32results about How to "Applicable to online monitoring" patented technology

The invention discloses enriching and detecting equipment for particles. The equipment comprises a sampling chamber body; the sampling chamber body comprises a sampling chamber; a sampling filter membrane is arranged in the sampling chamber, and is used for dividing the sampling chamber into an air inlet chamber and an air outlet chamber; the air inlet chamber is provided with an air inlet, and the air outlet chamber is provided with an air outlet; a detecting device is also arranged in the sampling chamber; a detecting window of the detecting device is communicated with the are inlet chamber and/or the air outlet chamber, so that the particles enriched on the sampling filter membrane are detected and analyzed. According to the enriching and detecting equipment for particles of the invention embodiment, by arranging the sampling filter membrane and the detecting device in the sampling chamber, the enrichment and detection of the particles can be realized simultaneously; problems that particle sampling and enriching are separated, and real-time detection is impossible are solved; errors caused by the movement of the sampling filter membrane are eliminated; and the equipment can be applied in on-line monitoring of particles in gas.

The invention discloses a transformer winding state recognition method based on a fuzzy adaptive resonance neural network. The transformer winding state recognition method comprises the steps of (1) arranging monitoring points of a vibration sensor, setting the sampling frequency and the sampling time of a data acquisition instrument, and acquiring transformer vibration signals; (2) performing wavelet packet decomposition and reconstruction on the transformer vibration signals; (3) extracting vibration signal sub-band energy values; (4) analyzing the vibration signal sub-band energy values of each vibration monitoring point, and selecting feature band energy of valid monitoring points to construct a feature vector so as to act as input of the fuzzy adaptive resonance neural network; (5) building the fuzzy adaptive resonance neural network, adjusting network parameters until set precision is reached; and (6) performing recognition on transformer winding state through the fuzzy adaptive resonance neural network. The transformer winding state recognition method can judge a transformer winding pressing state accurately and quickly, and can be applied to online monitoring and recognition for the transformer winding pressing state.

The invention discloses a fluid motion vector estimation method based on a space-time image and belongs to the technical field of flow field measurement. The method firstly adopts a single camera to acquire a fluid image sequence at fixed time intervals; then an aberration correction linear conversion method is adopted to carry out optical distortion correction on images and a group of speed measurement lines are arranged in a flow measurement direction, wherein each speed measurement line uses a pixel as an x-coordinate and a time as a y-coordinate to synthesize a time-space image; then frequency-spectrum analysis is carried out on the time-space images which undergo edge detection and a discrete approximation method is adopted to solve a frequency-spectrum main direction and obtain a texture main direction vertical to the frequency-spectrum main direction; and according to a mapping relation of an object-space coordinate and an image-space coordinate, physical sizes of the speed measurement lines are solved and the value of a one-dimension flow speed vector is calculated through combination of the texture main direction and the time intervals so as to realize time-averaged flow field calibrating. The fluid motion vector estimation method based on the space-time image can be used for time-averaged flow field measurement of natural tracing water flows such as river surfaces and the like and is especially suitable for online monitoring of flow rates of rivers in a flood period.

The invention provides an overvoltage measuring sensor for an overhead transmission line based on a coupling electric field, and relates to an overvoltage online monitoring sensor of the overhead transmission line. The overvoltage measuring sensor provided by the invention mainly comprises a sensor shell, an insulating pad, a fixing board, a partial voltage capacitor group, a matching resistor, a coaxial cable, an inner screening shell, a data collecting unit and the like. The overvoltage measuring sensor provided by the invention has the advantages of simple structure, convenience in mounting and dismounting, low cost, high accuracy of overvoltage measurement, good linearity, high accuracy, strong generality, wide application range, safe and reliable use and the like. With the adoption of the overvoltage measuring sensor provided by the invention, overvoltage online monitoring can be conveniently carried out on the overhead transmission line. The overvoltage measuring sensor provided by the invention can be widely applied to the overvoltage online monitoring of the overhead transmission line, and is particularly suitable for monitoring overvoltage of 110-1000 kV high-voltage overhead transmission lines on line.

The invention discloses a method for monitoring resistance of valve discs of arresters based on a resistance-capacitance network. The method includes the first step of building an arrestor three-dimensional resistance-capacitance network model based on a three-dimensional finite element static electric field, the second step of solving partition capacitance between the arrestors and capacitance of the valve discs of the arresters, the third step of installing leaked current measurement devices on flanges at the lowest ends of the arresters operating on site, and obtaining actual leaked current measurement values and actual phase angle difference measurement values of the arresters, and the fourth step of solving resistance of the valve discs of the arresters in an iteration mode. The resistance valve of the valve discs of the arresters in operating states can be accurately obtained in the method, errors are controllable, requirements of a project can be met, operation conditions of the arresters are directly and effectively monitored, and meanwhile accurate data references an be provided for power grid operation and maintenance staff; the method is suitable for being used for monitoring a high-voltage zinc oxide arrestor on line.

