8491 results about "Air compressor" patented technology

The invention discloses an air compressor frequency conversion and energy saving control method and system based on data analysis, and particularly relates to the technical field of air compressor control. Aiming at the problems of frequency jitter and motor instability of the permanent magnet variable-frequency screw air compressor in a high-frequency air consumption fluctuation scene, the method comprises the following steps of: acquiring exhaust pressure, air consumption flow, motor current, frequency converter output frequency and environment temperature data in real time, performing trend prediction by utilizing a data analysis model, and constructing a multi-dimensional working condition characteristic curve; a gas consumption fluctuation mode is accurately recognized, and predictive judgment is made before frequency fluctuation is about to exceed the limit; according to a prediction result, a frequency converter control strategy is dynamically adjusted, the frequency response rate is buffered in advance, an energy-saving mode is automatically triggered when the load change trend is abnormal, rotating speed control is optimized in real time, and according to the method, the operation stability and response precision of the air compressor are effectively improved, energy consumption is remarkably reduced, and the service life of equipment is prolonged; the method is suitable for the intelligent energy-saving operation requirement in a high-beat and high-precision manufacturing scene.

The invention relates to an air compressor remote fault diagnosis system and method based on a CNN and a type-2 fuzzy algorithm, and belongs to the field of intelligent recognition, and the system comprises a signal acquisition system, a data transmission system, a remote monitoring system and the CNN and type-2 fuzzy algorithm. According to the system, parameters such as pressure, temperature, rotating speed, vibration frequency and amplitude of the air compressor are monitored in real time through the sensor and transmitted to the server for processing, fault diagnosis and prediction are conducted through the CNN and the type-2 fuzzy algorithm, and the function of remotely monitoring and diagnosing the faults of the air compressor is achieved. In this way, the feature extraction capability can be better improved, so that the classification performance is improved, and the accuracy of fault diagnosis is improved. Meanwhile, when sensor noise causes fluctuation of fault features, the type-2 fuzzy algorithm can adjust a classification decision according to an uncertainty range of a fuzzy membership function, so that interference of noise on fault classification is reduced, and better robustness is achieved.

The invention relates to the technical field of grinding regulation and control, in particular to a tool alignment control and grinding machining method suitable for an air compressor shell. According to the method, an initial grinding point of a grinding tool is determined for grinding according to the local distribution condition of the height and thickness of the surface of the shell; according to the grinding temperature and the grinding depth of the grinding wheel and the time sequence change degree in the grinding process, the grinding strength value of the surface of the machine shell is analyzed, and a grinding change trend index is obtained; according to the height and thickness deviation change and the vibration amplitude of the contact area in the grinding process, in combination with a grinding change trend index, parameter adjustment factors of grinding parameters are determined, and the combination of the rotating speed and the feeding speed of a grinding wheel is adjusted in real time for grinding. According to the method, the real-time parameter adjustment factors in the grinding process are determined by combining the grinding real-time strength change with the shell surface quality, correlation adjustment is carried out on the grinding parameters, and the surface adaptability and the grinding effect in the grinding process are improved.

The invention relates to an air compressor energy-saving control method, device and equipment based on an AI algorithm, and the method comprises the steps: collecting the operation data of an air compressor in real time; the real-time energy efficiency ratio of each air compressor is dynamically calculated according to the energy consumption and the gas production rate, and the air compressors with the energy efficiency ratios lower than 75% are marked as low-efficiency equipment; on the basis of the historical gas consumption data, the production line schedule, the environmental parameters and the equipment state, the gas consumption demand in the future time period and the future efficiency of the air compressor are predicted through the air pressure demand prediction model and the EER prediction model; on the premise of meeting the gas demand in the future period, with the goal of minimizing the total energy consumption and the constraint conditions that the total gas production rate meets the predicted gas demand and the load rate of the frequency converter is limited to be 50%-100%, an optimal switch combination and load distribution scheme is obtained, and a corresponding control instruction is generated and executed, so that the production demand is accurately matched, and meanwhile, the production efficiency is improved. The energy consumption of the system is reduced, the equipment management efficiency is improved, and the purpose of refined and intelligent energy-saving control of the air compression system is achieved.

