1298 results about "Mechanical vibration" patented technology

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable electrical performance detection method and system, and the method comprises the steps: synchronously collecting a broadband electromagnetic signal, a mechanical vibration signal and a temperature signal at a cable monitoring point; calculating a wavelet coherence coefficient between the signals through continuous wavelet transform, and constructing a multi-modal coupling tensor fusing amplitude and cross-modal time-frequency correlation characteristics; performing time slicing and high-order singular value decomposition on the tensor to obtain a time-varying core tensor sequence, mapping the time-varying core tensor sequence into a high-dimensional manifold curve, and generating a system state fingerprint by calculating local curvature distribution and topology invariants of the curve; inputting the fingerprints into a pre-trained defect prediction model, and directly outputting defect inoculation probability and evolution stage judgment; according to the method, the limitation that early weak defect detection is not sensitive in a traditional method is broken through, and early warning and accurate diagnosis of cable insulation latent defects are achieved.

The invention discloses a complete switch equipment online detection method and system based on multi-sensor fusion, and relates to the technical field of equipment state detection.The method comprises the steps that temperature distribution, partial discharge signals, mechanical vibration waveforms and operation current data of switch equipment are collected, key fault features are extracted based on preprocessed multi-source data, and the key fault features are extracted; generating a high-dimensional feature matrix, constructing a graph convolution-long and short-term memory hybrid network model as a fault diagnosis model, and obtaining a defect detection result according to the spatial-temporal features; and constructing an equipment degradation index based on defect characteristics to calculate an equipment health index, performing probabilistic prediction of the remaining service life in combination with a Wiener degradation model, and triggering an early warning signal when defects are detected or the service life is lower than a threshold value. According to the method, the problems of large data limitation and insufficient fault diagnosis precision of a single sensor in the operation state monitoring of the whole set of switch equipment are solved, and fault evaluation and residual life prediction are further realized by combining a hidden defect evolution rule.

The invention relates to the technical field of computers, discloses a multi-system data fusion predictive maintenance method and system, and aims to solve the problems that multi-source heterogeneous data integration is difficult, health state perception is one-sided, prediction model adaptability is poor and maintenance decision closed loops are missing. The method comprises the following steps: acquiring mechanical vibration, thermodynamics and electrical signals through a multi-source sensing access module, and performing time alignment; and in combination with operation behavior information input by the operation registration terminal, a data fusion engine executes standardized mapping and missing value compensation to generate a structured input matrix. According to the scheme, deep fusion of multi-system data and continuous quantitative evaluation of the health state are achieved, the fault early warning advance time is shortened, the evaluation accuracy is improved, adaptive regulation and control and closed-loop maintenance decision are supported, the service life of key components is prolonged, and the method is suitable for various high-end manufacturing scenes.

The invention relates to a GIS (Gas Insulated Switchgear) equipment mechanical defect diagnosis method based on a Gramb angle field and a dual-channel PCNN-Attention neural network, and belongs to the technical field of gas insulated switchgear mechanical vibration defect diagnosis. The method solves the problems that traditional diagnosis depends on artificial feature extraction, so that subjectivity is high, information mining is insufficient, and defect severity evaluation is missing. According to the technical scheme, the method comprises the steps that a one-dimensional vibration signal is converted into a GASF two-dimensional image and a GADF two-dimensional image through a GASF field so as to completely reserve time sequence topological features; carrying out data enhancement by adopting an image geometric transformation technology so as to improve the generalization ability of the model; and a dual-channel PCNN-Attention model is constructed, and synchronous intelligent identification of defect types and severity is realized through parallel feature extraction and dynamic weight optimization of an attention mechanism. According to the method, the diagnosis accuracy, reliability and adaptive capacity are improved, and support is provided for equipment state operation and maintenance.

