389results about How to "Reliable diagnosis" patented technology

A discharge control apparatus arranged in a power conversion system having high-side switching element and low-side switching element to convert the DC power from the battery to a predetermined type of power. The discharge control is performed to discharge a capacitor connected in parallel to the switching elements when an abnormal event occurs. The discharge control apparatus is configured to simulate the operation of the switching elements assuming occurrence of the abnormal event, and transmits a plurality of signals representing the simulation result to a control device via a common photocoupler that isolates the switching elements side and the control device side. The control device diagnoses whether or not a fault occurs in the discharge control or the like by the simulation result.

In this invention, a biomarker discovery method has been developed using specific biotin-labeled oligonucleotide ligands and magnetic streptavidin beads. In one embodiment, the oligonucleotide ligands are firstly generated by whole-cell based SELEX technique. Such ligands can recognize target cells with high affinity and specificity and can distinguish cells that are closely related to target cells even in patient samples. The targets of these oligonucleotide ligands are significant biomarkers for certain cells. These important biomarkers can be captured by forming complexes with biotin-labeled oligonucleotide ligands and collecting the complexes using magnetic streptavidin beads, whereupon the captured biomarkers are analyzed to identify the biomarkers. Analysis of biomarkers include HPLC-Mass Spectroscopy analysis, polyacrylamide gel electrophoresis, flow cytometry, and the like. The identified biomarkers can be used for pathological diagnosis and therapeutic applications. Using the disclosed methods, highly specific biomarkers of any kinds of cells, in particular cancer cells, can easily be identified without prior knowledge of the existence of such biomarkers.

The present invention relates to the diagnosis and monitoring of diseases and conditions by quantifying markers, including degradation products of disease-associated proteins, such as diketopiperazines composed of the two N-terminal amino acids or the two C-terminal amino acids of such proteins. The methods are useful for diagnosing or monitoring various diseases, including multiple sclerosis, Alzheimer's disease and ischemia. The invention further provides binding partners specific for the markers and compositions and kits for conducting the methods of the invention.

In an ultrasound imaging system (UIS), an ultrasound scanning assembly (USC) provides volume data (VD) resulting from a three-dimensional scan of a body (BDY). A feature extractor (FEX) searches for a best match between the volume data (VD) and a geometrical model (GM) of an anatomical entity. The geometrical model (GM) comprises respective segments representing respective anatomic features. Accordingly, the feature extractor (FEX) provides an anatomy-related description (ARD) of the volume data (VD), which identifies respective geometrical locations of respective anatomic features in the volume data (VD). In a preferred embodiment, a slice generator (SLG) generates slices (SX) from the volume data (VD) based on the anatomy-related description (ARD) of the volume data (VD).

The invention relates to a fault diagnosis method for a power converter of a switch reluctance motor; current spectrum of the power converter phase of the switch reluctance motor is tested and used asfault characteristic quantity; and the proportion coefficient Lambada of relative spectrums ratio of the phase current is defined as the ratio of the amplitude of fundamental component of the phase current and the amplitude of direct current component of the phase current. If the coefficient of the relative spectrum ratio of all phase current is basically constant, the power converter does not occur fault; if the amplitudes of the direct current component and the fundamental component of a certain phase current are near to zero respectively, the phase occurs open fault; and compared with thecoefficient of the relative spectrum ratio of the phase current without fault, if the coefficient of the relative spectrum ratio of a certain phase current occurs obvious change, the phase occurs short fault. The method not only can be used for fault detection, discrimination for types of faults and positioning of fault phases when one phase of the power converter of the switch reluctance motor occurs fault, but also can be used for fault detection, discrimination for types of faults and positioning of fault phases when two or more than two phases of the power converter of the switch reluctance motor occur fault.

The invention relates to an electric equipment thermal fault diagnosis method and system and an electronic device. The method comprises the following steps: collecting an infrared image of electric equipment and constructing a convolutional neural network model on the basis of the infrared image; inputting a to-be-detected infrared image into the convolutional neural network model; identifying a temperature scale and electric equipment in the infrared image through the convolutional neural network model; generating an RGB value and temperature reference table on the basis of RGB values of pixels points of the identified temperature scale and upper and lower bounds of the temperature scale, extracting an RGB value of the identified electric equipment, comparing the extracted RGB value withRGB values in the RGB value and temperature reference table, and obtaining a temperature result of the identified electric equipment; carrying out diagnosis on the temperature result through a power grid system diagnosis standard, and determining whether thermal faults of the electric equipment occur. The electric equipment is identified in a highly efficient and accurate manner through the convolutional neural network model, the temperature is read accurately through the RGB value, and a power grid system is improved in intelligent level.