Systems and methods disclosed herein include a portable air pump and inflator system comprising an electric air compressor, a compressor housing enclosing the electric air compressor, an air hose attached or attachable to an air outlet of the electric air compressor, and a housing base attached to the compressor housing. The housing base comprises a rigid base portion that is attached to the compressor housing and a plurality of flexible foot extensions that extend from the rigid base portion. The flexible foot extensions are configured to support the portable air pump and inflator system on a surface and dampen vibrations caused by the operation of the electric air compressor.

The invention discloses a screw air compressor dynamic adjustment method and system based on multi-sensor feedback, and relates to the technical field of air compressor adjustment. The method comprises the steps that N air compressor key parts of a target screw air compressor are obtained through division, and a screw air compressor adjusting platform is built according to the N air compressor key parts; n multi-source heterogeneous operation parameters of the N air compressor key parts are collected, state prediction modeling is conducted on the N multi-source heterogeneous operation parameters, and N air compressor state prediction models are generated; feedback regulation analysis is conducted on the N air compressor state prediction models, and target air compressor regulation parameters are determined; and based on matching of the target air compressor adjusting parameters and the adjusting executing mechanism, the adjusting mechanism combination is activated, and air compressor operation dynamic adjusting is executed. The technical problems that in the prior art, a screw type air compressor is insufficient in adjusting precision and lags behind in adjusting response are solved, and the technical effect that accurate dynamic adjusting of the screw type air compressor is achieved based on multi-sensor feedback is achieved.

The present application relates to the technical field of safety management, in particular to a mine air compressor safe operation management method and system based on deep learning, comprising the following steps: collecting output pressure and input current, calculating slope change rate and direction switching frequency, clustering fluctuation amplitude and frequency by time period, generating working condition fluctuation label, identifying state mutation point and duration, calculating risk level label, analyzing load fluctuation coefficient, judging periodic fatigue early warning, combining synchronous rate to identify abnormal section, judging the need to switch state and generating instruction set. In the present application, by constructing the load risk level sequence on the basis of state duration and mutation point identification, the quantitative identification of multi-period load deviation is realized, the early warning period is marked by combining risk level and fatigue deviation ratio, and the abnormal load trend is judged according to the change direction of pressure and return gas flow, which strengthens the capture ability of abnormal operation section and improves the accuracy of operation safety risk identification and the adaptability of response strategy.

The invention provides a full-automatic air compressor system integrating intelligent monitoring and fault diagnosis, and relates to the technical field of air compressor diagnosis. The management main system comprises an AI intelligent interaction module, an AR visual presentation module, a multi-source data fusion module, an intelligent diagnosis decision module, a fault prediction and prevention module, a user feedback learning module, an equipment linkage coordination module and a system core regulation and control unit. Operating parameters and microstructure change data of the air compressor are collected, and a high-precision comprehensive data vector is generated in cooperation with an algorithm based on tensor decomposition and generative adversarial network fusion. Compared with a traditional remote monitoring mode which depends on a single traditional sensor, the system can sense micro fault characteristics of a molecular level, and in cluster operation and maintenance of the air compressors in a factory, micro cracks of the air inlet valve are detected in advance, so that the problem of insufficient air pressure caused by delayed sensing is avoided.

The invention relates to the technical field of underground engineering groove wall stability physical simulation, and discloses a model test device and method for stratum excavation and grouting reinforcement, and the model test device comprises a grooving construction module, a grouting support module and a dynamic monitoring module. The device adopts a pressing plate method to simulate a grooving excavation process, so that the device conforms to an actual excavation working condition from top to bottom; a servo motor drives a power lead screw to drive a model groove to move downwards, so that a free face is formed in a sand layer, and the actual excavation working condition is simulated; a water tank for storing fresh slurry is pressurized by using an air compressor, so that the slurry overflows from the hollow model groove section, and the permeation and invasion process of the slurry is simulated; and a water and soil pressure gauge and a matched acquisition device are arranged in the sand layer, and a soil displacement field is obtained by utilizing a particle image velocity measurement system, so that the visualization of the whole process of excavation-grouting-invasion is realized. It is ensured that simulation results are highly matched with actual working conditions, and accuracy and reliability are improved.

