54results about How to "Enable predictive maintenance" patented technology

The invention provides an intelligent medical robot for lower limb rehabilitation therapy. A movement suspension system is mounted above a movement transmission system through a stand column; weight reduction and position holding of a patient subjected to lower limb rehabilitation therapy are implemented by the suspension system; the patient uprightly walks on the movement transmission system under the driving effect of a lower limb exoskeleton robot system; the lower limb exoskeleton robot system is connected with a rotating system by a gravity center self-balancing system; the height of the lower limb exoskeleton robot system is adjusted according to change of center of gravity of the patient during walking of the patient; and a sensor system acquires equipment parameters and human body parameters, and transmits the equipment parameters and the human body parameters to an embedded control system to be displayed. The intelligent medical robot for lower limb rehabilitation therapy is high in control precision and high in intelligence, and can be used for rehabilitation training of upright walking of the patient.

The invention discloses a train storage battery pack fault early warning system and method. The system comprises a storage battery pack, a charger, a train control and management system, a data processing unit and a fault early warning unit. The charger uploads the real-time state data of the storage battery pack to the train control and management system. The train control and management system sends the real-time network data to the data processing unit. The data processing unit analyzes the received real-time network data and extracts real-time state data of the storage battery pack from the real-time network data. The fault early warning unit analyzes the extracted real-time state data of the storage battery pack to obtain the state of the storage battery pack and outputs fault early warning prompt information. The technical problems that an existing train storage battery pack fault early warning method is insufficient in coverage, early fault symptoms are difficult to find, and early fault early warning cannot be achieved can be solved.

The invention relates to the technical field of energy, and provides an equipment predictive maintenance method and device, and the method comprises the steps: obtaining a knowledge graph according tothe effective information of target equipment needing predictive maintenance, wherein the effective information at least comprise historical data, expert experience and an operation record; processing the knowledge graph to obtain a fault tree corresponding to the knowledge graph; processing the fault tree by adopting a Monte Carlo model to obtain a function relationship between the fault of thetarget equipment and the fault reason; and performing preset simulation operation on the function relationship to obtain the fault probability of the target equipment. According to the method, the knowledge graph is acquired by taking the effective information of the equipment as basic data, modeling is performed on the basis of the knowledge graph, and the simulated fault probability is obtainedin cooperation with large-scale simulation prediction; predictive maintenance of the equipment is realized, the downtime is reduced, the production loss caused by equipment faults is effectively avoided, and the method is accurate in prediction and simple and reliable in operation processing.

The invention provides an IGBT junction temperature estimation system and method based on the temperature of an emitter power terminal. The system comprises a temperature measurement element, a temperature collection circuit, an IGBT gate driver, and a main controller, wherein the temperature measurement element is used for measuring the temperature of an IGBT power emitter terminal, the IGBT gatedriver comprises a gate drive and protection circuit and a temperature acquisition circuit, the gate drive and protection circuit is mainly used for on-off control and short circuit protection of a power device IGBT, the temperature acquisition circuit is used for acquiring a temperature signal, outputted by the temperature measurement element, on the IGBT power emitter terminal, and the main controller is used for sending a gate trigger signal to the IGBT gate driver, receiving a temperature signal on the IGBT power emitter terminal transmitted by the IGBT gate driver, and estimating the IGBT junction temperature according to the temperature signal on the IGBT power emitter terminal. According to the system, the over-temperature protection of the IGBT is realized, and IGBT temperature estimation is realized on the controller side.

The embodiment of the invention aims to provide an oil well pump inspection period prediction method and device and a storage medium, and the method comprises the steps: obtaining a sample data set influencing an oil well pump inspection period, the sample data set comprising a plurality of index data influencing the oil well pump inspection period; determining a plurality of principal component data influencing the oil well pump inspection period from a plurality of index data of the sample data set through principal component analysis; performing regression analysis on the plurality of principal component data to obtain a regression analysis result; and predicting the pump inspection period of the oil well according to a regression analysis result.