The invention relates to a method for online quantitative-diagnosing a battery micro-short-circuit fault. The method comprises the following steps: pre-establishing a relation table of electric quantity and a charging-discharging voltage, and storing; in an online diagnostic process, performing table lookup or interpolation in the relation table on the voltage while the charge / discharge is over, to obtain the electric quantity of a battery while the charge / discharge is over, thereby estimating a micro-short-circuit current according to the change of the electric quantity along with the time, and judging whether a micro-short-circuit is existent and the severity degree according to the size of the micro-short-circuit current. In allusion to end conditions of a battery pack, such as fully charged, fully discharged, non-fully charged, and non-fully discharged, corresponding diagnostic methods are provided for forming an all-around diagnosis framework. In order to solve problems that the battery is aged and the diagnosis accuracy degree and the calculation precision of the micro-short-circuit current can be affected because the environment temperatures in the different times of the charging and discharging are different, a compensated solution scheme is implemented. The method for the micro-short-circuit current is capable of detecting the micro-short-circuit fault in advance, andoutputting the size of the micro-short-circuit current so as to estimate the severity degree of the fault, and providing the basis for measures, such as warning or answering.

The invention discloses a clustering analysis-based intelligent fault diagnosis method for an antifriction bearing of a mechanical system. A diagnosis model is trained firstly, comprising the following steps: collecting standard vibration signal samples of five fault and normal bearing states of an outer ring, an inner ring, a rolling body and a holding frame; decomposing signals, extracting original vibration signals as well as time domain and frequency domain characteristics of decomposed components to obtain an original characteristic set; removing redundancy by means of a self-weight algorithm and an AP (Affinity Propagation) clustering algorithm to obtain Z optimal characteristics; classifying sample statuses by means of the AP clustering algorithm to obtain a well-trained diagnosis model. A fault diagnosis is performed by the following steps: collecting real-time vibration information of a bearing, decomposing the signals, extracting the optimal characteristics determined by the model, importing the AP clustering algorithm to cluster parameters based on the diagnosis model, comparing with the Z characteristics known in the model to obtain a category of a current unknown signal, so as to complete the fault diagnosis. According to the clustering analysis-based intelligent fault diagnosis method disclosed by the invention, both EEMD (Ensemble Empirical Mode Decomposition) and WPT are utilized to decompose the vibration signals, more refined bearing status information can be acquired, the self-weight algorithm and the AP clustering algorithm increase intelligence of the diagnosis, and therefore accurate diagnosis is ensured.

The invention discloses a server fault automatic detection system and a detection method based on a decision tree, which combines an expert system and an IPMI management unit to generate a historicaldata set. The server running state data, i.e. Abnormal data stream, is obtained by IPMI management unit. According to the abnormal data stream, the new fault feature vector is extracted, and the new feature vector and the fault cause relation pair are formed into a fault data set, and the fault data set is trained into a self-diagnosing decision tree model. When the server fails while running, thecorresponding fault feature vectors are extracted, the fault types are automatically judged by the self-diagnostic decision tree model, After the fault is cleared, the fault feature vector and the fault cause relation are updated and the self-diagnosing fault tree model is updated. Therefore, the fault diagnosis system will be more accurate and reliable with the improvement of the history fault set.

The invention discloses a contact network failure detection and diagnosis method based on an unmanned aerial vehicle, which comprises the following steps: (1) image acquisition: carrying a video camera to shoot along a contact network by an unmanned aerial vehicle so as to respectively acquire contact network images under visible light and infrared light; (2) image graying; (3) image enhancement; (4) image segmentation; (5) image dissection; (6) image fusion: fusing Laplacian pyramid layers under visible light with corresponding Laplacian pyramid layers under infrared light, and carrying out image reconstruction on the fused Laplacian pyramid to obtain a contact network component image after the visible light image and the infrared light image are fused; and (7) carrying out image identification and failure judgment by a BP (back-propagation) neural network. The method can be used for effectively acquiring a contact network image in the operation process of a locomotive in a multidirectional multiangular real-time mode, automatically identifying the contact network component in the image, and judging whether the contact network fails and the type of the failure; and the judgment result is more accurate and reliable, and can better ensure the safety of railway transportation.

