700results about How to "Implement diagnostics" patented technology

The invention provides a layered exchange and control method for real-time monitoring system data by power dispatching. A CIM/XML (common information module)/(extensible markup language) interface, a CIM/E interface and other models of the electric power subsystems are processed and combined to establish a device model of the large power grid, therefore, perfect interaction of various application data including SCADA (supervisory control and data acquisition) real-time data, WAMS (wide area measurement system) data, EMS (enhanced message service) advanced application data and dynamic security analysis warning information can be realized, and power system control commands among the systems can be smoothly transmitted and executed. A data acquisition and exchange function can support multiple communication protocols, multi-application, multi-type and large amounts of data, satisfy the real-time requirements of all transmission environments, and provide comprehensive multi-scale real-time operation information of the power grid for other application analysis systems. A close integration mode is used to realize application integration and each subsystem and application can be monitored and managed, so a uniform access interface and a man-machine interface can be ensured to provide for each application system.

The invention relates to the field of specific molecular identification and diagnosis reagent and in particular discloses a near infrared fluorescent dye with structural formulas I and II; and the invention also discloses a near infrared molecular probe which is obtained through covalent bonding between the near infrared fluorescent dye with the structural formulas I and II and a ligand of specific molecules. The near infrared molecular probe can be used for early diagnosis of turmour diseases.

The invention relates to a battery management system. The battery management system comprises a monitoring device, a mobile terminal and a cloud processor, wherein the monitoring device is used for detecting and transmitting working parameters of each single battery in real time when the battery is charged or discharged, comparing the working parameters of the single battery with the standard parameters, automatically adjusting the working state of the single battery when the working parameter exceeds the standard parameters and transmitting alarming information; the mobile terminal receives and displays the alarming information; the cloud processor receives and stores the working parameters. The invention aims at providing the battery management system, the voltage and temperature of the single battery are monitored and recorded in real time, the working state of a battery pack and the charging-discharging times are detected, and the integral temperature of the battery pack is detected, so that the working state of a charging-discharging device and a heating device is controlled, the functions such as battery quantity management, temperature management, fault pre-judgment and diagnosis and the like can be realized, and the safety and reliability of the battery can be guaranteed; a great amount of data is stored in a cloud server and can be pushed to enterprises for producing the battery or he battery management system users.

The invention discloses an asynchronous motor fault monitoring and diagnosing method based on a model. The method comprises the following steps: firstly, acquiring a three-phase input voltage signal and a three-phase output current signal of an asynchronous motor which can be normally operated, establishing a mathematical model to serve as a fault-free model; carrying out parallel running on the fault-free model under driving of a same input voltage u to obtain a residual signal d; then carrying out time domain analysis on the residual signal d, determining a threshold value eta of the residual signal of the asynchronous motor according to the 3 sigma principle, and judging whether a fault occurs or not by monitoring whether a residual effective value dRMS exceeds the threshold value eta or not when the asynchronous motor is stably operated; carrying out frequency domain analysis on the residual signal d again, and determining the fault type according to a fault feature frequency component fF appeared in a residual spectrum. The monitoring and diagnosing method disclosed by the invention can effectively weaken adverse effects on motor fault monitoring and diagnosis by an input voltage and improve the signal-to-noise ratio of a fault signal, thereby improving the sensitivity of the motor fault monitoring and the reliability of the fault diagnosis.

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.

The invention discloses a rail transit vehicle door system fault diagnosis and early warning method based on multiple conditions. Operation data aiming at a door system is divided into initialized standard data, daily standard data and real-time operation data. Diagnosis and early warning can be accurately realized. Motor monitoring is used to monitor a parameter and a door controller IO signal in real time so as to diagnose a door-system typical fault, and a fault type of the door system, which can be diagnosed, is enriched. Through refining a fault characteristic, diagnosis precision is increased. A real-time health degree model is established, through a real-time parameter and a residual error characteristic of a model, a sub-health phenomenon is diagnosed, and a new method is provided for early warning of an early-stage fault. For a long-term slow degeneration sub-health problem of the door system, by using a traditional diagnosis method, a door system parameter can not be monitored; but, through using the method of the invention, by establishing a degeneration threshold model and comparing and analyzing a long-term change trend of a state parameter characteristic value, a long-term degeneration sub-health state can be identified, early stage early warning is realized and a good application prospect is possessed.

The invention discloses a torsional vibration error monitoring method for a shaft system of a steam turbine generator unit, a monitor and a system. The method includes the steps of S1, collecting output signals of rotation speed sensors, key phase sensors and bearing base vibration acceleration sensors on the steam turbine generator unit; S2, conducting Fourier transform on the collected output signal of each bearing base vibration acceleration sensor to obtain vibration spectrum signals; S3, extracting an amplitude value in the vibration spectrum signal of each bearing base vibration acceleration sensor under the corresponding rotation frequency; S4, obtaining the types of to-be-detected frequencies and separately extracting the corresponding amplitude value and phase under each type of to-be-detected frequency from the vibration spectrum signal of each bearing base vibration acceleration sensor to serve as feature parameters; S5, on the basis of a judgment principle and the extractedfeature parameters, judging whether or not torsional vibration errors of the shaft system occur in the steam turbine generator unit. By means of the method, real-time online monitoring of the torsional vibration errors of the shaft system is achieved.