An integrated automatic control hydraulic servomotor comprises an oil tank, an oil supply pipeline and an oil return pipeline, the oil supply pipeline passing a supply oil filter and the oil return pipeline passing a return oil filter are connected with the oil tank. The oil supply pipeline is connected with an energy accumulator and a hydraulic oil cylinder through an oil pump. The oil pump is connected with a motor used for driving the oil pump to act. A temperature sensor used for collecting the oil temperature and a heating control device used for temperature rising of oil liquid are arranged on the oil tank. A cylinder block of the hydraulic oil cylinder is connected with a pressure sensor used for collecting pressure in the hydraulic oil cylinder. A position sensor is connected to an output shaft of the hydraulic oil cylinder. The temperature sensor, the pressure sensor and the position sensor are all connected with a center controller. The signal output end of the center controller is connected with a motor driver and an electro-hydraulic change-over valve. The motor driver is connected with the motor in a control manner. The electro-hydraulic change-over valve is connected with the hydraulic oil cylinder in a control manner. By the adoption of the integrated automatic control hydraulic servomotor, site construction and maintenance are convenient, integrated integral mounting is conducted according to the mounting position, the requirement on site construction and maintenance is lowered, the fault probability is reduced, and automatic control can be achieved.

The invention aims to disclose a method for intelligently monitoring an abnormal operation state of equipment. Compared with the prior art, comprehensive perception of equipment is realized in a non-contact manner; equipment state information is obtained by monitoring sound generated during equipment operation, and the defects of traditional sensing data can be effectively overcome; and meanwhile, fault monitoring diagnosis and predictive maintenance of the equipment and full-life-cycle management of the industrial equipment are realized based on big data and deep learning, so that the aim of the invention is fulfilled.

The invention provides an intelligent programmable axial plunger pump controller and a control method, and belongs to the field of intelligent hydraulic elements. The controller comprises a sensor group, a control panel, and an ARM chip, an RISC-V chip, a signal acquisition module, a filtering module, a charge amplifier, a power amplifier, a mode selection module, a display module and a cloud storage module which are installed on the control panel; and the control panel is further provided with a plurality of external interfaces. The magnitude of flow in a variable piston cavity of a hydraulic pump and the swing angle of a swash plate can be controlled by the ARM chip; automatic compound control over the pressure, flow and power of the axial plunger pump is achieved; meanwhile, the pressure, flow and power of the axial plunger pump can be controlled by detecting signals such as the outlet pressure of the hydraulic pump, the swing angle of the swash plate and vibration on a pump shell; the state of the hydraulic pump is monitored and recognized based on the RISC-V chip, common faults of the hydraulic pump are judged, and judgment results are transmitted to the ARM chip, thus implementing predictive maintenance of the hydraulic pump.

The invention discloses a digital power station delivery method based on BIM and GIS, a medium and equipment. The method comprises the following steps: determining a digital delivery rule; determining a digital deliverable delivery requirement; determining a digital delivery scheme to establish a BIM, a GIS model or a BIMGIS model of a digital deliverable in the power station; digital delivery information association: determining that a digital deliverable in the power station needs to deliver associated information and associates the associated information to the deliverable; taking the BIM, the GIS model or the BIMGIS model of the deliverable associated with the completed information and the information associated with the deliverable as delivery objects for delivery; and after the delivery is completed, checking the detailed information of the deliverable through the local window. According to the method, paperless digital interaction can be achieved, the technical problems that traditional delivery is low in efficiency and accuracy are solved, a source is provided for digitization of a power station through three-dimensional design, source-to-end direct connection of information is achieved, and it is fundamentally ensured that the information is correct.

