362 results about "Device failure" patented technology

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The embodiment of the invention provides an equipment fault prediction management method and device based on a large model, and the method and device achieve the accurate evaluation of a state through the innovative design of a multi-modal feature fusion model, data integration and feature learning. A fault diagnosis system is constructed, and a reliable root cause analysis mechanism is established in combination with semantic analysis and case retrieval. Maintenance guidance is introduced, and the feasibility of a maintenance scheme is ensured through experience precipitation and priority evaluation. According to the method, the defects of the traditional technology in the aspects of feature extraction, fault diagnosis, maintenance guidance and the like are effectively overcome, and technical guarantee is provided for equipment management.

The invention belongs to the technical field of intelligent engineering management, and discloses an automatic monitoring and control method of an engineering management system based on the Internet of Things. According to the method, a four-layer Internet of Things architecture comprising a sensing layer, a network layer, a platform layer and an application layer is constructed, and the four-layer Internet of Things architecture is uploaded to a cloud platform through a 5G / narrowband Internet of Things dual-mode transmission channel. A digital twinborn technology is innovatively adopted to construct a three-dimensional visual engineering model, intelligent identification and prediction of abnormal working conditions are realized through a machine learning algorithm, and a multi-stage linkage control mechanism is established. And when construction deviation or equipment failure is detected, the system automatically generates an optimization control strategy and issues the optimization control strategy to the execution terminal, so that accurate regulation and control of the construction machinery and intelligent pushing of early warning information are realized. According to the method, a manual inspection mode of traditional engineering management is broken through, full-process automatic supervision is realized through data fusion analysis and closed-loop control, the construction quality supervision precision is effectively improved by more than 30%, the safety accident rate is reduced by 50%, and the engineering management efficiency is remarkably improved.

The invention is suitable for the field of equipment fault prediction, and provides an underground equipment multi-source fault prediction method and system based on a large model, and the method comprises the following steps: synchronously collecting an acoustic signal, a visual image, a vibration signal, environment data and working condition parameters of underground equipment in real time, and integrating the acoustic signal, the visual image, the vibration signal, the environment data and the working condition parameters into a multi-modal time sequence data stream; performing time alignment and cleaning on the multi-modal time sequence data stream, extracting deep features of each modal, and generating a multi-source fusion feature vector; inputting the multi-source fusion feature vector into a preset prediction model to obtain prediction information of the underground equipment, and generating fault traceability information; continuously optimizing parameters of the prediction model based on fault data collected in real time; and dynamically displaying prediction and traceability results, and automatically generating a maintenance strategy and an equipment scheduling scheme based on the results. According to the invention, panoramic perception of the operation state of the equipment and accurate capture of early weak fault features are realized, and the comprehensiveness and accuracy of fault detection are greatly improved.

The invention provides an equipment fault maintenance method and system based on a large model, and a storage medium. The method comprises the following steps: determining fault equipment according to equipment fault description; obtaining current operation data and a current operation image of the fault equipment, and inputting the equipment fault description, the current operation data and the current operation image into a pre-trained large model for fault identification to obtain a target fault reason; determining a priority troubleshooting step according to the target fault reason, and sending the target fault reason and the priority troubleshooting step to the user; a troubleshooting feedback result for the priority troubleshooting step is received, and a real fault reason is determined according to the troubleshooting feedback result; and generating an equipment fault maintenance result according to the equipment fault description, the current operation data, the current operation image and the real fault reason. According to the embodiment of the invention, the target fault reason and the priority troubleshooting step are sent to the user, thereby effectively assisting the equipment fault maintenance, and improving the equipment fault maintenance efficiency.