The invention discloses a ventilator fault detection and early warning method based on edge calculation, and particularly relates to the field of fault detection.The ventilator fault detection and early warning method comprises the steps that a multi-dimensional sensor is deployed to collect mechanical vibration, acoustic characteristics, electrical parameters and environment working condition data, and 28 structured feature sets are formed through preprocessing and feature extraction; constructing four types of adaptive feature functions, and converting high-dimensional features into fault reliability or environmental interference correction coefficients; fusing multi-dimensional evidences by adopting an improved D-S evidence theory, resolving conflicts and judging four states of normal, slight fault, serious fault and uncertainty; and finally, through a cloud-edge collaborative architecture, the cloud end iteratively optimizes characteristic function parameters and fusion rules based on a fault case library, and safely issues the characteristic function parameters and the fusion rules to an edge end. The method depends on the low delay advantage of edge calculation, combines machine learning and evidence theories, effectively reduces misjudgment caused by environmental interference, realizes accurate fault identification and early warning, and is suitable for intelligent operation and maintenance of the ventilator under multiple working conditions.

The invention discloses a vacuum negative pressure vibration assisted precision casting forming method. The method comprises the steps of model preparation, sand box and dry sand pretreatment, layered vibration sand filling, film covering vacuumizing, molten metal smelting and purification treatment, negative pressure vibration pouring, pressure maintaining solidification, segmented temperature control and pressure relief box opening and multi-stage cleaning aftertreatment. The core of the method is that mechanical vibration and a vacuum negative pressure technology are deeply coordinated and penetrate through the whole process of modeling, pouring and solidification, and meanwhile, a series of fine control means such as dry sand accurate pretreatment, molten metal rotary blowing degassing and instantaneous inoculation, stepped negative pressure control, temperature-related segmented pressure relief and two-stage shot blasting cleaning are matched; through dynamic cooperation of vibration and negative pressure, the sand mold compaction uniformity, the molten metal mold filling capacity and the solidification feeding effect are remarkably improved, the casting defects of air holes, shrinkage porosity, insufficient pouring and the like are effectively reduced, and the internal density, the size precision and the surface quality of a casting can be remarkably improved.

The invention belongs to the technical field of industrial automation control, and particularly relates to a control system and method of a battery shell cleaning machine, and the method comprises the steps: obtaining a sound field signal in a cleaning tank, and carrying out the preprocessing to remove direct current drift and low-frequency mechanical vibration; the permutation entropy of the sound field signals is calculated to evaluate load complexity, a penalty factor of variational mode decomposition is adaptively constructed according to the load complexity, and then a fundamental frequency mode and a high frequency mode are separated; constructing a risk index reflecting the cavitation erosion degree according to the energy ratio of the high-frequency mode and the time domain volatility of the high-frequency mode; and according to the deviation between the risk index and the safety threshold, cooperatively adjusting the frequency jitter amplitude and the power amplitude of the ultrasonic generator. According to the method, through a self-adaptive decoupling damage signal and double-variable cooperative control strategy, on the premise that high cleaning efficiency is guaranteed, destructive cavitation and resonance damage are effectively restrained, and the balance between the cleaning effect and workpiece safety is achieved.

The invention relates to the technical field of electrical fault positioning, in particular to an urban rail train relay state online monitoring method and system. The specific implementation process comprises the following steps: collecting coil current, contact voltage, load current and environment vibration signals by using an electrical sensor group; when the coil current is subjected to step change, intercepting an electromechanical characteristic data set; arc energy and contact resistance fluctuation characteristics are calculated, correlation analysis is carried out in combination with environment vibration signals, and passive fluctuation and active fluctuation are distinguished; and establishing a multi-factor health degree evaluation model, dynamically adjusting the arc energy and the weight coefficient of the contact resistance according to the magnitude of the load current and the vibration intensity, monitoring the health state of the relay in real time, and predicting the residual life. Through multi-dimensional data synchronous acquisition and vibration decoupling, the interference of mechanical vibration on electrical performance monitoring is effectively eliminated, the problem that a dynamic working condition cannot be reproduced in an off-line test is improved, and the accuracy of relay state evaluation and service life prediction is remarkably improved.