The invention discloses a blockchain-based intelligent management method for a power supply system, and the method comprises the following steps: S1, monitoring the operation state of the power supply system in real time through a monitoring terminal, storing effective data in a blockchain system through an intelligent contract, and triggering the abnormal alarm of the power supply system according to the effective data; s2, establishing a convolutional network, and evaluating and predicting the aging condition of the power supply system according to the video image information collected by the monitoring terminal; and S3, creating a deep learning network, and evaluating and predicting the intact state of the power supply system according to the monitoring parameters of the monitoring terminal and the aging condition evaluation value of the power supply system. The block chain technology is adopted, so that the monitoring accuracy of the system is greatly improved; an image processing technology is adopted, so that the aging degree of the system can be monitored, and predictive maintenance is realized; and the overall intact state of the power supply system can be evaluated and predicted, and predictive system maintenance is further guided.

The invention discloses a remote control method and system for a smart tea bag packaging machine, and the system comprises the smart tea bag packaging machine, an industrial remote gateway, an IP cloud camera, an industrial computer, a mobile phone, and a cloud server, wherein remote control terminals such as the industrial computer and mobile phone establish a network connection with the industrial remote gateway at the production site through the cloud server; the PLC controller and IP cloud camera in the smart packaging machine are connected to the industrial gateway. The remote control system is good in real-time control effect and simple in operation of the remote human-machine interface, and can remotely and centrally monitor the field equipment at any time and place through the remote control terminal, which can not only meet the needs of equipment suppliers for remote debugging, but also facilitate the user of the equipment to monitor the operating status and data of the equipment anywhere, thereby achieving remote monitoring and predictive maintenance of the performance of on-site equipment, and further reducing management costs and improving economic efficiency.

The embodiment of the invention discloses a server fault prediction method and device, equipment and a storage medium. The method comprises the following steps: acquiring working state data of a server, wherein the working state data comprises power consumption data and/or electromagnetic leakage data; and performing fault prediction on the server according to the working state data. By the adoption of the technical scheme, predictive maintenance of the server can be achieved, and the downtime rate of the server and the maintenance cost of the server are effectively reduced; and the data security of the server can be effectively guaranteed, and heavy loss is avoided. Moreover, since the server is subjected to fault prediction through the fault maintenance equipment, hardware of the serverdoes not need to be modified, and a software program does not need to be additionally installed on the server, occupation of internal resources of the server by fault prediction can be avoided, and the running speed of the server is increased.

The invention relates to a health state evaluation method, system and device for a numerical control machine tool and a medium, and the method comprises the steps: training model parameters of a health state analysis model based on each pre-obtained training sample data set, and calculating a corresponding likelihood probability, forming a preset model parameter set representing different preset working condition states of the machine tool by each group of trained model parameters, and forming a preset likelihood probability set by each likelihood probability; and based on the obtained preset model parameter set and the preset likelihood probability set, evaluating the health state of the current numerical control machine tool. According to the method, the historical monitoring data of the numerical control machine tool is utilized to construct the multi-dimensional data vector, the health state of the numerical control machine tool is evaluated by calculating the health degree, the number of machine tool sensors does not need to be increased, the model has wider practicability, the calculation complexity is reduced, and accurate, rapid and real-time health degree evaluation is achieved; the method can be widely applied to the numerical control machine tool health state monitoring field.

The invention discloses an electrode cap grinding cycle and energy consumption correlation analysis system which is characterized in that a feature information acquisition module is used for acquiring basic feature information of an electrode cap grinding cycle from welding spot main file data corresponding to a welding spot needing to be analyzed and a corresponding welding spot index file through big data analysis software; derivative characteristic information of energy consumption calculation of the electrode cap grinding cycle of each welding spot needing to be analyzed in the three stages is calculated by utilizing the basic characteristic information of the electrode cap grinding cycle; and the energy consumption calculation module obtains the electrode cap grinding cycle energy consumption of each welding spot in the three stages through a self-adaptive Simpson algorithm by utilizing the basic characteristic information of the electrode cap grinding cycle of each welding spot in the three stages and derivative characteristic information calculated by the energy consumption of the electrode cap grinding cycle. According to the method, through a big data mathematical statistical analysis means, mathematical statistical analysis is carried out on mass welding spot data, and the electrode cap grinding cycle energy consumption of the welding robot in the welding spot welding process is accurately calculated.

