21 results about "Periodic maintenance" patented technology

The invention relates to the technical field of vehicle maintenance, and provides a vehicle part abrasion determination method and a vehicle. The method comprises the steps that vehicle driving data and external environment data in the current time period are obtained, and the abrasion influence coefficient of the external environment on a part is determined through the external environment data; the current wear value of the component is determined according to the wear influence coefficient and the vehicle running data, component loss prediction in combination with the external environment and the vehicle running state is achieved, the predicted component loss better conforms to the actual use condition, more accurate maintenance prompt can be achieved on the basis of the predicted component loss, the risk of misjudgment of maintenance is avoided, and the maintenance efficiency is improved. The purpose of on-demand adaptive maintenance is achieved, the problems of excessive maintenance and insufficient maintenance caused by periodic maintenance are solved, the maintenance cost and the failure rate of vehicle parts are reduced, and the active maintenance willingness of a user can be improved while the driving safety of the vehicle is guaranteed.

The invention relates to the field of operation and maintenance management of Internet of Things equipment, in particular to a periodic maintenance task automatic scheduling system and method, and the method comprises the steps: firstly collecting vibration, current and environment signals, and constructing a three-dimensional digital twin map; obtaining a health index and a remaining available life in a preset window through time sequence alignment convolution and an ordinary differential equation recursive network, generating a risk coefficient through index mapping, and driving a reaction-diffusion equation to form a continuous degradation field; the scheduling optimization unit obtains an initial solution through symplectic gradient Hamiltonian-Monte Carlo sampling, and maintenance scheduling data meeting resource constraints are generated through bose sampling, topological bose evolution and dual gaming; the mixed reality terminal displays a risk hotspot and an operation instruction on site, retest data is recorded after maintenance is completed, and reinforcement learning updates a risk assessment and scheduling model on line according to an execution result to form a closed loop; according to the method, accurate quantification of risks, global optimization of scheduling and adaptive feedback of execution are realized, and the fault rate and the maintenance cost can be remarkably reduced.

The invention provides an intelligent rail smoothness adjusting method and system based on dynamic detection and big data analysis, and belongs to the technical field of railway engineering maintenance. The method comprises the steps that track geometric data, vehicle dynamics response data and environment data are collected in real time through a multi-dimensional sensor array installed on an operating train; uploading the data to a cloud big data platform for fusion and deep mining, constructing a track state degradation prediction model by using a machine learning algorithm, and accurately predicting the development trend of track irregularity; and based on the prediction result, automatically generating a targeted track fine adjustment scheme, and issuing the scheme to an intelligent maintenance robot deployed on the line or guiding a manual maintenance team. The system comprises a data acquisition layer, a cloud analysis layer and an intelligent execution layer. According to the method, the change of the track state from'regular maintenance 'to'predictive maintenance' is realized, the maintenance efficiency, the precision and the line smoothness are remarkably improved, and the maintenance cost of the whole life cycle is reduced.

The invention discloses an automatic generation method and system for a periodic maintenance work order of equipment, and relates to the technical field of automatic work order management control. The automatic generation method and system for the periodic maintenance work order of the equipment comprises the following steps: S1, constructing a periodic task basic data set; s2, based on the periodic task basic data set, identifying a periodic adaptability relationship between an equipment state and a plan configuration parameter; s3, according to the corresponding relation between the equipment operation behavior and the planned cycle configuration, judging a cycle adjustment requirement; s4, evaluating a task response offset condition in combination with a work order execution progress and an operation stability state; and S5, generating information content with node attributes, pushing the information content to the configuration object, and recording a corresponding sending state. The problem that the work order process data is not utilized due to the fact that a current system evaluates the execution state only after the work order is executed and does not recognize the running trend change of the execution process is solved.

The invention provides a comprehensive scheduling method for regular maintenance of flexible equipment. The method comprises the following steps of: firstly, adopting a strategy scheduling process of layer priority, short time and equipment priority; secondly, on the basis of a forest fire fighting model, according to the relation between the number of processed procedures and the number of unprocessed procedures and loss generated by the equipment in a maintenance period, the maintenance starting point and duration of the flexible equipment are determined; and finally, a dynamic adjustment strategy is adopted, so that maintenance of flexible equipment is realized, and the total time for product processing is reduced.