The invention relates to the technical field of fault early warning of power plant equipment, in particular to a method for generating a fault early warning model of power plant equipment. The method comprises the following steps: acquiring power plant equipment operation data, extracting operation coordination relation data, and constructing a power plant equipment operation simulation model; identifying the coupling interference increase condition of the power plant equipment based on the operation data, and detecting the dynamic suppression load increase condition of the equipment by using the operation simulation model; on the basis, the power plant multi-equipment linkage amplification effect is recognized, and the abnormal fault risk is detected; carrying out extension chain analysis, point location tracing and cause identification on the abnormal fault to form a fault correlation structure chain; constructing a fault early warning model based on fault causes, point locations and extension chain data; according to the invention, the fault early warning model of the power plant equipment is constructed, so that the power plant equipment fault identification is more accurate.

The invention provides a computer cross-scale integrated analysis method based on a multi-physics field coupling mechanism, and the method comprises the steps: constructing a multi-level finite element model from a system level to a micro-area level, and integrating thermal-mechanical-electric coupling modeling; therefore, the technical problems that in a traditional method, multi-physics field coupling lacks, local area modeling is not fine, and electrical performance evolution is not introduced are solved, high-precision simulation of a typical device failure mechanism is achieved, and the method is suitable for device packaging reliability evaluation in special electronic systems such as military and high-reliability civil use and the like. The method can be widely popularized in microelectronic structures such as a power supply module, a processor and a memory, and can improve the evaluation precision of the high-frequency signal transmission performance of complex electronic equipment, thereby improving the precision of predicting the service life.

The embodiment of the invention provides an electromechanical equipment fault prediction method and system based on a BAS system, and the method comprises the steps: firstly constructing an equipment state reference library based on a historical operation log of the BAS system, and then obtaining an equipment multi-source monitoring data set through a real-time monitoring network of the BAS system, and covering part sensor data, system control instruction data and environment feedback data. And then performing state alignment verification processing on the multi-source monitoring data set and the equipment state reference library to generate a state alignment verification result indicating a matching degree. And performing fault feature level derivation processing based on the result, and generating an equipment fault precursor feature chain containing an abnormal transmission path. And finally, an equipment fault dynamic risk assessment report is generated according to the evolution trend of the fault precursor feature chain and is synchronized to an operation and maintenance decision module of the BAS system, so that the probability of equipment fault occurrence is effectively reduced, the operation reliability and stability of electromechanical equipment are improved, and economic loss and safety risks caused by equipment faults are reduced.

The invention discloses an electric power system fault protection system and method based on multi-modal data fusion, and relates to the technical field of electric power systems.The method comprises the steps that an electric power equipment fault analysis model is constructed, and historical electric power fault data is collected to train the electric power equipment fault analysis model; inputting the electrical measurement abnormal condition, the equipment state abnormal condition and the environment meteorological abnormal condition into the power equipment fault analysis model to judge abnormal equipment, positioning a fault point of the abnormal equipment, analyzing the abnormal condition of a module corresponding to the fault point based on the electrical measurement data of the fault point of the abnormal equipment, and determining the abnormal condition of the module. Meanwhile, the influence degree of equipment outage caused by the fault of the fault point is evaluated, the equipment fault severity degree is analyzed based on the abnormal condition of the module corresponding to the fault point and the influence degree of equipment outage caused by the fault of the fault point, and an equipment first-aid repair sequence is generated based on the equipment fault severity degree; the accuracy of power failure degree judgment and the emergency response capability of a power system are improved.

The invention provides a multi-parameter cooperative control method and system for a chemical product production workshop, and relates to the technical field of intelligent control, and the method comprises the steps: constructing a multi-parameter sparse coupling topology according to the dynamic mutual information amount between parameters; performing topology path sensitivity evaluation, identifying a key coupling chain which has the greatest influence on the reaction stability, and tracking the collaborative change trend of parameters on the chain in real time; establishing a parameter prediction model, and executing the adjustment priority automatic rearrangement of the intra-chain parameters based on a constraint migration mechanism; constructing a self-evolution cooperative control strategy generator, and generating a cooperative regulation and control instruction of the reaction process through online evolution; and the materials are delivered to corresponding heating equipment, cooling equipment, pressure discharging devices and feeding equipment. The technical problems that in the prior art, adjustment is generally conducted on a single parameter, deep analysis on interaction among multiple parameters is lacked, the system cannot be kept stable when the multiple parameters fluctuate, and then equipment failures or safety accidents are caused are solved.