The invention discloses a hanging basket motion control method and system based on operation state recognition, particularly relates to the technical field of bridge construction automation, and is used for solving the problem of poor synchronous control of hanging basket movement. According to the method, working conditions are accurately recognized through multi-source sensing data fusion and a finite-state machine, servo system parameter self-adaptive setting is achieved based on working condition parameter mapping, and an anti-interference movement track is generated by adopting a model prediction control algorithm fusing pose feedback and wind load feedforward; walking synchronization and platform stability are achieved through cross coupling control and posture closed-loop fine adjustment, emergency response is provided through the independent safety guarding thread, control and protection double closed loops are formed, and therefore the motion precision, the anti-interference capacity and the intrinsic safety level of the hanging basket under the variable-load and strong-disturbance working conditions are effectively improved, and the safety of the hanging basket is improved. And the energy consumption and the mechanical vibration frequency in the moving process of the hanging basket are reduced.

The invention discloses an electric linear servo system control method based on cascade LESO-DISMC. The method comprises the following steps: firstly, establishing a complete mechanism model of a permanent magnet synchronous motor servo loading system; then designing DOB interference observer real-time compensation by adopting a LuGre friction model and a gap dead zone model; a feedforward control quantity is constructed by utilizing a measurable displacement signal, and redundant torque is inhibited; designing a dynamic integral sliding mode controller to ensure global stability; a high-low bandwidth LESO is constructed, and both fast tracking and smooth filtering are considered; and finally, trajectory optimization is carried out, a smooth trajectory is generated by adopting polynomial interpolation for the step and triangular wave signals, and mechanical vibration is inhibited. According to the method, a LuGre friction model is adopted to compensate nonlinear friction, cascade linear LESO and sliding mode control are combined, a track is optimized through an S-shaped curve, and the problem of'differential explosion 'is solved; according to the method, instruction smoothing, core control and friction compensation are fused, and the anti-interference capability and the loading precision of the system are improved.

The invention relates to the technical field of plastic molding and treatment, and discloses a molding processing and post-treatment process of low-odor plastic, which comprises the following steps: extruding a plastic melt containing volatile components into a to-be-cut material strip, pelletizing in a pelletizing area by using a pelletizing device, and synchronously adjusting the pulse spraying frequency to adapt to the pelletizing frequency, an alternating stress field is induced on the surface layer of a material strip through periodic cold impact and mechanical vibration, a micro-crack structure is formed on the surface layer of plastic particles by controlling the particle size of atomized liquid drops, an exhaust channel for volatile component escape is constructed, and then the plastic particles are conveyed to post-treatment equipment for volatile component removal. A solid diffusion process blocked by a skin layer is converted into a physical inrush flow along a microcrack channel, so that a mass transfer energy barrier is reduced, efficient removal of peculiar smell components is realized, and thermal degradation and yellowing of particles are avoided.

The invention provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering, and the method comprises the steps: carrying out the multi-period sampling and time window division of an original signal inputted by an industrial field sensor or a switching value, and extracting the time distribution characteristics of the signal; performing signal stability judgment and pseudo edge elimination based on the signal time distribution characteristics, and outputting a stable edge event set; performing event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary time sequence trigger signal; environment characteristic data are collected, real-time feedback of an actuator is obtained, and system judgment parameters are dynamically corrected; and generating a control command and verifying an execution result based on the preliminary time sequence trigger signal and actuator feedback, the method can dynamically distinguish real signals and noise under vibration interference, simultaneously ensures the real-time performance and trigger reliability of the system, and solves the problems of insufficient edge detection stability and false trigger phenomenon caused by mechanical vibration.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention relates to the technical field of hydropower station intelligent operation and maintenance, in particular to a hydropower station equipment operation and maintenance management system with a fault self-diagnosis function, which comprises a data acquisition module, a fault diagnosis module, a decision optimization module and a visual interaction module which are connected in sequence, the data acquisition module comprises a hydraulic parameter acquisition unit, a mechanical vibration acquisition unit and an electrical parameter acquisition unit; the fault diagnosis module comprises a hydropower station feature extraction unit, a multi-physics field coupling analysis unit and a dynamic threshold adjustment unit; the decision optimization module comprises a fault influence tree unit and a maintenance decision optimization unit; the visual interaction module comprises a three-dimensional equipment twinning unit and an early warning traceability unit. Through the fault influence tree and maintenance decision optimization, the remaining service life of the equipment can be effectively predicted, the maintenance time is reasonably arranged, and stable operation of the hydropower station is ensured.