The invention relates to the technical field of intelligent traffic and vehicle engineering, in particular to a vehicle maintenance method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the fault prediction of a vehicle based on a preset fault prediction model according to a preliminary anomaly detection result, and recognizing the fault maintenance priority of the vehicle according to a fault prediction result, the method comprises the steps of adjusting a fault maintenance priority by using a reinforcement learning algorithm according to a vehicle driving road condition, user driving data and a vehicle part inventory state, generating a dynamic maintenance strategy of a vehicle according to the adjusted fault maintenance priority, obtaining a recommended maintenance network point of the vehicle based on the dynamic maintenance strategy, and maintaining the vehicle according to the recommended maintenance network point. Therefore, the problems of regular maintenance limitation, fault diagnosis hysteresis quality, unreasonable resource allocation and the like in the related technology are solved, and through multi-source data fusion, a deep learning model and a dynamic maintenance strategy, the maintenance efficiency and the fault prediction accuracy are improved, and resource allocation is optimized.

The invention provides a high-voltage isolation switch contact state intelligent verification and maintenance auxiliary method. Multi-dimensional parameters such as contact temperature, contact resistance, micro displacement and environment temperature and humidity are collected in real time through the integrated sensing module, after data preprocessing and spatial-temporal feature extraction are conducted through the edge calculation unit, the parameters are input into a CNN-LSTM fusion AI model for intelligent diagnosis, the degradation state of the contact is accurately recognized, and the accuracy of the contact degradation is improved. And secondary verification is implemented in combination with historical trends and environmental factors, so that false alarms and missing alarms are effectively reduced. The system supports local quick response and cloud linkage early warning, and realizes conversion from'periodic maintenance 'to'state maintenance'. In actual measurement of a 220kV transformer substation, the state recognition accuracy rate reaches 98.5%, the maintenance frequency is reduced by 40%, and the operation and maintenance safety and economy are remarkably improved.

The utility model discloses a point inspection maintenance device of an automatic guided vehicle, which relates to the technical field of maintenance equipment.The point inspection maintenance device of the automatic guided vehicle comprises a rack, a lifting mechanism, a rotating mechanism, a first driving component and a second driving component, the lifting mechanism comprises a screw rod, and the rotating mechanism comprises a support and a clamping component. The clamping assembly comprises a first bottom supporting block, a second bottom supporting block and an arc-shaped guide rail, the second driving assembly can drive the arc-shaped guide rail to rotate relative to the support, and the first driving assembly can drive the arc-shaped guide rail to move in the vertical direction relative to the rack through the screw and the support. According to the technical scheme, the lifting mechanism and the rotating mechanism are adopted to achieve lifting and overturning of the automatic guided vehicle, so that the operation of carrying or regular maintenance of the automatic guided vehicle in a fault state becomes simpler, the operation difficulty is reduced, the risk that the automatic guided vehicle is overturned and injured in the overhaul operation is eliminated, and the service life of the automatic guided vehicle is prolonged. And the safety of operators is ensured.

The invention relates to the technical field of automobile exhaust detection, in particular to an automobile exhaust detection method and system based on a gas sensor array, and aims to analyze the damage of a sensor in the operation process according to the environment of the sensor and the change of detection data in the detection process. According to the method, the damage of the gas sensor is comprehensively evaluated, and the maintenance period is finally determined according to the damage comprehensive evaluation result of the gas sensor, so that the reliability and stability of the gas sensor can be remarkably improved, the maintenance period is dynamically adjusted according to actual abnormal conditions, and the sensor can be always in a good running state; the safety accident risk caused by sensor faults is reduced, the maintenance cost can be effectively reduced, the high cost caused by blind regular maintenance is avoided through accurate arrangement of maintenance work, meanwhile, the production loss caused by shutdown due to sensor faults is reduced, and the win-win situation of economic benefits and equipment performance is achieved.

The application discloses a vehicle information database perfecting method for automatic oiling, which integrates real-time, quasi-real-time and periodic maintenance and updating. The periodic maintenance and updating is triggered and completed manually; the real-time updating is triggered by events including new vehicle model design or market release of a vehicle manufacturer, and includes real-time automatic updating, real-time semi-automatic updating and / or real-time manual updating; and the quasi-real-time updating is triggered by events that a vehicle not matching the database information appears at a gas station, and includes quasi-real-time automatic updating, quasi-real-time semi-automatic updating and / or quasi-real-time manual updating. The application solves the problems of untimely information acquisition and unobtainable part of vehicle model feature point information by means of protocol pushing, automatic searching, manual surveying and mapping and autonomous surveying and mapping, and changes the past situation that vehicle information database perfecting mainly relies on manual periodic maintenance and sometimes cannot be perfected, and is more complete, flexible and practical.