The invention discloses a split type vehicle-mounted intelligent oxygen supply system based on the VPSA technology and a control method, and the system comprises an air compressor which is arranged at the inner side of a wheel cover protection plate of a rear wheel of a vehicle and is used for compressing air and discharging the compressed air to a molecular sieve assembly; the molecular sieve assembly is arranged below a spare tire in a trunk of the vehicle and is used for separating nitrogen from oxygen in the air to generate oxygen; the oxygen conveying pipe is arranged on the inner side of the vehicle roof and connected with an oxygen outlet of the molecular sieve assembly, and an oxygen outlet is formed in the position, located above each seat, of the oxygen conveying pipe; the detection module comprises an altitude sensor and an oxygen concentration sensor, the altitude sensor is installed on the molecular sieve assembly, and the oxygen concentration sensor is arranged on a ceiling in a vehicle compartment; distributed arrangement is adopted, all parts of the oxygen supply system are installed at different positions of the vehicle, the space occupied by the oxygen generation system can be reduced, and meanwhile directional oxygen supply to specific seats is achieved through the distributed oxygen outlets.

An integrated hydrogen-electric engine includes, an air compressor system; a hydrogen fuel source; a fuel cell; an elongated shaft connected to the air compressor system or a propulsor; and a motor assembly disposed in electrical communication with the fuel cell, wherein the air compressor system includes a plurality of electrically driven compressors configured to run in series or parallel.

Assemblies, apparatuses, and methods to extract or convey a material from a source of the material may include a vacuum generation and sound attenuation assembly. The vacuum generation and sound attenuation assembly may include a compressor housing having a vacuum source, a cooling system, and a vacuum controller. The vacuum source may include a compressor powered by an electric motor and positioned to cause a vacuum flow between the source of the material, and the vacuum generation and sound attenuation assembly. The vacuum generation and sound attenuation assembly may further include a sound attenuation chamber positioned to receive at least a portion of the vacuum flow from, and attenuate sound generated by, the vacuum source. The compressor housing and sound attenuation chamber of the vacuum generation and sound attenuation assembly may be arranged compactly on a common chassis for ease of transport and deployment.

The invention relates to a magnetic suspension air separation expansion machine system and an operation method thereof. The system comprises a base, a magnetic suspension bearing assembly, a rotor assembly and a control module. The magnetic suspension bearing assembly is arranged on the base, comprises an expansion end radial magnetic suspension bearing, a brake end radial magnetic suspension bearing and an axial thrust magnetic suspension bearing, and is used for supporting radial and axial forces of the rotor assembly; the rotor assembly is arranged in the machine body and connected with the machine body through the magnetic suspension bearing assembly, and non-contact suspension is achieved. The control module is electrically connected with the magnetic suspension bearing assembly and used for controlling the magnetic suspension bearing assembly to suspend the rotor. The operation method comprises the steps of self-checking before starting, electrifying to suspend the rotor, starting the air compressor to pressurize, adjusting flow to drive the expansion impeller, carrying out split-flow cooling on gas, and collecting data in real time to maintain stable suspension of the rotor. The device has the advantages of high efficiency, stability, energy conservation and the like and is suitable for the field of air separation.

The invention belongs to the technical field of energy storage, and relates to a compressed air and water coupled multi-energy co-production energy storage system and method, comprising an air compressor, a first heat exchanger, a second heat exchanger, an air turbine and a cooling module; a circulating medium of the first circulating loop is fused salt; the circulating medium of the second circulating loop is water, and the second circulating loop comprises a low-temperature water tank, a first booster pump, a second heat exchanger, a high-temperature water tank, a flash tank, a third heat exchanger, a steam turbine high-pressure cylinder and a heating module which are sequentially communicated end to end. Compression heat generated by compressed air can be stored in fused salt and water, special geographical conditions needed for storing high-pressure air are avoided, meanwhile, in the energy release stage, the stored water is used for flash evaporation and fused salt heating and then enters a steam Rankine cycle to do work, combined heat and power generation can be achieved in winter, combined cooling and power generation can be achieved in summer, and the energy-saving effect is achieved. Therefore, combined supply of different kinds of energy in different seasons is achieved, and the energy supply efficiency is improved.