The invention discloses a method for diagnosing a heat radiation system, comprising the steps of obtaining a temperature value related to a heat resistance value during the normal working of the heat radiation system, figuring out a temperature value related to a designed ideal heat resistance value according to a mathematical model of the system by using the power loss of the system; comparing the obtained value with the calculated value; and determining that the heat radiation system works abnormally if the difference between the obtained value and the calculated value exceeds a preset threshold. Therefore, by using the method disclosed by the invention, the working status of the heat radiation system can be determined in advance so that maintenance and repairing are arranged for the heat radiation system according to working.

The invention discloses an analog circuit fault diagnosis method and an implementation method of the analog circuit fault diagnosis method. The content includes the first part of analog circuit fault feature information extraction, the second part of fault classifier construction, and the third part of implementation of algorithm software. The analog circuit fault diagnosis method includes the following steps of constructing a fault feature information base, selecting an optimal mother wavelet through an information entropy maximizing principle, conducting wavelet decomposition on response nodes of a measured circuit, extracting the optimal feature of the measured circuit, conducting dimensionality reduction on the fault features through principal component analysis, conducting fault classification and intelligent diagnosis, constructing a fault diagnosis device according to the obtained fault feature information and through a multi-classifier cascade connection model and the classifier integration technology so as to recognize existing faults and causes of the faults, and conducting specific implementation on the algorithm through a C#.NET platform and through combination with the Weka software. The diagnosis method and the implementation method have the advantages of being high in fault diagnosis performance, wider in diagnosis range, higher in algorithm robustness and higher in interpretability.

The present invention provides a MIPS-architecture-based CPU outlier location diagnosis method which including the following procedures: machine codes of a present function is scanned; a migration in instructions on constructing a function stack is analyzed and a function stack pointer of an invoking function is computed; the migration in the storage function of a return address is analyzed, the storage location of the return address in the function stack of the present function is computed and the return address is received. The function stack pointer of the call function and the return address obtained according to the present invention can output a call relation of the present function so as to locate and diagnose failure.

The invention discloses an online self-adaptive working condition monitoring and fault diagnosis method for a process industrial course and belongs to the technical field of fault monitoring and diagnosis of complex industrial courses. The method comprises the following steps of firstly, analyzing historical observation data under a normal working condition, introducing an elastic regression network combining Lasso constraints with Ridge constraints to establish an industrial course fault monitoring model on the basis of sparse principal component analysis, and then obtaining a course controllimit to industrial course fault monitoring statistics; during online monitoring of industrial course faults, adopting an order-1 matrix correcting algorithm for resolving a covariance matrix of the online monitoring data, conducting recursion updating on a load matrix of the sparse monitoring model to obtain the course control limit to the course fault monitoring statistics matched with the working condition, and achieving self-adaptive fault detection in the process industry course; finally, according to the detected faults, adopting a contribution plot method for obtaining specific causes of the faults. By means of the method, the faults of the process industry course with complex and changeable working conditions can be self-adaptively monitored for a long time; the method has the advantages of low calculation complexity, high precision, a low report missing rate and the like.

The invention provides a fault diagnosis method for electromechanical equipment based on a gray model, which comprises the steps of first, collecting operation parameters of the current electromechanical equipment; second, enabling the collected operation parameters to serve as an original modeling sequence, building a gray model, and predicting relevant data in the system by using a prediction model; third, obtaining sample data of the operation parameters of the electromechanical equipment in different fault states, building a reference model of faults of the electromechanical equipment, finding out a fault source of the system and determining a fault parameter of the system; fourth, magnifying the variation trend of the data, performing fault diagnosis, performing consistency checking on a predicted value and a solution of an analytical model, substituting the predicted result into a parameter estimation model to solve, obtaining a predicted value of the fault parameter at a futuremoment, predicting the fault parameter according to the prediction model and the parameter estimation model, and judging a fault cause according to a mechanical relationship between the variables. Thefault diagnosis method can realize diagnosis for the fault type of the electromechanical equipment during the operation.