The invention provides an equipment joint maintenance optimization method and system based on a time window, which are applied to the technical field of equipment maintenance, and are used for establishing a joint maintenance time window under the same time scale by utilizing periodic and predictive maintenance time, identifying two types of equipment under the same time window, and carrying out unified maintenance. The method comprises the following specific steps: collecting operation state data of predictive equipment, and establishing a predictive maintenance plan; counting maintenance logs of periodic equipment, and establishing a periodic maintenance plan; constructing execution processes of two maintenance calculations under the same time scale; counting time intervals of predictive maintenance equipment and periodic maintenance equipment, and setting an average value of the time intervals as a threshold value of a combined maintenance time window; and judging whether the two maintenance calculations can be subjected to joint maintenance or not according to a threshold value. According to the equipment joint maintenance optimization method and system based on the time window, the downtime is reduced by unifying the equipment maintenance time, so that the production efficiency is improved.

The utility model relates to a double-layer pasting machine with an obvious refueling distinguishing mark, which belongs to the technical field of pasting machines and comprises a double-layer pasting machine body, and a control panel is arranged on the front side of the double-layer pasting machine body. According to the double-layer pasting and attaching machine with the obvious refueling distinguishing mark, reminding for setting regular maintenance is input into a control panel computer system, and after the input set regular maintenance time is up, a signal is sent to a controller through a control panel; the controller can turn on the first flashing lamp, the second flashing lamp and the third flashing lamp respectively, and the loudspeaker broadcasts which equipment in the structure of the double-layer pasting machine body needs to be maintained and refueled. The first flashing lamp, the second flashing lamp and the third flashing lamp respectively flicker to prompt equipment in different places that refueling maintenance needs to be carried out, so that when refueling maintenance needs to be carried out, maintenance personnel can conveniently observe, contrast and carry out refueling maintenance, and the situation of refueling maintenance errors is reduced.

A cleaning trolley is arranged on the outer side of the track body, sliding rods are slidably connected to the interior of the cleaning trolley at equal intervals, one end of each sliding rod is rotationally connected with an installation block, an installation frame is arranged on one side of each installation block, and a cleaning plate is slidably connected to the outer side of each installation frame. The outer side of the cleaning plate abuts against the interior of the track body, the mounting frame and the mounting block are mounted through bolts, the top and the bottom of the inner wall of the mounting frame are fixedly connected with shells, and fixing assemblies are arranged in the shells. According to the track sports car capable of cleaning the track, the fixing assembly is arranged, so that the cleaning plate can be quickly mounted and dismounted, time can be effectively saved when a maintainer replaces the cleaning plate, working efficiency is improved, and the track sports car is particularly important for equipment needing to be maintained regularly.

The present application mainly relates to the technical field of aircraft maintenance. In order to solve the problem of low prediction accuracy of hinge wear under the scene of small sample, variable working condition, individual difference and equipment degradation, the present application provides a kind of aircraft door hinge wear prediction method based on dynamic wear coefficient, the core is based on real wear coefficient as training label, constructs physical information embedding type neural network, the working condition parameters of hinge are as input, the real wear coefficient is as training label and is trained to obtain the wear coefficient prediction model, after training, the working condition parameters collected in real time are input into the model to obtain the dynamic wear coefficient that changes with the working condition in real time, and the wear depth is recursively calculated based on the dynamic wear coefficient using discrete integration method;At the same time, the measured wear depth obtained by periodic maintenance is used to realize the adaptive correction of the model by weighting fusion and scaling update of the output layer weight of the model, and the high-precision adaptive prediction result of the wear depth of the aircraft door hinge is obtained.

The utility model discloses an inspection unmanned aerial vehicle protection structure in a hearth, which comprises a main body unit comprising a rack main body, four ends of the rack main body are respectively and fixedly provided with four connecting frames, and the main body unit further comprises a flight assembly; the protection unit comprises protection parts and an adjusting part, and the protection parts are located at the four ends of the adjusting part; the supporting unit comprises a supporting part and a power part, the power part is located on the top face of the supporting part, the adjusting part comprises two bidirectional hydraulic cylinders A fixed to the upper end face and the lower end face of the rack body, and the protection part comprises a sliding rod fixedly connected to the inner wall of the connecting frame. The bidirectional hydraulic cylinder A and the bidirectional hydraulic cylinder B are matched to work, so that the protection frame can contract, extend and move, can extend out to quickly shield and protect the flying blades when routing inspection work is carried out in a hearth, can contract when regular maintenance work is carried out, and is convenient to maintain. The flight blades are quickly disassembled and maintained, and the service life of the device is prolonged.