The invention provides an error checking method and system of an intelligent water meter, and relates to the technical field of error checking of the intelligent water meter, and the method comprises the following steps: collecting multi-modal monitoring data of the intelligent water meter; performing signal preprocessing on the water flow acoustic signal and the mechanical vibration signal to generate an acoustic fusion signal, extracting time-frequency domain features of the acoustic fusion signal related to errors, and constructing an optimal error feature set; performing pattern recognition based on the optimal error feature set and a preset standard verification feature library to generate an error preliminary result; constructing an error prediction and verification model, verifying the error preliminary result in combination with the optimal error feature set and the flow data, and outputting an error verification result; when the error grade reaches a preset error grade threshold value, an early warning mechanism is triggered, and early warning information is pushed, so that the metering performance of the water meter can be evaluated in real time, the error change trend can be dynamically predicted, the timeliness and effectiveness of error verification are effectively guaranteed, and the dynamic error management and control requirements of the intelligent water meter are comprehensively met.

The invention provides a bridge self-adaptive multi-dimensional vibration reduction support device and a working method, relates to the field of bridges, and aims to solve the technical problems that an existing bridge vibration reduction device is difficult to consider multi-dimensional vibration control and adjustment depends on an external power supply. Vibration mechanical energy is converted into magnetic field energy to act on magnetorheological fluid, a self-adaptive vertical damping adjusting mechanism which does not need external energy and has stronger vibration and larger damping is constructed, and rolling friction energy consumption of friction balls in the transverse vibration reduction assembly and displacement constraint of a limiting ring are combined, so that the vibration reduction effect is improved. The multi-dimensional control over the vertical direction, the horizontal direction and the torsion is achieved through the combined deformation energy dissipation of a vibration reduction spring and a limiting cable in the torsion vibration reduction assembly, the problems that a traditional bridge vibration reduction support is single in function, cannot be adjusted in a self-adaptive mode and fails in power failure are effectively solved, and the anti-vibration safety and the operation stability of the structure in the complex power environment are improved.

The invention provides an anti-eavesdropping system and an anti-eavesdropping method for eavesdropping according to a substance vibration principle. The system comprises an anti-eavesdropping intelligent host which is arranged indoors and comprises a pickup module and a signal processing module; the pickup module is used for picking up a sound signal of an indoor target sound source; the signal processing module is connected with the pickup module and is used for processing the sound signal to generate an interference signal; and the at least one interference generator is arranged on a solid isolator for isolating the indoor space from the outdoor space, is connected with the anti-eavesdropping intelligent host, and is used for converting the interference signal into a mechanical vibration signal so as to interfere an eavesdropping device through the mechanical vibration signal. According to the environment medium vibration principle, anti-eavesdropping is effectively achieved through reverse interference of the mechanical vibration signals on the eavesdropping device, and the anti-eavesdropping device has the advantages of being independent of sites, rapid in deployment and low in cost.

The invention relates to a cable insulation aging state evaluation method based on operation data analysis. The method comprises the following steps: S1, acquiring cable operation data in real time through a heterogeneous sensor array; s2, adopting a layered preprocessing strategy on the edge side; s3, constructing a hybrid deep learning model; s4, outputting by adopting an improved evidence theory fusion model; s5, outputting a fourth-level aging state label and a confidence evidence theory fusion model; electrical parameters and non-electrical parameters of the cable are collected in real time through the heterogeneous sensor array, and multi-dimensional data such as partial discharge signals, three-phase current harmonic distortion rate, distributed optical fiber temperature, mechanical vibration acceleration and environment dew point temperature are covered. The multi-source data fusion mode can comprehensively reflect the operation state of cable insulation, the limitation of single parameter evaluation is avoided, and the accuracy of the evaluation result is greatly improved.