The invention relates to a method and device for online detection of the state of a leakage conductor with a series gap. The method and device are applied to an electric transmission line of an electric system. A conductor action power frequency current collector is connected with a data processor. A counting indicator is respectively connected with the conductor action power frequency current collector and the data processor. The data processor is connected with a failure alarm indicator. The detected action current and continuous actuation time of the leakage conductor with the series gap are adopted to serve as criteria, alarm threshold values are set for the action current and the voltage value of the continuous actuation time, and when the alarm threshold values are reached or exceeded, an alarm is given for state abnormality of the leakage conductor with the series gap, so that the state of the leakage conductor with the series gap is monitored. A passive monitoring method is adopted in the device, the device is simple in structure, state information of the leakage conductor is provided for operation and maintenance personnel in time, and the operation reliability of the leakage conductor with the series gap is improved.

The invention discloses a wind turbine generator gearbox bearing temperature state monitoring method based on a self-organizing kernel regression model. A self-organizing kernel regression modeling method and a sequential probability ratio residual error analysis method are introduced into wind turbine generator state monitoring. A partial least squares method is adopted to select variables, a self-organizing kernel regression method is adopted to establish a relation model between the bearing temperature of the gearbox and influence variables of the bearing temperature, and the model is usedfor predicting the bearing temperature of the gearbox in the monitoring stage. In order to reduce the false alarm rate and the missing alarm rate of gearbox bearing temperature early warning, a sequential probability ratio method is adopted to analyze a residual error between a gearbox bearing temperature prediction value calculated by the model and an actual value, and when the sequential probability ratio is larger than a set threshold value, a gearbox bearing temperature abnormity alarm is given out. The method is used for analyzing the temperature data of the gearbox bearing, the purposesof temperature monitoring and fault early warning of the gearbox bearing of the wind turbine generator are accurately achieved, and the practicability and universality of the method are verified.

The invention relates to a visual intelligent monitoring system and method for the ash deposition corrosion wear state of medium-low temperature smoke heat exchange equipment, which are characterizedin that actual smoke is adopted, the actual operation working condition is fitted, the smoke inlet temperature and air speed can be adjusted, the working conditions of different smoke temperatures andflow speeds of the whole smoke system are simulated, and online monitoring data are provided; a resistance probe and an eddy current sensor are used for monitoring the conditions of heat exchange, corrosion resistance, wear resistance, ash deposition, coking and the like of the heat exchange tube; based on the convolutional neural network and an intelligent identification and analysis model of amonitoring probe state image, an image data set is made by using a large number of on-site pictures of the monitoring probe and the calculated heat transfer coefficient, ash deposition thermal resistance, corrosion rate and wear rate, and accurate matching of the image and the heat exchange tube state is realized; the intelligent identification and analysis model adopts a convolutional neural network algorithm model to carry out pixel-level processing on the image, state image features are accurately identified, and the accuracy of an analysis result is high; and the heat transfer coefficient,the ash deposition thermal resistance, the corrosion rate and the wear rate of the heat exchange tube are analyzed by images.

The invention discloses an automatic on-line monitor for measuring the water body COD. The monitor comprises an ultraviolet transparent electrolytic cell, wherein a reference electrode, a counter electrode and a working electrode made from a TiO2 nanometer material are arranged in the electrolytic cell, the reference electrode and the counter electrode are arranged on the same side of the working electrode in parallel, the reference electrode, the counter electrode and the working electrode are connected to a constant potential rectifier, a liquid inlet is formed in the upper end, located on one side of the working electrode, of the electrolytic cell, a high-power LED ultraviolet lamp is arranged on the outside of the electrolytic cell, and a liquid outlet is formed in the lower end, located on the other side of the working electrode, of the electrolytic cell. According to the automatic on-line monitor for measuring the water body COD, a high-power ultraviolet LED light source and an efficient heat dissipation structure, the nanometer TiO2 working electrode, a peristaltic pump and a miniature electromagnetic valve are adopted, so that light, rapid and automatic on-line monitoring is realized, the volume of test solution required for analysis each time is small, secondary pollution is not caused, and the monitor is suitable for site and on-line monitoring and can be used for laboratory analysis.