The invention discloses a heavy haul railway locomotive auxiliary transformer on-line monitoring system which comprises a sensing system, a control system and a monitoring center, the sensing system is in communication connection with the control system, and the control system is in communication connection with the monitoring center through a wireless communication network. According to the on-line monitoring system for the auxiliary transformer of the heavy haul railway locomotive, through cooperative work of the sensing system, the control system and the monitoring center, real-time acquisition, transmission, analysis and remote monitoring of core operation parameters of the auxiliary transformer are realized, and the problem that the equipment state cannot be mastered in real time through traditional regular maintenance and off-line detection is solved; the 5G wireless transmission and encryption technology guarantees the stability and safety of data transmission, the multi-channel high-speed acquisition module guarantees the data accuracy, the data sharing and report function of the monitoring center provides basic decision support for operation and maintenance, the safety and reliability of operation of the heavy haul railway locomotive auxiliary transformer are remarkably improved, and the safety and reliability of the heavy haul railway locomotive auxiliary transformer are improved. And the sudden fault risk is reduced.

A device for planning the maintenance of a passenger elevator comprising an elevator car with a door equipped with an accelerometer for detecting door vibration, a data collection device designed to generate a vibration profile from the detection, a transmission device for transmitting the vibration profile to a server, and a server for analyzing the vibration profile, wherein the data collection device is spatially connected to the elevator car and is designed to determine the deviation of the vibration profile from a reference profile and, depending on the deviation from at least a threshold value or at least a range of values, cause the transmission device to transmit the deviation and / or the vibration profile to the server.and where the server is spatially separated from the passenger elevator and is designed to compare the deviation and / or vibration profile with at least one threshold value or at least a range of values ​​and, depending on the comparison, issue a signal indicating whether no action is to be taken, an action within the scope of the next regular maintenance, or an action before the next regular maintenance.

The present application relates to the technical field of automobile exhaust detection, and particularly relates to a method and system for detecting automobile exhaust based on a gas sensor array, which comprehensively evaluates the damage of the gas sensor according to the damage analysis of the sensor operation process in the environment where the sensor is located and the damage analysis of the sensor operation process caused by the change of the detection data in the detection process, and then determines the final maintenance cycle according to the comprehensive evaluation result of the damage of the gas sensor, so as to significantly improve the reliability and stability of the gas sensor, dynamically adjust the maintenance cycle according to the actual abnormal situation, keep the sensor in a good operating state at all times, reduce the risk of safety accidents caused by sensor failure, effectively reduce the maintenance cost, accurately arrange the maintenance work, avoid high cost caused by blind regular maintenance, reduce the production loss caused by sensor failure downtime, and realize the win-win of economic benefits and equipment performance.

The invention belongs to the field of electromechanical product maintenance prediction, and particularly discloses a method and system for predicting the periodic maintenance cost of an electromechanical product, and the method comprises the steps: predicting the maintenance cost of a product based on a cost prediction curve between a prediction cost array and a corresponding cost satisfaction rate array; wherein the acquisition method of the cost prediction curve comprises the following steps of: traversing and calculating the probability of the maintenance frequency j of a product i in a new year based on the fact that the service life of the product obeys Weibull distribution; calculating a probability array of the total number of times of maintenance of the first i products by adopting convolution; and repeating the steps until a probability array of the total maintenance times J of all the products in the current batch is obtained, traversing and calculating the prediction cost of the total maintenance times J of the products in the current batch and the corresponding cost satisfaction rate, and obtaining a cost prediction curve. According to the method, the influence of the product number and the product aging degree can be accurately reflected, and the budget cost can be more reasonably formulated.

The present application relates to a kind of solenoid valve maintenance method, it is related to solenoid valve detection field, it includes current solenoid valve parameter in response to maintenance signal, acquisition;Call historical solenoid valve parameter and form parameter change curve;Output maintenance instruction, and receive reply signal;When reply signal is check operation signal, determine parameter change characteristic based on current solenoid valve parameter;Based on parameter change curve and parameter change characteristic, simulate future parameter change curve;Based on future parameter change curve and solenoid valve abnormal parameter critical value, determine predicted maintenance time node, and form maintenance signal when predicted maintenance time node;Reset parameter change curve and form maintenance signal when the small value in periodic maintenance time node and predicted maintenance time node.This application has the time of predicting abnormal parameter occurrence based on future parameter change curve and does good maintenance work in advance, avoid the effect of maintenance not in time produced by relying on periodic maintenance operation.

The application provides an online scheduling method considering periodic maintenance of a batch processing machine, taking the batch processing machine as a research object, considering the batch processing machine in the periodic maintenance process, and solving the actual problems of online scheduling of orders arriving over time, batch grouping, processing and the like. For various common scheduling objectives, such as maximum completion time of a workpiece, maximum flow time of a workpiece in a production system and the like, an effective online algorithm is designed through a waiting strategy to generate a real-time scheduling scheme, and the best value that can be reached by the algorithm performance under various objectives is analyzed, so that the online scheduling rules and methods for solving the online scheduling problem of the batch processing machine under the periodic maintenance are provided, guidance is provided for production planning of an enterprise, and a referable technical means is provided for other batch processing scheduling problems of the enterprise.