The invention relates to the technical field of fault prediction, and particularly discloses a fault prediction method and system for a high-voltage switch operating mechanism. According to the method, early-stage accurate prediction of the fault of the high-voltage switch operating mechanism is realized through multi-feature extraction and fusion in combination with a lightweight Transformer model, and compared with a traditional single current signal analysis method, the method fully considers the coupling effect of electromagnetic and mechanical states, captures electromagnetic abnormality through current-electromagnetic coordination node features, and improves the accuracy of the fault diagnosis of the high-voltage switch operating mechanism. According to the method, mechanical vibration faults are recognized through vibration energy features, motion discordance is detected through electromagnetic driving force-motion acceleration cooperative features, so that possible fault modes of the mechanism are comprehensively covered, the accuracy and the real-time performance of evaluation are further improved through efficient fusion of the lightweight Transform model, the early fault missing report rate is effectively reduced, and the reliability of the mechanism is improved. And reliable guarantee is provided for safe operation of a power system.

The invention discloses a robot joint torque control system and method, and the system comprises a joint torque sensor which is used for detecting the actual output torque of a joint; the joint driver is used for driving robot joints to move according to the control instruction; the bearing pre-tightening mechanism is used for applying pre-tightening force to the joint bearing; the multi-source environment sensing module comprises a temperature sensor used for detecting the temperature of the bearing, a vibration sensor used for detecting the mechanical vibration frequency and amplitude, and an electromagnetic interference detector used for detecting the electromagnetic interference intensity; the intelligent compensation module is integrated in the controller and comprises a thermal deformation compensation unit, the input end of the thermal deformation compensation unit is connected with the temperature sensor, and the thermal deformation compensation unit is used for generating the first torque compensation amount. According to the method, high-stability control over the robot joint torque is achieved, the influence of various kinds of interference and uncertainty on the torque control effect is reduced, the reliability under the complex working condition is remarkably improved, and the higher-precision robot joint torque control requirement is met.

The invention relates to the technical field of transformers, and discloses a noise reduction type intelligent large dry-type transformer which comprises a base, a sound insulation plate is fixedly connected to the left side of the base, a ventilation door is rotatably connected to the front portion of the sound insulation plate, a buffer spring is fixedly connected to the inner wall of the base, and a supporting frame is fixedly connected to the top of the buffer spring. A transformer body is arranged at the top of the supporting frame, a fixing plate is fixedly connected to the left side of a sound insulation plate, a chamfering plate is fixedly connected to the bottom of the fixing plate, a reciprocating lead screw is rotatably connected to the inner wall of the chamfering plate, and a U-shaped plate is in threaded connection with the circumferential surface of the reciprocating lead screw. And vibration generated in the operation process of the transformer body can be buffered, so that mechanical vibration generated in the operation process of the transformer body can be remarkably reduced, noise is reduced, and the comfort degree of the working environment is improved.

The invention discloses a microwave cavity heating structure based on acoustic vibration and a microwave heating homogenization method. Comprising a microwave heating cavity, an acoustic vibration device, a dynamic metal structure, a microwave generator and a temperature sensor, the microwave heating cavity is internally provided with an inner cavity, and the bottom surface of the inner cavity is connected with a plurality of dynamic metal structures which are distributed at equal intervals in an array manner; the acoustic vibration device is arranged on the side wall of the inner cavity and used for generating acoustic vibration to the inner cavity, the microwave generator is arranged on the side wall of the inner cavity and used for injecting microwave energy into the inner cavity to form an electromagnetic field, and the dynamic metal structure generates mechanical vibration under the influence of the acoustic vibration so as to dynamically change the distribution of the electromagnetic field of the inner cavity; and the temperature sensor is arranged at the top of the microwave heating cavity. The problems of hot spots and non-uniform heating in the microwave heating process can be effectively solved, the heating efficiency and quality are improved, and the microwave heating device is suitable for the fields of food processing, material treatment, chemical reaction and the like.