The utility model discloses an overturning positioning device for machining detection of mechanical parts, which comprises a table body, a clamping positioning assembly is arranged right above the table body, two sliding blocks are driven by a two-way lead screw to drive clamping arms to move face to face or back to back, and preliminary clamping positioning of the mechanical parts is realized. Meanwhile, the adjusting rods are matched with the adjusting openings to flexibly adjust the distance between the suckers according to the sizes of the accessories, so that the suckers can be accurately attached to the surfaces of mechanical accessories of different specifications. A vacuum generator is connected with a connecting port through a connecting pipe, the negative pressure principle is used for enhancing the adsorption force of a suction cup to the parts, a multi-layer and multi-direction stable clamping structure is formed, the situation that the mechanical parts are separated from the clamping device in the overturning process is effectively avoided, the risks of part damage and equipment failure are greatly reduced, the safety of operators is guaranteed, and the working efficiency is improved. And the stability and the production efficiency of the processing detection process are obviously improved.

The invention provides a power device failure positioning method and system based on multi-physics field coupling, and the method comprises the steps: applying dynamic electric stress to a tested power device, and synchronously collecting the thermal field data, electric field data and mechanical field data of the tested power device; wherein the thermal field data is used for representing the surface temperature distribution of the to-be-measured power device, the electric field data is used for representing the electrical parameters of the to-be-measured power device, and the mechanical field data is used for representing the internal morphology of the to-be-measured power device; synchronizing the thermal field data, the electric field data and the mechanical field data through timestamps, and aligning in a preset three-dimensional coordinate system to obtain multi-dimensional alignment data; and generating a failure probability distribution diagram according to the multi-dimensional alignment data so as to determine corresponding failure point coordinates. Through multi-physics field data coupling analysis, the problem of failure point fuzzy positioning in a complex packaging structure is solved, and the detection sensitivity under a dynamic working condition is improved.

The invention relates to the technical field of fault prediction, and provides an equipment fault prediction method and device and electronic equipment. According to the method, the equipment operation data is acquired, the fault prediction model is constructed based on the equipment operation data, and the constructed fault prediction model is used to predict the real-time operation data of the equipment at the second moment to obtain the fault prediction result. And when it is determined that the target index of the fault prediction model is smaller than a preset index threshold value based on the fault prediction result and the rechecking mark information of the fault prediction result, the equipment operation data is updated based on the real-time operation data at the second moment, and then the fault prediction model is updated based on the updated equipment operation data. And finally, the updated fault prediction model is used for predicting the equipment fault, the real-time data of equipment operation can be analyzed and processed, the fault prediction model is constructed in combination with an incremental learning method, model parameters are updated in real time to improve prediction accuracy and real-time performance, the maintenance cost is reduced, and the equipment safety is improved.

The invention discloses an equipment fault prediction method based on industrial big data, and belongs to the technical field of equipment fault prediction, and the method comprises the following steps: S1, obtaining historical operation data and real-time monitoring data of industrial equipment, and generating a standardized data set; s2, analyzing the quantitative proportion of fault samples and normal samples in the standardized data set to form a balanced data set; s3, dividing the balanced data set into a training set and a test set, training a plurality of preset base models in parallel through the training set, and evaluating the weight of each trained base model through the test set; s4, constructing an integrated prediction model according to the weight of the trained base model, and outputting a fault probability and a type identifier; and S5, when the fault probability exceeds a preset early warning threshold value, generating an alarm signal and a maintenance suggestion. By generating the simulated fault data, the problem of data imbalance is effectively solved, and the prediction sensitivity of key fault modes such as rare faults is improved.