The invention relates to an environment-friendly container type digital energy air compression station based on air floating shaft driving, and belongs to the technical field of digital energy air compression stations. A ventilation and heat dissipation module of an air treatment system is designed to comprise an airflow distribution modeling unit, an adjustable fan array, a distributed sensor network and an intelligent control unit; the airflow distribution modeling unit generates an initial airflow distribution diagram in a container body through computational fluid mechanics simulation based on container structure parameters, arranges an adjustable fan array in an airflow distribution non-uniform area in the container body according to the airflow distribution diagram, deploys a vector fan in a dead area, feeds back data in real time in combination with a sensor network, and establishes an airflow distribution model in the container body. A nonlinear relation function of the temperature, the airflow speed and the fan rotating speed is output and established, an airflow dead zone is eliminated, a temperature field in the box is balanced, and the problem that in the prior art, when an axial flow fan and a flow guide plate are arranged for ventilation and heat dissipation, system heat dissipation is insufficient is solved.

The invention relates to the technical field of state monitoring, in particular to an energy-saving air compressor running state monitoring system and method, and the system comprises a pressure slope analysis module, a deviation trend judgment module, an error fitting acquisition module, an error trend analysis module and an alarm triggering module. According to the method, a pressure range is calculated by dividing time slices, a slope ratio is generated, a dynamic threshold algorithm is combined to successively compare and capture an offset trend, frequency and current waveform data are synchronously acquired to match a timestamp to calculate a difference value, an error vector is generated in a standardized manner, and a periodic mean value is extracted to verify an incremental relationship to identify progressive anomaly. The cross validation slope ratio and error vector positioning wear or coordination abnormity, the dynamic threshold value is adjusted along with the mean value coefficient at the initial period to reduce fluctuation interference, the timestamp matching enhances the composite fault detection capability, and the range division operation quantifies the matching degree to provide an energy efficiency optimization basis. And a standardized error vector and mean value incremental mechanism improves the prediction precision and reduces false alarm and missing alarm.

The utility model discloses a superfine powder jet mill which comprises a base, a crushing tank, a crushing component and a feeding component, and the crushing tank is mounted above the base. The crushing assembly is arranged, under the arrangement of a blocking net, materials are prevented from falling to the position close to a supersonic nozzle, crushing of the supersonic nozzle is affected, or the supersonic nozzle is blocked, the height of a cleaning brush and the height of an air nozzle can be adjusted through an electric push rod, and under the arrangement of the cleaning brush and the air nozzle, a grading rotor can be cleaned; under the arrangement of an air compressor and a supersonic nozzle, the materials entering the crushing tank are accelerated to impact and collide with crushing teeth repeatedly, superfine crushing of the materials is achieved, a first motor is used for driving a worm to rotate, the worm rotates to drive a worm gear to rotate, the worm gear rotates to drive a vertical rod to rotate, and the vertical rod rotates to drive the grading rotor to rotate; and the vertical rod rotates to drive the crushing wheel to rotate, and under the cooperation of crushing teeth, the materials are efficiently and fully crushed, so that the overall processing efficiency is improved.