Methods, devices, and computer program products facilitate characterization of a patient's upper airway including nose, palate, oral cavity, epiglottis, pharynx and larynx. Identification of full or partial obstructions in the upper airway enables the production of a full assessment the patient's airway. A report is produced that details the various contributing factors to the patient's airway obstructions. The comprehensive assessments can be utilized to effectively carry out various surgical and non-surgical procedures for treating sleep apnea.

Spectral optical imaging at one or more key water absorption fingerprint wavelengths measures the difference in water content between a region of cancerous or precancerous tissue and a region of normal tissue. Water content is an important diagnostic parameter because cancerous and precanerous tissues have different water content than normal tissues. Key water absorption wavelengths include at least one of 980 nanometers (nm), 1195 nm, 1456 nm, 1944 nm, 2880 nm to 3360 nm, and 4720 nm. In the range of 400 nm to 6000 nm, one or more points of negligible water absorption are used as reference points for a comparison with one or more key neighboring water absorption wavelengths. Different images are generated using at least two wavelengths, including a water absorption wavelength and a negligible water absorption wavelength, to yield diagnostic information relevant for classifying a tissue region as cancerous, precancerous, or normal. The results of this comparison can be used to identify regions of cancerous tissue in organs such as the breast, cervix and prostate.

The invention relates to a body position rotating device, which comprises a host machine and at least one bracket, wherein the host machine comprises a transmission mechanism and a rotating device which is driven by the transmission mechanism to rotate, the rotating device comprises at least one rotating shaft, and the bracket is connected with the rotating shaft in a rotation way. The invention also relates to a body position rotating system containing the body position rotating device. During the diagnosis and treatment process, the system can precisely control the variation speed of the patient body position and the space position of the patient, so the diagnosis and the treatment of the semicircular canal dysfunctional diseases are more accurate and more reliable.

The invention belongs to the technical field of wind power equipment detection and relates to an autonomous unmanned aerial vehicle fan blade polling system and method. The autonomous unmanned aerial vehicle fan blade polling system comprises an unmanned aerial vehicle used for carrying out automatic polling on fan blades, a polling vehicle used for stopping and placing the unmanned aerial vehicle, a ground station used for receiving and processing image data sent by the unmanned aerial vehicle to control the unmanned aerial vehicle and a specialist terminal used for judging and analyzing the image data of the ground station. The autonomous unmanned aerial vehicle fan blade polling method comprises the following steps: starting polling; positioning; finding a fan nose by the unmanned aerial vehicle after taking off; processing image data, and judging whether the fan nose is found or not; starting an automatic polling mode; shooting fan blades by the unmanned aerial vehicle flying along the edges of the fan blades, and transmitting blade image data to the ground station; determining whether the fan blades are in fault or not by a maintainer, transmitting the fan blades which can not be determined to the specialist terminal, and judging and analyzing by the specialist terminal; and finishing the polling. The autonomous unmanned aerial vehicle fan blade polling system and method have the advantages that on-the-spot control of the maintainer is not required, a vehicle collision accident can be avoided, manpower resource is saved and popularization and application are easy.

The invention relates to a vibration fault diagnosis method of a hydroelectric generating set. The method comprises the steps of: firstly, carrying out denoising processing on collected original vibration signals by utilizing a random resonance technology; secondly, carrying out characteristic vector extraction on the vibration signals after the denoising by utilizing a multi-dimensional permutation entropy technology; thirdly, establishing fault diagnosis models of optimized support vector machine based on an improved particle swarm algorithm; fourthly, inputting extracted characteristic vectors into the models of the models of optimized support vector machine based on the improved particle swarm algorithm for fault diagnosis. The method is applicable to the vibration fault diagnosis of the hydroelectric generating set, the diagnosis result is high in precision, the fault type of the generating set can be relatively accurately diagnosed, the reliable diagnosis result is provided to operation maintenance personnel of the generation set, the maintenance personnel can process faults timely and rapidly, and the safety and the economic performance of the operation of the generating set are ensured.

The present invention provides a screw tightening diagnostic device which can easily and reliably detect a state of incomplete screw tightening and can allow reliable screw tightening work to be performed. The screw tightening diagnostic device comprises rotational torque detection means for detecting that rotational torque occurring in a rotating shaft of an electric driver has reached a preset rotational torque within a predetermined time range past a minimum required screw-tightening time that has been set in advance according to the length of the screw being tightened, pressure force detection means for detecting that pressure force created by the electric driver pressing the screw when the screw is being tightened has reached a preset pressure force, and screw tightening diagnostic means for diagnosing that screw tightening has been performed in a normal manner when the rotational torque detection means detects that the preset rotational torque has been reached within the predetermined time range past the minimum required time and the pressure force detection means has detected the preset pressure force.