The invention discloses a transfer branch structure for vacuum parallel connection and disconnection, a load switch and a ring main unit, and relates to the technical field of ring main unit power distribution switches, and the transfer branch structure comprises a parallel connection and disconnection arc extinguish chamber, a moving end pull rod, a transfer conductor, a solid sealing insulation shielding body, a guide limiting mechanism and an opening maintaining and closing auxiliary mechanism. The guide limiting mechanism is connected with the moving end pull rod and the insulating rotating plate so as to convert the rotating motion of the insulating rotating plate into the linear motion of the moving end pull rod, and when the insulating rotating plate rotates to the position where the transfer branch structure is in the opening state, the opening maintaining and closing auxiliary mechanism compresses stored energy so that the transfer branch structure can be kept in the opening state; and energy can be released in the process that the insulating rotating plate rotates towards the closing position to assist closing, so that the action stability and reliability of the transfer branch structure are greatly improved, the equipment fault risk is reduced, and the service life of the equipment is prolonged.

The invention relates to the technical field of faults of the Internet of Things, in particular to a work order closed-loop management method and system for automatically handling faults of fire-fighting Internet of Things equipment. The method comprises the following steps: obtaining the equipment type, the located area code and the linkage fire-fighting equipment number, and calculating a fault risk index; the partial derivatives of the weight coefficients are solved according to the objective function, the gradient is formed according to the partial derivatives, and the weight coefficients are updated according to the partial derivatives in the reverse direction of the gradient through the principle of the gradient descent method. And the updated weight coefficient is combined with the maximum accommodation number of people corresponding to the region, the maximum linkage number of single equipment, the equipment type weight corresponding to the equipment type, the real-time number of people corresponding to the region and the linkage fire-fighting equipment number to recalculate the fault risk index, and after the weight is dynamically adjusted, influence factors are comprehensively considered, so that the fault risk index is obtained. More reasonable risk assessment is provided for each fault, risk misjudgment caused by unreasonable weight is avoided, and the accuracy of quantitative assessment of fault influence is improved.

The invention provides a maintenance resource configuration and scheduling method, and the method comprises the steps: determining a resource configuration point and the minimum number of resources in a given range, completing the planning of a resource scheduling path for avoiding obstacles and threats, enabling the recovery degree of equipment to be not lower than a target threshold value, and enabling the total time to be shortest. Comprising the following steps: (1) on the basis of a weighted K-means clustering algorithm, taking a clustering center of a plurality of to-be-repaired equipment clusters as a resource configuration point according to position information of to-be-repaired equipment and a repair task weight; (2) allocating resources to each resource allocation point based on a Hungary algorithm, and allocating the resources at the corresponding resource allocation points to the to-be-repaired equipment to ensure that the allocated resources can adapt to the fault of the to-be-repaired equipment; (3) selecting key equipment from the to-be-repaired equipment based on a priority greedy algorithm and preferentially allocating resources for key faults of the key equipment; and (4) planning of avoiding obstacles and threats is carried out on the path from each resource to the allocated to-be-repaired equipment based on an A * algorithm. According to the invention, efficient resource configuration and scheduling optimization are realized.

The invention discloses a transformer substation primary equipment fault discrimination method, equipment and a medium, and relates to the technical field of power systems and automation thereof. Characterization learning is carried out on time sequence operation parameters of a transformer through a variational auto-encoder, load influence and potential fault components are decoupled in a potential space, and a fault is discriminated; the interference of periodic load fluctuation on insulation state judgment is effectively stripped; causal relationship analysis is combined with Granger test and transfer entropy, component independence is evaluated from linear and nonlinear dual paths, early warning is triggered only when a fault component is remarkably independent of load change, and false alarms are reduced; the data quality is improved through the preprocessing and standardization steps, the sliding window segmentation mechanism adapts to the dynamic characteristics of operation parameters, and the adaptability of the model to complex working conditions is enhanced; and the online retraining mechanism triggers model updating through concept drift detection and newly added data accumulation, so that the system can continuously track equipment state evolution, and discrimination performance degradation caused by operation environment change is avoided.