The invention provides an oilless air compressor bearing temperature alarm method and device, and the method comprises the steps: collecting the temperature rise rate, heat flow density and environment parameter data under different working conditions through a miniature thermocouple array in a sealing structure, and obtaining a heat flow characteristic data set; calculating temperature gradient change between adjacent measuring points based on the data set, analyzing a heat accumulation trend by combining geometric characteristics of the sealing structure and thermal physical parameters of a material, and generating a three-dimensional hot spot distribution diagram and heat accumulation rate data; according to the hot spot distribution diagram and the heat accumulation rate data, a sealing structure and bearing system heat path network model is constructed, and the monitoring point position of the potential heat abnormal area of the bearing is determined; and according to the monitoring point arrangement scheme, a temperature monitoring device is installed in a potential heat abnormal area of the bearing, temperature data are collected, heat distribution change characteristics are extracted, and an abnormal heat flow mode in the sealing structure is recognized. The method can accurately capture a bearing local heat abnormal area caused by a sealing structure, and prevents early failure of the bearing caused by a hot spot blind area.

The invention discloses a dynamic target pressure generation method based on multi-source pressure characteristics and an energy efficiency curve, and belongs to the technical field of energy-saving control of industrial air compression systems. According to the method, the minimum value, the maximum value, the average value and the mode of tail end pressure are counted, the dynamic range of gas consumption requirements is accurately represented, and the dynamic target pressure is generated. The air compressor energy efficiency curve library is set, the optimal continuous pressure interval is screened instead of single-point optimization, the operation stability and the energy efficiency advantage are considered, finally, the target pressure of each air compressor is distributed through the multi-target optimization algorithm, energy consumption waste caused by pressure overlapping is avoided, and the energy efficiency of the air compressor is improved. The global energy efficiency is optimal, and the problems that due to frequent loading and unloading of the air compressors of an existing air compression station, energy consumption is wasted, the tail end air pressure requirement is lower than the air compressor set value, and the redundancy of the pressure set value is too high when the multiple air compressors operate in parallel are solved.

The invention discloses a high-pressure type oil-free scroll compressor which comprises a compressor body composed of a first static scroll plate, a first dynamic scroll plate, a second dynamic scroll plate and a second static scroll plate, and the back face of the first dynamic scroll plate and the back face of the second dynamic scroll plate are arranged in an attached mode. The front face of the first orbiting scroll and the front face of the second orbiting scroll are meshed with the back face of the first static scroll and the back face of the second static scroll respectively, and the orbiting scrolls and the static scrolls are matched with each other to form a compression cavity. The scroll compressor further comprises four eccentric pins, an eccentric pin eccentric section is integrally formed in the middle of the outer surface of each eccentric pin, and the eccentric pin eccentric sections of the four eccentric pins are rotationally connected to the four corners of the first movable scroll plate and the four corners of the second movable scroll plate through movable plate bearings respectively. The oil-free scroll air compressor solves the problem that the exhaust pressure of the oil-free scroll air compressor is difficult to reach 1.2 Mpa or above.

The invention provides a zinc-nickel air flow battery energy storage cabinet. The zinc-nickel-air flow battery energy storage cabinet comprises a control module and a plurality of flow battery energy storage modules, each flow battery energy storage module comprises a plurality of supporting layers, an electrolyte storage tank is fixed to a first supporting layer in the supporting layers in a limited mode, a galvanic pile is fixed to a second supporting layer above the first supporting layer in a limited mode, and the control module is connected with the control module. The third supporting layer above the second supporting layer is used for installing an electric pile sensor module, a liquid delivery pump is arranged below the first supporting layer, and the electric pile, the liquid delivery pump and the electrolyte storage tank are connected through a liquid circulation pipeline; the control module comprises an air compressor; the air compressor is connected with the electric pile through an air pipeline; the control module further comprises an electric energy management system, and the electric energy management system comprises an energy management system, a power conversion system and a battery management module. According to the embodiment provided by the invention, the integration of the zinc-nickel air flow battery is realized.

