected tool vibration signals x(t) are processed, and tool characteristics are extracted; the correspondence relation between the tool vibration signals and a tool abrasion fault is built according to the obtained tool characteristic information so that the tool abrasion fault can be intelligently identified; each tool vibration signal is trained to obtain a hidden Markov model lambda; and the likelihood ratios are calculated through the trained hidden Markov models lambda, the sequences O of the tool vibration signals are input into all the trained hidden Markov models, the likelihood ratio of each tool vibration signal under the corresponding hidden Markov model is obtained, and then the tool state is identified. According to the intelligent tool fault diagnosis method, the characteristics of the machine tool and the characteristics of the vibration signals in the tool machining process are combined, the tool abrasion fault is automatically detected, and the tool abrasion state can be automatically identified.

The invention discloses a fault diagnosis method of a fuel regulator of an aero-engine. The method comprises the following steps: on the basis of a part-level modeling method, a high precision model of a fuel regulator is established; on the basis of the conventional model, fault parameters are introduced by means of soft faults to simulate various typical faults that may occur in a real fuel regulator; a parameter that can reflect the system state is selected from a measurable output value of the fuel regulator and parameter feature values in various fault modes are extracted; sample sets ofdifferent fault modes are established and the sample sets are divided into training samples and testing samples; the samples are trained and tested by using a BP neural network as a classifier so as to realize the fault diagnosis of the fuel regulator. According to the invention, problems of many parameters for collection, tedious operations, and low diagnosis accuracy according to the existing fuel regulator fault diagnosis technology are solved. The fault diagnosis method can be applied to the field of fault diagnosis of aero engine fuel regulators or the fault diagnosis field of other hydraulic systems.

A scan diagnosis system for testing and diagnosing a device-under-test is disclosed. The system includes a semiconductor tester adapted for coupling to the device-under-test and operative to generate pattern signals in the ATE domain to test the device-under-test and produce test output data in the ATE domain. An ATPG diagnosis tool is operative to generate ATPG pattern data and ATPG results data in the ATPG domain. A translator serves to effect automatic correlation of data between the ATPG domain and the ATE domain to allow data communication between the tester and the tool.

The invention discloses a substation automation wide-area operation and maintenance interoperability testing system and method. The substation automation wide-area operation and maintenance interoperability testing system comprises a wide-area operation and maintenance master station, a wide-area operation and maintenance sub-station and experimental equipment, and is characterized in that the wide-area operation and maintenance master station comprises a service management center and an operation and maintenance center, the wide-area operation and maintenance sub-station comprises a substation data communication gateway computer and a monitoring host, and the experimental equipment comprise a GPS consistency testing client tool, a regulation and control master station preposed simulation system, a GSP based message analysis and diagnosis tool and a protection measurement and control information simulation system. The system and method disclosed by the invention are used for carrying out an interoperability test on communication services between the wide-area operation and maintenance master station and the wide-area operation and maintenance sub-station of substation automation, checking the applicability of a general service protocol of an electric power system on various applications of substation automation equipment wide-area operation and maintenance, and achieving consistence in understanding of monitoring manufacturers for various applications of wide-area operation and maintenance before implementation of field applications.

The invention relates to a fault diagnosis method of a high voltage breaker, and belongs to online monitoring and fault diagnosis technologies of the high-voltage breakers. The method comprises that 1) the current of a closing/opening coil of the high voltage breaker is collected to form a data set; 2) different related state types of the breaker are coded, data is extracted randomly according tocertain proportion and serves as a pre-training sample, parameter tuning sample and a test sample, and the data is normalized; 3) a DBN classification network model is established, the pre-training data sample without a label is used to carry out network pre-training, and the sample with a label is used to carry out parameter tuning; and 4) the DBN after training is used for fault diagnosis of thehigh-voltage breaker. According to the method, manual operation of feature extraction can be avoided effectively, the problem of local optimization of network training is overcome, the operation state of the breaker can be identified effectively, the defect that a present fault diagnosis system cannot identify the degree of a fault is overcome, and breaker fault diagnosis becomes more intelligent.