The invention discloses an equipment failure risk analysis method based on a probability hesitant fuzzy evidence theory, and relates to the field of failure risk analysis, and the method comprises the steps: S1, building a fault tree structure, and constructing a reference failure rate set of basic events in a fault tree; s2, acquiring evaluation data of an expert on a reference failure rate set of the basic event, and obtaining an adjustment score and a tendency degree of the basic event; s3, converting the adjustment score into an evaluation probability by using a score conversion rule; s4, establishing a qualification weight according to the qualification of the expert; quantizing and adjusting the uncertainty of the score and the tendency degree to obtain an evaluation uncertainty weight, and carrying out weighted summation on the two weights to synthesize a total weight; s5, based on the evaluation probability and the total weight, synthesizing a final evaluation probability through a Dempster-Shafer evidence theory; and S6, performing fault tree analysis based on the final evaluation probability. According to the method, the probability hesitant fuzzy set and the Dempster-Shafer evidence theory are introduced, the diversity and uncertainty of expert evaluation are reserved, and conflicts and consistency between evidences are effectively processed.

The invention discloses an equipment fault prediction method and device, equipment and a medium, and relates to the technical field of Internet of Things. The method comprises the following steps: acquiring real-time operation data of monitored external equipment; processing the real-time operation data by using a lightweight neural network model so as to extract feature information representing the state of the equipment; the lightweight neural network model is obtained based on teacher model training through a knowledge distillation technology; based on the feature information, performing fault tendency prediction through a lightweight neural network model to obtain a prediction result used for indicating the probability that the external device has a target fault in the future; inputting the feature information into a preset probability clustering model for clustering analysis so as to identify the state mode category of the external equipment; and based on the prediction result and the state mode category, generating maintenance decision information for the external equipment. Through the technical scheme of the invention, the prior art can be optimized, and the accuracy and benefit of intelligent fault prediction of the telemetering terminal can be improved.

The application discloses a kind of high-precision online weight detection equipment with self-checking function, including detection assembly, mutual detection component, weighing top seat, mounting plate, second balance cylinder, first push material cylinder, first push material plate, first material baffle, second material guide plate, second level, self-checking component, third support, third conveyor belt, third motor, fifth fixed plate, second push material cylinder, second push material plate, self-checking block, second material baffle, third material guide plate;The application is designed with self-checking function, and the preset self-checking block is automatically pushed by self-checking component regularly to carry out detection operation, this process is not only a calibration to the weighing accuracy of detection equipment, but also a comprehensive physical examination to the overall operation state of equipment, this self-checking mechanism ensures that equipment maintains high-precision and high-efficiency working state, effectively avoids the problem of inaccurate weighing caused by equipment failure or error accumulation, thereby greatly improving the quality control level of production line.

The present disclosure provides an industrial process-oriented abnormality automatic recovery and event-driven linkage method, comprising: classifying and recording abnormality occurred in an industrial process, scanning unprocessed abnormality records in a preset period, the abnormality at least including one of device offline, device failure, no available device, call failure and production line alarm; for each abnormality record scanned, querying the current state of the corresponding device according to the abnormality type, and judging whether it meets the preset recovery condition corresponding to the abnormality type; in response to meeting the preset recovery condition, generating a recovery instruction based on the abnormality record, finding an execution instance waiting for the abnormality and sending an abnormality recovery signal; in response to listening to the abnormality recovery signal, executing the execution instance. The present disclosure automatically completes abnormality recovery by including each abnormality in life cycle management, associates abnormality recovery and process continuation with event signals, and realizes abnormality traceability and recovery automation.

The application relates to the technical field of energy station building monitoring, in particular to an energy station building equipment fault monitoring method and system, which comprises the following steps: obtaining operation monitoring data of a screw water chiller in an energy station building, setting a slide valve hot range power-on event and a slide valve cold range power-on event according to the operation monitoring data based on a slide valve stable condition; generating a hot range power-on stroke rate according to the slide valve hot range power-on event and a cold range power-on stroke rate according to the slide valve cold range power-on event; counting an abnormal stroke rate number of the hot range power-on stroke rate being less than or equal to the cold range power-on stroke rate; judging whether the abnormal stroke rate number is greater than or equal to an abnormal number threshold value; and if the judgment is yes, generating a slide valve fault alarm. The application can solve the problems of affecting production and long time consumption caused by fault monitoring after the compressor is stopped and disassembled in the prior art.