The invention provides a self-adaptive volume-variable intelligent air compressor and a control system thereof, and relates to the technical field of air compressors. The control system comprises a multi-source heterogeneous sensing fusion module, a dynamic manifold optimization control module, a quantum probability fault pre-judgment module, a cross-domain energy cooperative scheduling module, a distributed self-organizing communication module and a self-adaptive meta-evolution module. And a grey Markov chain model and a wavelet decomposition-support vector machine are combined to accurately predict the real-time electricity price and the factory gas consumption load. And according to the predictions, the operation mode of the air compressor is intelligently adjusted through dynamic game equilibrium and a multi-target differential evolution algorithm. In the low ebb period of the electricity price, the operation load is automatically increased by 30%-40%, and meanwhile the gas storage capacity is increased by 25%-35%; and in the peak period of electricity price, stored gas is reasonably released, and the operation load is reduced by 20%-30%. And energy waste caused by unreasonable operation is effectively avoided.

An air supply apparatus for an aerial mobility vehicle, which is provided with a fuel cell stack, includes an air compressor mounted in the aerial mobility vehicle and configured to compress air introduced into the aerial mobility vehicle, and an air chamber provided in the aerial mobility vehicle and connecting the air compressor and the fuel cell stack, wherein the air chamber defines a conditioning space for conditioning the air having passed through the air compressor, obtaining an advantageous effect of ensuring a stable supply of air to the fuel cell stack and improving operational stability and reliability.

The invention relates to the technical field of data processing, in particular to a screw air compressor state monitoring method, which comprises the following steps: acquiring monitoring data of M dimensions of each monitoring object in an air compressor to obtain multi-dimensional real-time monitoring data and multi-dimensional historical monitoring data; screening suspected real multi-dimensional monitoring data from the multi-dimensional historical monitoring data, and obtaining the data real degree of each dimension in the suspected real multi-dimensional monitoring data; n reference dimensions of any monitoring object at the current moment are obtained, and the data authenticity of each reference dimension in each piece of suspected real multi-dimensional monitoring data and the difference between each piece of suspected real multi-dimensional monitoring data and the multi-dimensional real-time monitoring data are calculated according to the data authenticity of each reference dimension in each piece of suspected real multi-dimensional monitoring data. Obtaining the state voting weight of each piece of suspected real multi-dimensional monitoring data for any monitoring object; and according to the state voting weight of each piece of suspected real multi-dimensional monitoring data, a state evaluation result of any monitoring object at the current moment is obtained by utilizing a KNN algorithm, and the running state of the current air compressor is accurately obtained.

The invention belongs to the technical field of air compressor filters, and particularly relates to a compressor front air inlet oil removal filter which comprises a shell, an upper end cover and a lower end cover are fixedly connected to the upper end and the lower end of the shell respectively, a through medium inlet and a through medium outlet are formed in the two sides of the shell respectively, and a partition plate is fixedly connected into the shell; two pressing rings are rotationally connected to the partition plate, an impeller is fixedly connected to the bottom of each pressing ring, a filter cylinder is fixedly connected into each impeller, the impellers rotate at a high speed to conduct centrifugation so that airflow can flow outwards from the interiors of the filter cylinders, a driving part is arranged below each impeller, and an oil removing and cleaning part is arranged in each filter cylinder. A back flushing cleaning mode that the impeller and the filter cartridge synchronously rotate at a high speed is adopted, the static pressure is higher, the effect is more continuous, meanwhile, air pressure distribution at all positions in the filter cartridge is more uniform, airflow can fully penetrate through the filter cartridge, and more comprehensive and efficient back flushing cleaning work is achieved.

The invention discloses an intelligent energy-saving control method for an air compression system based on data driving. The intelligent energy-saving control method is suitable for an industrial air compression station with multiple parallel units. According to the method, operating parameters of all air compressors and pressure and flow data of a header pipe are collected in real time, an energy efficiency model of the output flow and the gas-electricity ratio of all frequency converters is established, and the future flow requirement of the header pipe is dynamically predicted in combination with the characteristics of a fixed frequency machine. Based on a prediction result and a single-machine energy efficiency model, the basic load and the fluctuation load are distributed according to the overall energy efficiency optimization principle of the system, the start-stop sequence and the rotating speed of each unit are determined, and stable main pipe pressure, continuous gas supply, rapid response and good operation flexibility are achieved. By means of the method, the energy efficiency difference of all the units can be fully utilized, energy consumption fluctuation and equipment abrasion caused by frequent starting and stopping are reduced, and the overall energy utilization rate of the air compression system and the reliability of the industrial production process are improved.