The invention discloses a composite nano-drug for integrated tumor diagnosis and treatment and a preparation method thereof. A chemotherapeutic drug, a photodynamic drug and shRNA plasmids capable of reversing multidrug-resistant MDR1 genes of tumor cells are located through a nano-carrier, a magnetic nano-sphere with precious metal nano-particles arranged on the surface serves as a kernel of the nano-carrier, mesoporous silica with the surface modified with amino groups serves as a shell of the nano-carrier, and a polyelectrolyte thin film layer structure with pH response is formed on the surface of the kernel-shell structure through a layer-by-layer self-assembly technology. The composite nano-drug integrates targeted drug delivery, multimode imaging, chemotherapy, photodynamic therapy and gene therapy, can be applied to early diagnosis of tumors, image mediation and real-time monitoring treatment at the same time and overcomes the defect that tumor diagnosis and treatment are separate in the prior art; besides, the composite nano-drug is simple in preparation process, high in operability and suitable for industrial production.

The invention provides a pressure monitoring system marking instrument of an automobile tyre and a marking method thereof. The marking instrument includes two parts; one is a tyre pressure sensor and the other is a diagnosing marking device; the tyre sensor includes a central control module 2, a sensor 1, a RF transmitting circuit and a lower frequency receiving circuit; the tyre sensor is connected with the lower frequency receiving circuit and the central control module 2; the central control module 2 is connected with the RF transmitting circuit and the sensor 1; the marking instrument consists of an SCM, the lower frequency transmitting circuit, the RF receiving circuit, a human-computer interface and a communication interface; the SCM is connected with the lower frequency transmitting circuit; the tyre sensor is connected with the RF receiving circuit which is connected with the SCM; the SCM is connected with the human-computer interface and the communication interface. The invention can simply realize the diagnosing and the marking of the pressure monitoring system sensor of the tyre, is suitable for the automobile types of any collocation, is not interfered by the tyre pressure monitoring system on other automobiles and can not affect other automobiles.

The invention relates to a novel mobile hospital system which is characterized in that: the system is composed of six mobile medical treatment units including an on-site first aid group (1), an outpatient service group (2), a surgical treatment group (3), an on-site ward group (4), a sorting inspection group (5) and medical technologies guarantee group (6); each mobile medical treatment unit is respectively composed of one to more than two cars provided with medical treatment function, each car is composed of a car chassis, a carriage and medical treatment facilities and medical drugs arranged in the carriage; at least three carriages of the mobile medical treatment units are expandable carriages which are provided with one-side expansion structure or two-side expansion structure; adjacent carriages are connected to form a multi-functional diagnosis and treatment platform which is easy to assemble and disassemble. The invention can finish out-patient reception, realize mobile inspection and diagnosis, perform basic surgical operation and carry out ward and rehabilitation on the patients in postoperative period, provide medicaments and medical facilities, and provide food, water and electricity for patients and hospital staff, etc.

The invention discloses a general fault detecting and maintenance method for equipment. The general fault detecting and maintenance method for the equipment comprises the following steps of carrying out modeling on maintenance knowledge of the equipment faults, automatically detecting and diagnosing the faults and carrying out auxiliary maintenance based on a model, simulating maintenance training based on the model, optimizing an experience knowledge model in real time in the maintenance training process, and synchronizing maintenance information and experience knowledge model information in the maintenance training process. Compared with an existing equipment maintenance method, the general fault detecting and maintenance method for the equipment enables the accumulation and the sharing of equipment maintenance experience knowledge and the automatic diagnosis of equipment faults to be achieved, and assistance can be provided for related maintenance strategies through the analysis and the exploitation of the maintenance record information. In addition, effective transmission of the experience of experts to primary maintainers is achieved through the step of simulation training. The general fault detecting and maintenance method for the equipment is especially suitable for chemical defense troops and other occasions where a large amount of complex equipment needs to be maintained frequently and where the turnover rate of maintenance personnel is high. Practice proves that the general fault detecting and maintenance method for the equipment can obvious improve equipment maintenance efficiency, promote maintenance levels and improve maintenance quality.

The invention provides a method and a device for online diagnosis of a battery short circuit fault, and relates to the technical field of batteries. With the adoption of the diagnostic method providedby the embodiment of the invention, a current state of a battery cell can be determined, and the short circuit fault or a normal state of the battery cell can be judged. The diagnostic method requires a small amount of data and low calculation cost, and can quickly diagnose whether the short circuit fault occurs in any battery cell or one of the cells in a battery pack. The charging current and the discharging current used in the diagnosis process are small; charging and discharging are carried out in one direction, thereby realizing the diagnosis of the short circuit fault in the battery cell under a constant current condition.

The invention relates to a polypeptide immunoassay kit, which comprises a protein G or protein A agarose serving as a solid phase carrier, a purified serum polypeptide antibody and PBS buffer solution. The invention also relates to a polypeptide immunomic spectrometry analysis method, which comprises the following steps: coupling a purified serum antibody with a proper solid carrier; allowing the coupled product to bond with a polypeptide marker antigen specifically, and separating the antigen from the carrier by using eluent; and finally, detecting polypeptide antigen in the eluent by high-pass matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOFMS). Thus, the complete immunomic spectrometry analysis method is established.