The invention provides a design method of a replaceable anti-falling beam device for improving transverse anti-seismic performance of a bridge, and relates to the technical field of design of anti-falling beam devices.The method comprises the specific steps that parameters of the bridge without an anti-falling beam and regional historical seismic parameters are collected; different earthquake scenes are simulated, and pier bottom reference anti-seismic performance indexes are output when no anti-falling beam exists; a group of anti-falling beam parameter combination is randomly set, a coupling model is constructed, an earthquake scene in the same S2 is simulated, and an anti-seismic performance index in the presence of an anti-falling beam is output; s5, calculating a performance comprehensive difference value by taking the reference index as a reference, calculating a comprehensive matching degree after the performance comprehensive difference value exceeds a performance threshold value, and entering S5 when the comprehensive matching degree exceeds a matching degree threshold value; and taking the selected parameters as initial solutions, and carrying out iterative optimization by using a simulated annealing algorithm to obtain an optimal combination. Through double-model linkage, a double-screening mechanism and algorithm optimization, the problems that in the prior art, adaptation verification is missing, and a device is prone to failure are solved, the anti-beam-falling protection effect is quantified, and the transverse anti-seismic reliability of a bridge is improved.

The utility model discloses a shielding device for electrical calibration, which belongs to the field of electrical calibration and comprises a movable box body, a cavity is arranged on the upper surface of the movable box body, an electrical shielding tube interface is fixedly mounted on the front surface of the movable box body, a through hole is arranged on one side of the electrical shielding tube interface, and a through hole is formed in the other side of the electrical shielding tube interface. A through hole in one side of the electric shielding pipe connector penetrates into the movable box body, a cover plate is connected to the upper surface of the movable box body in a penetrating mode, an inserting part is arranged on one side of the lower surface of the cover plate, a finger-shaped part is arranged on the other side of the lower surface of the cover plate and is of an arc-shaped structure, and the lower surface of the inserting part is connected to the inner side wall of the movable box body in a penetrating mode. The cover plate is stably and fixedly installed on the surface of the movable box body through the two acting points, a stable, reliable and convenient-to-disassemble structure is constructed, a continuous and stable protection environment is provided for precise elements in the box body, the risk of equipment failures caused by environmental factors is greatly reduced, daily protection requirements are met, and emergency maintenance convenience is achieved.

The utility model relates to the technical field of tool apron structures, in particular to a multifunctional cutting machine tool apron structure which comprises a connecting arm and a tool apron installed on one side of the connecting arm in a sliding mode, a sliding groove is formed in the connecting arm, a fixing part is arranged in the sliding groove, the tool apron comprises a first module and a second module, and a blade is rotationally arranged on the second module. The sliding groove is formed to quickly install the tool apron, installation can be completed only by aligning the first module of the tool apron to the inlet of the sliding groove and then pushing the first module, time is saved, the complexity and the error rate in the installation process are reduced, the tool apron is further locked and fixed through the bolt, and when a blade needs to be replaced or the tool apron needs to be maintained, the tool apron can be installed conveniently. Due to the design of the sliding groove, the downtime can be greatly shortened, replacement or maintenance work of the tool apron can be completed in a short time, the positioning groove is formed so that the blade can be conveniently and rapidly aligned with the output shaft of the micro motor, the equipment failure rate caused by improper alignment is reduced, the reliability and stability of equipment can be improved, and the installation efficiency is improved.