The invention discloses an air compressor fault monitoring and operation and maintenance management method and system based on the AI technology, and relates to the technical field of air compressors, the key points of the scheme are as follows: based on the operation data of an air compressor, reading pressure and temperature data to carry out data standardization processing, and utilizing a CNN network to carry out feature extraction on the pressure and temperature data; and identifying an abnormal fluctuation mode of sudden increase or sudden decrease, and monitoring the change trend of the abnormal mode to obtain abnormal mode tracking information. The method is used for carrying out continuous monitoring and feature extraction on pressure and temperature data, an abnormal fluctuation mode can be effectively recognized, the development trend of the abnormal fluctuation mode can be predicted, short-term data abnormity can be captured through application of the long-term and short-term memory network, long-term data changes can be analyzed, prediction of potential risks in the future is provided, and the method is suitable for popularization and application. The accuracy and timeliness of fault prediction are improved, so that a maintenance team can carry out intervention before the problem develops to a serious fault, and the downtime caused by the fault is remarkably reduced.

The invention relates to the technical field of air compression stations, in particular to an air bearing-based digital energy air compression station with a noise reduction effect, which comprises a machine room, and a screw air compressor, a cooler, an oil-water separator, a first air storage tank, a dryer, a filter and a second air storage tank which are sequentially connected are arranged in the machine room. The first air storage tank is further connected with a first air supply pipe, and the second air storage tank is further connected with a second air supply pipe. An air bearing is selected for the screw type air compressor, a third air supply pipe is arranged between the filter and the second air storage tank, the third air supply pipe is connected with the air bearing, and part of compressed air is continuously input into an air path of the air bearing through the third air supply pipe. According to the screw type air compressor, mechanical friction can be avoided through the air bearing, so that noise is further reduced, the noise reduction effect of the digital energy air compressor is improved, and compressed air can be conveniently obtained by the air bearing in an air compression station.

The invention relates to an oil-proof emulsification control method and system for an air compressor of an urban rail vehicle, and solves the problem of lubricating oil emulsification caused by insufficient working efficiency of the air compressor and environment humidity change by constructing a comprehensive control system comprising the compressor, an oil-proof emulsification device and a controller. The system monitors lubricating oil temperature and environment humidity in real time through a temperature sensor and a humidity sensor; the working rate of the air compressor is accurately calculated, wherein the vehicle operation time t2 takes the operation speed larger than 10 km / h as the calculation starting point; the controller comprehensively judges whether the oil-proof emulsification device is started or not based on temperature, humidity and working rate data, and the control strategy comprises the step of dynamically adjusting the working rate judgment node according to the real-time temperature and humidity. According to the method, accurate and reliable control over the oil-proof emulsification process is achieved, emulsification of the lubricating oil is effectively prevented, and energy consumption and maintenance cost can be reduced.

The invention relates to the technical field of air compressor optimization, and discloses an intelligent air compressor optimization control method and system based on an IOE control system.The intelligent air compressor optimization control system comprises a collection unit, a preprocessing unit, an analysis unit and an optimization unit, rich operation data such as air inlet pressure, flow, exhaust pressure and temperature are comprehensively collected in real time, denoising is conducted through a moving average method, and data accuracy is ensured; normal, overload and abnormal fluctuation modes are clearly divided, accurate judgment is performed by means of a preset interval and reasonable calculation, and powerful support is provided for control decision making; in a normal mode, intelligent regulation is performed according to a parameter average value, and operation efficiency and stability are improved; the valve opening degree is adjusted according to the deviation degree in the overload mode, the load is effectively reduced, and equipment is protected; in the abnormal fluctuation mode, a PID control algorithm is used, the opening degree, the cooling amount and the like of the valve are flexibly adjusted according to deviation and the change rate, and parameters are rapidly recovered to be stable. The fault rate and the maintenance cost can be greatly reduced, and the working efficiency is remarkably improved.