The invention provides a ground grid fault diagnosis method based on a self-adaptive particle swarm optimization algorithm. A fault diagnosis equation is established according to a node voltage analytical method in an electric network theory, then a target optimization function is obtained according to a minimum energy principle, the problem of ground grid fault diagnosis is converted into the problem of target optimization with constraint conditions, then the self-adaptive particle swarm optimization algorithm is adopted to solve the optimization problem, and a diagnosis result is obtained. In the ground grid fault diagnosis method based on the self-adaptive particle swarm optimization algorithm in the invention, through establishing a mathematical model for ground grid fault diagnosis, and using the optimization target function, the ill-condition degree of the diagnosis equation is lowered, and the global optimization capability of the self-adaptive particle swarm optimization algorithm is called, thereby improving the efficiency and the accuracy of solving the optimization model, and enabling the diagnosis result to be accurate and reliable.

The invention is directed toward a micro-endoscope assembly for the removal of tissue and cells from breast ducts comprising a cylindrical guide tube with a beveled distal end defining an internal cylindrical passageway and a first smaller cylindrical tube eccentrically formed in the cylindrical passageway of a smaller diameter than the tube internal cylindrical passageway and adapted to receive and guide an endoscope with a handle assembly wherein the smaller cylindrical tube together with an inner wall surface of the cylindrical guide tube forms a second passageway. A second conduit of a smaller diameter than the smaller cylindrical tube is mounted in the second passageway to divide the second passageway into two separate divided sections and is of sufficient diameter to receive a laser fiber and a micro-endoscope is mounted in the smaller cylindrical tube and a laser is mounted in the second cylindrical tube in the second passageway. The assembly is inserted into a mammary duct and the interior of the duct is viewed until an abnormality is determined in the duct. The tissue and cells from the abnormality area are dislodged, irrigated and aspirated through a suction channel to a removable collection device.

A diagnostic method for estimating the turn-to-turn short circuit fault degree of large generator exciting windings belongs to the technical field of test and includes the steps of firstly acquiring stator vibration signals, stator current, exciting current and an internal power angle of a faulted generator, calculating the ratio of the number of short-circuit turns to the total number of turns by the aid of the signals, and stator vibration signals, stator current, exciting current, an internal power angle and parameters of a generator running normally, simultaneously reckoning in the influence of turn-to-turn short circuit positions on the short circuit degree and obtaining the fault degree value of turn-to-turn short circuit of the exciting windings. The diagnostic method has the advantages of simplicity, feasibility, reliability of diagnostic results and the like, and effectively overcomes the shortcoming that the traditional monitoring technology giving priority to rotor vibration performance is capable of only judging whether generators have turn-to-turn short circuit faults of the exciting windings or not instead of estimating the short circuit fault degree, so that important reference materials can be provided for maintenance of the generators.

A driving diagnosis method and driving diagnosis apparatus enable drivers to recognize where their own driving diagnosis result is located in the driving behavior distribution of a safe driver group and the driving behavior distribution of a dangerous driver group. The degree of a driver's dangerous driving behavior is set as a horizontal axis, the number of drivers according to the dangerous driving behavior degree is set as a vertical axis, and screen information including distributions of a safe driver group and a dangerous driver group as determined by using travel history information about the drivers stored in a storage unit, and a diagnosis result of the dangerous driving behavior of a diagnosis target driver is generated and output.

A diagnostic device of an internal combustion engine diagnoses a condition of a differential pressure sensor. The sensor in a normal condition detects a differential pressure of an exhaust gas between upstream and downstream sides of a diesel particulate filter disposed in an exhaust system of the engine. The sensor in a fault condition detects no differential pressure of the exhaust gas. The device has a block obtaining a first signal from the sensor when the engine is operated in a low flow rate region of the exhaust gas and obtaining a second signal from the sensor when the engine is operated in a high flow rate region of the exhaust gas. A diagnostic block of the device judges from a difference between levels of the first and second signals whether the sensor is in the normal or fault condition.