This application provides a material cleaning device for the tail end of a bucket elevator, including a bucket elevator. Two air cannons are installed on one side of the bucket elevator, and each air cannon has a fixed pipe at its lower end. A first air supply pipe is fixedly connected to one side surface of each of the two fixed pipes, and a second air supply pipe is installed at the upper end of the first air supply pipe. A nozzle is connected to the upper end of the second air supply pipe. A buffer mechanism is installed on the lower side of the fixed pipes. This application effectively solves the problem of material accumulation by installing air cannons at the tail end of the bucket elevator. The air cannons can utilize the strong impact force generated by high-pressure gas to quickly and efficiently remove accumulated material at the tail end of the bucket elevator, avoiding problems such as equipment malfunction, increased energy consumption, and accelerated bucket wear caused by hardened accumulated material. This not only significantly improves the operating efficiency of the bucket elevator and reduces downtime due to equipment failure, but also reduces the manpower and safety risks required for manual material cleaning.

The utility model relates to the technical field of agricultural product screening devices, and discloses an agricultural product primary processing screening device convenient to clean, which comprises a shell I, a screen is arranged in an inner cavity of the shell I, a vibration motor is fixedly mounted at the bottom of the screen, and fillets are fixedly mounted at the bottom of the screen and located on the left side and the right side of the vibration motor respectively. Grooves are formed in the bottoms of the fillets, sliding grooves are symmetrically formed in the left portion and the right portion of an inner cavity of the first shell, cavities are formed in the bottoms of the sliding grooves, electromagnets are fixedly installed at the bottoms of inner cavities of the cavities, springs are fixedly installed at the positions, located on the left sides and the right sides of the electromagnets, of the bottoms of the inner cavities of the cavities correspondingly, and movable plates are fixedly installed at the telescopic ends of the springs; and through clamping of an electromagnet power-on and power-off adjusting limiting block and a groove, the screen mesh is convenient to disassemble and assemble, so that the screening mechanism is cleaned, a large amount of time is prevented from being consumed, the working efficiency of the device is prevented from being influenced, maintenance of the device is facilitated, the maintenance efficiency is improved, and the failure rate of the device is reduced.

The application discloses a kind of based on artificial intelligence's hydropower station equipment fault prediction method and device, it is related to equipment fault identification technical field.The method is by collecting the multi-source heterogeneous data of each device, unified state representation vector of each device is generated by the integration of multi-source heterogeneous data of cross-modal fusion network, equipment state is fully described, avoid single data one-sidedness;Determine the equipment coupling state time series based on unified state representation vector;Based on equipment coupling state time series, determine system coupling evolution vector by dynamic evolution analysis;For each device, determine target equipment state representation based on system coupling evolution vector and the enhanced equipment representation corresponding to equipment;Target equipment state representation is input into preset fault prediction model, to obtain the prediction probability of each device failure.By fusing equipment individual characteristics and system global information for fault prediction, improve single and concurrent fault prediction accuracy, greatly reduce operation and maintenance cost and fault risk.

The utility model discloses a patch detection mechanism, and belongs to the field of patch detection, and the patch detection mechanism comprises a frame, the inner wall of the frame is fixedly provided with a conveying belt, the top of the frame is fixedly provided with a vertical rod, the outer edge of the vertical rod is slidably connected with a rubber block, the inner wall of the rubber block is slidably connected with a cross rod, and the cross rod is fixedly connected with the conveying belt. The outer side of the cross rod is fixedly connected with a fixing plate, the inner side of the fixing plate is fixedly provided with a barrier strip, the top of the frame is fixedly provided with a support, and the inner top of the support is fixedly provided with optical detection equipment. The sucking pump is driven to operate to generate suction force, the sucking disc is matched with the mechanical arm to suck defective PCBs after detection, the mechanical arm is driven to operate to transfer abnormal PCBs, the abnormal PCBs are stored in the inner wall position of the storage box, workers can conveniently treat the abnormal PCBs in a follow-up centralized mode, materials are taken in an adsorption mode, and the working efficiency is improved. And a material removing mechanism of a complex structure in an existing device is replaced, the material taking mode is simple and convenient, efficiency is high, and the equipment failure rate is low.