Disclosed is an excavating diagnosis method of failure data based on a bayesian network. The method is used for diagnosis of equipment failures. Parameter points, capable of being obtained, of existing devices with failures are monitored in advance, and a monitoring signal and a failure type are used as input variables of the bayesian network; then, nodes are sorted at random, side adding or changing operation is carried out in the search problem, the generated bayesian network is graded and stored in a database, finally, the bayesian network with the highest score in the database is used as an equipment failure diagnosis inference graph, equipment data needed to be diagnosed are input into the diagnosis inference graph, and the diagnosis result can be obtained. The search density can be set according to the actual condition, and can be prevented from being caught in endless loop; a penalty term can be adjusted so as to prevent excessive fitting; an initial sequence is generated at random to enable the system to have a certain roughness; a user can see the equipment failure diagnosis inference graph, and therefore logic analysis can be carried out on the failure happening mechanism.

The invention discloses a fault root cause positioning method and system for a micro-service architecture information system. The system comprises an index data collection module, an anomaly detection module, a micro-service dependence graph construction module and a reverse tracking module. A root cause positioning algorithm based on a fault propagation chain model is designed by establishing a dynamic correlation analysis method between micro-services, the propagation process of related faults is identified while the fault root cause service is positioned, the interpretability of fault positioning and diagnosis is improved, and the method can be applied to a micro-service architecture information system and is high in practicability. The accuracy of dynamic association modeling in the micro-service architecture information system is improved, the use convenience of a fault diagnosis tool of the micro-service architecture information system is improved through a micro-service performance index data driving method, and the deployment time and energy are saved.

The invention discloses a fault diagnosis method for damping springs of a vibrating screen and belongs to spring fault diagnosis methods. The method includes: subjecting actual measured static deformation values of four springs without faults and actual measured static deformation values of the four springs with faults to one-to-one corresponding substraction, and implementing fault spring diagnosis under a static state; and analyzing actual measured free vibration response at four spring supports of the vibration screen after the springs fail, and implementing fault spring diagnosis under a dynamic state by utilizing a three-dimensional bar chart and a three-dimensional spectrum array. When diagnosis results under the two states are uniform, the diagnosis results can be considered as a diagnosis result, when the diagnosis results under the two states are nonuniform, the steps need to be repeated until the diagnosis results are uniform. The fault diagnosis method for the damping springs of the vibrating screen has the advantages that four displacement sensors and a collect card are adopted, so that equipment is simple, input is small, and mounting is convenient; with the hardware configuration, static and dynamic spring fault diagnosis methods are mutually combined to realize high-precision diagnosis, the dynamic spring fault diagnosis method can be carried out during the process of free vibration when the vibrating screen stops working, and no other power input is needed.

The invention relates to an expert system and method for preventing and controlling sludge bulking under the diagnosis based on operation condition of a sewage disposal plant, belonging to the crossing fields of an environmental engineering technology, a computer software technology and an automatic control technology, and overcoming the defects that the diagnosis speed is slow and man-made erroneous judgment is easy to make because the sludge bulking is analyzed, diagnosed and controlled manually only in the traditional sewage disposal technology. A knowledge base module is used for storing diagnosis methods and control methods, wherein rules are mainly established based on process running parameters and sludge sedimentation performances, and a man-machine interaction unit can be used for timely updating the methods stored in the knowledge base module and adding successful and unsuccessful cases during diagnosis and control. A database module is used for storing original data, offline detection data and secondary data acquired by an online monitoring instrument. An inference engine module is used for carrying out forward reasoning. An explanation engine module is used for explaining. A man-machine interaction unit is used for finishing information communication between users and the system.

The invention discloses a teaching system of a flipped classroom pattern. The teaching system comprises a before-class part, a class part and an after-class part; students and a teacher use smart phones, a computer and an intelligent large screen to realize teaching of the flipped classroom pattern, and a flipped classroom APP is installed in each of the smart phones, the computer and the intelligent large screen; and the flipped classroom APP is provided with a micro course resource, a typical case resource, a professional public policy diagnosis tool, a diverse grouping pattern, a real-time statistical tool, a five-star review function, a review paintbrush, a countdown tool, a real-time interaction zone, a multi-dimensional performance scoring mode and a real-time linkage function. The social security theory teaching system created for flipped classrooms breaks through the single and boring theoretical teaching pattern of the traditional public management course, sufficiently animates the classroom atmosphere via various teaching modes, deepens the understanding on relevant theoretical knowledge, and cultivates students' abilities of self-regulated learning, problem discovery and collaborative solution of problems.