The invention relates to the technical field of cutting, and discloses an aluminum profile machining and cutting device which comprises a base table, a bearing table is fixedly connected to the top of the base table, cutting tables are symmetrically and fixedly connected to the outer wall of a shaft rod of the bearing table, and a material preparation table is arranged on one side of each cutting table and fixedly installed above the base table. A cutting structure is arranged over the cutting tables, clamping assemblies are arranged on the surfaces of the cutting tables, inclined vibration assemblies are arranged below the two cutting tables, through the inclined vibration assemblies, after cutting is completed, the inclined vibration assemblies drive the cutting tables to rotate in a hinged mode with a bearing table shaft rod as the axis, the ends, away from the bearing table, of the cutting tables are made to incline downwards, and a downward inclined face is formed; in the inclination process, the inclination vibration assembly enables the surface of the cutting table to vibrate through mechanical vibration, chippings are assisted to fall off, the situation that the residual chippings influence the follow-up cutting precision or scratch the surfaces of the pipes is avoided, the pipes and the cutting chippings are efficiently removed through the dual action of gravity and vibration, and the continuous operation efficiency is improved.

The invention relates to the technical field of grain loss monitoring, in particular to a wheat harvesting loss monitoring system and method, and the system comprises an unmanned aerial vehicle monitoring module, a multi-mode sensor array, a signal processing module, an intelligent control module and a man-machine interaction terminal. The unmanned aerial vehicle monitoring module is used for scanning a field parcel, identifying a lodging area and a high-density area and generating a loss risk thermodynamic diagram, the loss risk thermodynamic diagram is imported into a harvester navigation system, and the high-risk area is harvested preferentially; the multi-mode sensor array comprises a piezoelectric grain sensor and an image sensor; the piezoelectric type grain sensor is mounted at the tail part of the cleaning sieve, adopts a vibration isolation device (such as a rubber pad) to reduce mechanical vibration interference, and detects a grain impact signal through a piezoelectric element; the image sensor is arranged on a header and a threshing outlet and is used for identifying missed cutting of wheat ears and damaged grains based on a deep learning algorithm; the intelligent control module comprises a loss rate calculation unit and a parameter optimization unit; the precision and the real-time performance are improved.

The invention relates to the technical field of power equipment fault monitoring, in particular to a distribution box fault monitoring method, and aims to solve the problem that a traditional monitoring method in the prior art depends on a single filtering or fixed threshold strategy and is difficult to effectively separate fault features. According to the method, mechanical vibration and electromagnetic interference signals can be effectively separated through an adaptive blind source separation algorithm, the problem of early fault signal identification in a mixed interference environment is solved by combining multi-scale feature fusion and a dynamic environment compensation mechanism, and the method has the advantages of improving monitoring accuracy and realizing accurate quantification of fault energy.

The invention provides an online monitoring method for mechanical characteristics of a high-voltage circuit breaker. The online monitoring method comprises the following steps: mounting a plurality of piezoelectric ultrasonic sensors on the surface of a key mechanical part of the high-voltage circuit breaker; when the high-voltage circuit breaker executes opening or closing operation, ultrasonic original signals generated by the ultrasonic sensors are synchronously collected; filtering and de-noising the collected original signal, and extracting an effective ultrasonic signal generated by mechanical vibration; extracting a time domain feature and a frequency domain feature to form a feature vector; and inputting the feature vectors into a pre-generated ultrasonic feature-mechanical feature mapping model, calculating to obtain mechanical feature parameters of the high-voltage circuit breaker, judging whether an abnormal condition exists or not, and generating and outputting graded early warning information if the abnormal condition exists. The invention further discloses a corresponding system. According to the invention, the anti-interference performance of the monitoring process can be improved, multi-parameter synchronous monitoring can be carried out, the operation and maintenance efficiency can be improved, and the operation and maintenance cost can be reduced.