The invention discloses a complex transmission chain predictive maintenance system based on the industrial Internet of Things. Hardware equipment of the system comprises a high-frequency sensor network, a high-frequency signal processing device and intelligent hardware, wherein the high-frequency sensor network is used for collecting working signals in real time or periodically, the high-frequencysignal processing device is used for preprocessing the working signals, and the intelligent hardware carries out data processing at a local end by utilizing a wavelet characteristic extraction algorithm and uploads the data to a cloud end; and the cloud is used for verifying and storing the data, analyzing the data by utilizing an intelligent algorithm with feature extraction, and finally outputting a corresponding fault feature state. An exclusive data processing device is adopted for related high-frequency signals, processing capacity required by a core processing unit is dispersed, and thestability of the system is improved; aiming at the characteristics of complex vibration signals, high-frequency data dimension reduction is completed on the edge side through feature extraction, an equipment state degradation analysis side cloud data architecture is constructed through cooperative use of a cloud algorithm, and predictive maintenance of a complex transmission chain mechanism is realized.

The invention discloses a welding spot standard curve band fitting system and method. A data preprocessing module of the system is used for conducting data preprocessing on welding spot basic data formed by a welding robot within a preset time period; the pre-fitting processing module is used for filtering and removing the abnormal resistance data through big data analysis software to obtain pre-fitting data; the fitting processing module is used for carrying out linear iteration fitting by adopting linear regression based on a decision tree to obtain an upper boundary and a lower boundary of a welding spot resistance curve of a welding spot needing to be analyzed in a welding time period; and the fitting post-processing module is used for performing curve denoising on the upper boundary and the lower boundary of the welding spot resistance curve to obtain the corresponding upper boundary and the lower boundary of the welding spot resistance standard curve. Through a big data mathematical statistical analysis means, the function relationship between the welding spot resistance and the time of the welding object and the welded object under the condition that disturbance factors are eliminated is reflected, and the method has important value for further research on the welding spot process.

The invention provides a power environment monitoring system for a hyperscale data center to achieve real-time reliable monitoring of the running state of the hyperscale data center. The system comprises a data access module, a data buffer module, a data calculation module, a data storage module, an enterprise service bus module and a data analysis module, wherein the data buffer module is used for adding data collected by the data access module into a maintained data queue and reading monitoring data from the queue to submit the monitoring data to the data calculation module; the data calculation module is used for judging whether alarm information needs to be generated or not according to the obtained monitoring data; the data storage module is used for storing configuration data, real-time and historical monitoring data and alarm data; the data storage module stores the data by adopting different database combination modes; and the data analysis module is used for obtaining corresponding data from the data storage module through the enterprise service bus module to respond to a service request after receiving the service request.

The invention relates to a power transformation equipment intelligent analysis linkage system and method based on multi-source information fusion. Data of a plurality of systems are subjected to fusion analysis, panoramic data of the transformer substation can be obtained, and one-sidedness of equipment state diagnosis is avoided; when the in-station equipment is abnormal, the state data or the alarm signal of the equipment can trigger a configured video or robot linkage event, the linked camera or robot inspects an abnormal point, records a fault condition in detail, and automatically pushes a video picture and an inspection result of a corresponding area; and meanwhile, the equipment state comprehensive diagnosis module pushes an equipment state diagnosis result and an intelligent decision-making conclusion, comprehensive and multi-dimensional inspection data and intelligent auxiliary decision-making are provided for operation and maintenance personnel of the transformer substation, predictive maintenance is realized to a certain extent, the first-aid repair autonomy and the beforehand control capability of the transformer substation can be effectively improved, and the operation efficiency and security of substation operation and maintenance are improved.