The present invention obtains certainty of the blood-flow velocity information that is estimated in the blood-flow mapping display. The signal processor in the ultrasound imager is provided with a Doppler velocity operation part configured to calculate a Doppler velocity from echo signals by using the Doppler effect, and a first blood-flow velocity operation part configured to generate a tissue tomographic image from the echo signals and calculate a blood-flow velocity of a predetermined portion from a motion of the tissue, on the basis of the tissue tomographic image. It is further provided with a second blood-flow velocity operation part configured to calculate the blood-flow velocity of the predetermined portion by using the Doppler velocity calculated by the Doppler velocity operation part. Then, a coincidence degree is calculated between the blood-flow velocity calculated by the first velocity operation part and the blood-flow velocity calculated by the second velocity operation part, as to the predetermined portion, and according to the coincidence degree, certainty / reliability of the blood-flow velocity information is obtained and displayed.

A failure cause diagnostic device of the present invention receives input of internal and external state data on injection molding machines and diagnoses failure cause of the injection molding machines by means of a machine learning device. An internal parameter of the machine learning device is obtained by performing machine learning using the state data obtained from the injection molding machines subject to failure cause and the state data obtained from the injection molding machines free of failure cause.

The invention discloses a kit of chemiluminescent immunological analysis measurement of a Treponema pallidum antibody, and preparation method thereof, which belongs to the technical field of immunological analysis medical diagnosis. The kit comprises (1) a carrier coated with a treponema pallidum specific recombination protein antigen; (2) treponema pallidum antibody negative and positive reference substances; (3) an enzyme labeled treponema pallidum specific recombination protein antigen; and (4) an enzyme acted chemiluminescent primer. Furthermore, the preparation method of the kit based on the invention comprises the following steps of (1) preparing TP antibody negative and positive reference substances; (2) labeling the TP specific recombination protein antigen with an enzyme; (3) coating with the carrier; (4) sub-packaging; and (5) assembling. The kit of the detection of the Treponema pallidum antibody has the advantages of easy and simple operation, easy popularization, high sensitivity, strong specificity, good repeatability, safety, non toxicity, and no pollution.

The invention relates to a method for diagnosing fault of a wheel speed sensor of a vehicle, wherein the vehicle is provided with at least two wheels, each of which is provided with a wheel speed sensor. The method comprises the steps of obtaining the output signal of the wheel speed sensor to be examined on the wheel to be examined and the output signal of other wheel speed sensor on at least one other wheel, and diagnosing the fault of the wheel speed sensor to be examined based on the output signal of the wheel speed sensor to be examined on the wheel to be examined and the output signal of the other wheel speed sensor on the at least one other whee. The invention also relates to a controller for a vehicle and a vehicle comprising the controller. The method can be implemented in real time and is capable of diagnosing the fault successfully and preventing false start and misoperation of an ABS (Anti-lock Braking System) and / or an ESP (Electronic Stability Program) reliably, and therefore, reliable control on the wheel is further improved, and the operating performance of the vehicle is improved.

The invention discloses a test strip for fluorescence immunochromatography of myeloperoxidase. The test strip comprises a base plate, a sample pad, a binding pad, a nitrocellulose membrane and an absorbent pad, and the sample pad, the binding pad, the nitrocellulose membrane and the absorbent pad are assembled on the base plate in a sequential overlapping manner, wherein the absorbent pad and thebinding pad are respectively pressed on two ends of the nitrocellulose membrane in an overlapping manner, and a detection area is formed on the surface of the nitrocellulose membrane; the sample pad is pressed on the binding pad in an overlapping manner, and a myeloperoxidase antibody-fluorescent microsphere compound is immobilized on the binding pad; and the nitrocellulose membrane in the detection area is coated with a detection line formed by a monoclonal antibody for recognizing another epitope of myeloperoxidase and a control line formed by a goat anti-mouse IgG polyclonal antibody. The test strip has the advantages of high sensitivity, high stability, realization of the detection linearity being 3.125-600 ng / ml, no non-specificity, short detection time of 5 min, realization of bedside quick test, and great improvement of the clinical diagnosis efficiency.

A diagnostic system for a valve (2) that can be actuated by a position controller via a drive. The diagnostic system includes a device (14) provided for recording, storing, and evaluating, body sound spectra on the valve. In order to enable a valve to be diagnosed in a particularly reliable manner, a body sound spectrum which is recorded when the intact valve (2) is slightly open is stored in the device (14) provided for recording, storing, and evaluating, whereby the valve can be closed for diagnostic purposes and the resemblance of the recorded body sound spectrum to the stored body sound spectrum is used as a criterion for evaluating a non-sealing condition of the valve.