The invention discloses a power equipment fault prediction method and system based on deep learning. The method comprises the following steps: acquiring multi-modal monitoring data and carrying out standardization and normalization processing on the multi-modal monitoring data; respectively extracting preliminary features of the electrical signal, the mechanical vibration, the thermal imaging image and the sound spectrogram through a one-dimensional neural network and a two-dimensional convolutional neural network; fusing each modal feature by adopting an attention mechanism, calculating an association weight and generating a comprehensive feature vector; different long and short-term memory networks are selected according to the weight to process sequence dependence, and an association sequence is obtained; abnormal points are detected and clustered, and a high-risk abnormal mode is identified; performing multi-step prediction on the high-risk equipment to obtain a future evolution sequence; and a fault early warning is generated by comparing the similarity of the current and future features. According to the invention, multi-source information can be fused, early abnormity can be accurately captured, and intelligent prediction and early warning of power equipment faults can be realized.

The invention discloses a mechanical equipment residual life prediction method based on multiple expert models, and belongs to the technical field of mechanical equipment. Comprising the steps of receiving an original mechanical vibration signal, performing preprocessing, constructing a residual life prediction model of the mechanical equipment, training the residual life prediction model of the mechanical equipment based on a preset data set, inputting a mechanical vibration signal to be detected into the trained residual life prediction model of the mechanical equipment, and performing fault category prediction of the mechanical equipment. According to the method, the accuracy, generalization and practicability of residual life prediction of mechanical equipment are remarkably improved, and through multi-scale time-frequency domain feature fusion and a double attention mechanism, the identification capability and noise immunity of the model to a complex fault mode are enhanced; the domain adaptive routing network realizes cross-device efficient knowledge migration, greatly improves the prediction performance on unseen devices, has high precision and high real-time performance, and provides reliable technical support for industrial predictive maintenance.

The invention discloses an early warning system and method for looseness of a wiring terminal of a distribution box, and relates to the technical field of power equipment monitoring, and the system comprises a miniature vibration sensor module which is installed in the center of a polished rod section of a connecting bolt of a target wiring terminal in an embedded manner, and is used for collecting a micro-amplitude mechanical vibration signal; the non-contact infrared temperature measurement module is over against the surface of the wiring terminal and is used for monitoring the temperature and calculating the temperature rise rate; the edge calculation unit, the vibration sensor module and the infrared temperature measurement module are in communication connection, synchronously acquire data, extract vibration spectrum characteristics and temperature rise rate, and perform fault judgment based on a preset coupling criterion; and the wireless communication module uploads the early warning event to a remote monitoring platform. The method has the advantages that data are acquired through cooperation of the rigidly coupled miniature vibration sensor and the multi-angle infrared temperature measurement module, real looseness and load fluctuation interference are effectively distinguished by combining a dynamic threshold value and a multi-physics coupling criterion, the weak looseness characteristic signal acquisition sensitivity is improved, and the multi-physics coupling criterion is accurately identified.

The invention provides a prepreg method and equipment for automatically adjusting compensation parallelism, computer equipment and a storage medium. The method comprises the steps that an upper roller and a lower roller abut against each other in parallel to obtain a current end pressure standard value; setting an end pressure deviation threshold value; respectively obtaining a first end pressure value and a second end pressure value when the two ends of the upper roller and the lower roller jointly extrude the carbon fibers during preimpregnation; carrying out difference processing on the first end pressure value and the second end pressure value to obtain an end pressure deviation value; judging whether the absolute value of the end pressure deviation value is greater than an end pressure deviation threshold value; if yes, the gaps between the two ends of the upper roller and the gaps between the lower roller are automatically adjusted so as to compensate the parallelism between the upper roller and the lower roller. Through comparison and judgment of the end pressure deviation threshold value and the absolute value of the end pressure deviation value, the gap does not need to be adjusted according to the pressure detection value, mechanical vibration and pressure detection noise are prevented from causing excessive adjustment of preimpregnation equipment, interference items of mechanical vibration and pressure detection noise are eliminated, and the prepreg operation is more stable.