The invention relates to the technical field of environmental monitoring, and particularly discloses a biosensor for online monitoring of ammonia nitrogen concentration in water. The biosensor includes a probe module and a data processing display module connected by a connection module, and is characterized in that: the probe module is a multi-layer tubular structure, and from the inside to the outside is a porous cathode rod, a Nafion coating, a carbon-based anode, Electroactive biofilm, ion screening membrane and protective filter; the connection module is an adjustable telescopic rod wrapped with wires inside. The upper and lower ends of the adjustable telescopic rod are respectively connected to the probe module and the data processing display module, and the two ends of the wire are respectively connected to the carbon-based anode and a porous cathode rod; the data processing and display module includes a sealed waterproof case with a signal conversion memory installed inside, an LED display screen is arranged on the sealed waterproof case, and the two ends of the signal conversion memory are connected with wires. The device of the invention has the advantages of simple structure, low cost, simple maintenance and long service life.

The invention relates to a fault diagnosis method for an ANPC three-level inverter. The fault diagnosis method comprises the following steps: constructing an ANPC three-level inverter circuit simulation model, and acquiring upper, middle and lower bridge arm voltage signals of each phase through a voltage sensor; extracting a wavelet packet energy spectrum as a signal feature, performing dimension reduction on the extracted data to obtain a final signal feature vector, and dividing the final signal feature vector into training data and test data; establishing a joint optimization echo state network JO-ESN, optimizing parameters of an echo state network ESN reserve pool, and training the joint optimization echo state network JO-ESN by using the training data to obtain a trained JO-ESN model; and testing the JO-ESN model by using the test data, and analyzing a diagnosis result. According to the invention, the wavelet packet is used to replace the traditional wavelet and Fourier transform to extract the fault signal, the integrity of the signal is effectively retained, and the LDA is used to reduce the dimension after the fault signal is extracted, so that the fault diagnosis time can be effectively reduced.

The invention discloses an online monitoring method for the exhaust steam dryness of a low-pressure cylinder of a steam turbine. According to the method, an online monitoring system for the exhaust steam dryness of the low-pressure cylinder of the steam turbine is adopted for carrying out monitoring; in the system, exhaust steam of the low-pressure cylinder of the steam turbine is extracted by a throat of a condenser, enters the condenser through a gas-side inlet of the condenser, and then is discharged to the atmosphere by a gas-side outlet of the condenser and a vacuum pump; a refrigerant inlet and a refrigerant outlet of the condenser are connected with an outlet and an inlet of a refrigerating machine respectively; a drain opening of the condenser is connected with a hot well of the condenser through a drain pipeline; and sensors are correspondingly arranged at the gas-side inlet, the gas-side outlet and the drain opening of the condenser. According to the online monitoring methodfor the exhaust steam dryness of the low-pressure cylinder of the steam turbine, the calculation process is simple, and complex iterative calculation is not needed, so that the online monitoring method is more suitable for online monitoring; and the pressure, the temperature, the flow rate and the humidity of a medium are only required to be measured in the monitoring process, and the obtaining method of the parameters is mature and reliable, so that the relatively accurate parameters can be obtained, and the obtaining cost is relatively low.

The invention discloses an ion meter based determination method for determining the cyanide content of wastewater. Equipment used in the method includes an ion meter, a cyanide ion electrode, a temperature compensation electrode, a magnetic stirrer, a 100ml beaker, a 1000ml volumetric flask, a 100ml volumetric flask, a 50ml volumetric flask, an ion enhancer made of a sodium hydroxide saturated solution, and sodium cyanide standard calibration solutions with calibration degrees of 0.1 ppm, 1ppm and 10ppm. Compared with a silver nitrate titration method, the method disclosed by the invention has the beneficial effects of accurate detection method, rapidness, suitability for online monitoring, capability of providing effective data for production control in time and capability of greatly improving the production efficiency.

The invention discloses a power grid fault impact strength quantitative index analysis method based on EWM. The method comprises the following steps: establishing a sub-index model according to power grid fault transient data, solving a voltage drop area, a voltage drop amplitude and a voltage change rate of a target node, establishing a decision matrix and performing utility type normalization processing on matrix data, and then according to EWM, calculating characteristic proportions of evaluation objects, calculating entropy values of indexes, calculating information redundancy, calculating weights of all the indexes, and finally calculating a comprehensive score of each evaluation object, namely a value of a quantitative index. The method is simple in principle, rapid in calculation, good in performance, capable of achieving quantitative expression of the system node voltage stability after the power system is subjected to fault impact in a short time, good in use value, and capable of enhancing manual judgment of the severity of the system fault to a certain extent through data visualization, and a control strategy is adopted to prevent further expansion of the fault.