477 results about "Normal state" patented technology

The invention relates to an anti-ant large-core-number fiber ribbon cable comprising a cable core and an outer sheath. The cable core includes loose tubes; and fiber ribbons are sleeved inside the loose tubes. The outer sheath is an outer nylon sheath; and a non-metallic reinforcement layer is arranged between the outer sheath and the cable core. According to the anti-ant large-core-number fiber ribbon cable provided by the invention, with the outer nylon sheath, ants can not bite the sheath, so that the damages on the optical cable by ant biting in a pipe at an area with a serious anti problem are prevented, the normal state of the cable is ensured, and the application region of the cable is extended. The cable based on a simple and reasonable structure has advantages of small cable diameter, high fiber density, large fiber capacity, and low duty ratio and can be laid in a pipe conveniently; and the bending performance is good. During branched connection, 4 to 24 fibers can be melted once, so that the construction time is saved and the manpower cost is lowered, and the construction efficiency is improved. On the basis of the full drying type dielectric structure, branched connection is realized conveniently and usage becomes convenient.

The invention provides an equipment state deviation identification method based on self-supervision and incremental learning, and the method comprises the steps: S1, obtaining time sequence data of equipment in a fault-free state, and constructing a normal state model; s2, during operation, deviation detection is carried out on real-time data through the normal state model, and a deviation degree index is obtained; s3, comparing the deviation degree index with a preset threshold value, and judging an abnormal event; s4, determining new normal state data by manually verifying the abnormal event; and S5, updating the normal state model according to the new normal state data. The method does not need to depend on a fault sample, establishes an equipment normal behavior model through self-supervised learning, introduces a deviation index to quantify a state difference, and combines manual feedback and incremental learning to form a closed loop, so that the model has self-adaptability and long-term evolution ability.

The invention relates to a safety air bag ignition control method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps that the running state of the automobile safety air bag control system is configured through the safety control state machine; under the condition that the running state is the normal state, the ignition function of the automobile safety air bag control system is configured to be in a forbidden state through a safety control state machine; in response to the fact that the automobile safety air bag control system is powered on for the first time and / or a watchdog error is detected, the running state is switched to a watchdog error protection state from a normal state through a safety control state machine; and under the condition that the operation result of the sensor data processing unit exceeds a preset threshold value, the operation state is switched from the normal state to the safety air bag ignition enabling state through the safety control state machine, and the ignition function is switched to the starting state. By adopting the method, the low-delay and high-precision response requirements of the chip on the ignition control instruction can be met.

The invention provides a production control method and system based on SPC alarm linkage process locking, and relates to the technical field of quality management and control, and the method comprises the steps: deploying an SPC sensing node used for collecting technological parameters at a cutting edge position where a machining tool of production equipment is in contact with a workpiece, or at a contact point where a probe of online measurement equipment is in contact with the surface of the workpiece; the method comprises the following steps: acquiring process parameter data of a key production process in real time through an SPC sensing node deployed at a cutting edge position or a contact point; inputting the process parameter data as real-time data or small sample expansion data into an SPC judgment engine; and performing statistical process control analysis on the real-time data or the small sample expansion data through an SPC judgment engine so as to model data clusters which are densely distributed and have stable parameters in the data into a virtual reference curved surface representing a normal state domain. According to the invention, the accuracy of abnormity determination can be improved, and timely management and control of quality abnormity and minimization of production loss can be realized.

The invention provides an equipment fault early warning method and system, and belongs to the technical field of fault prediction, and the method comprises the steps: obtaining the real-time working condition parameters of equipment; the real-time working condition parameters comprise real-time operation parameters and environment parameters; extracting a first feature vector based on the real-time working condition parameter; respectively calculating the similarity between the first feature vector and a plurality of second feature vectors in a model library; determining the corresponding normal state model with the maximum similarity as a target model; and inputting the real-time working condition parameters of the equipment into the target model to obtain the deviation between the real-time working condition parameters and the normal parameter range, and carrying out fault early warning on the equipment based on the deviation. According to the equipment fault early warning method and system provided by the invention, the accuracy of equipment fault early warning can be improved.

The invention relates to the technical field of mechanical equipment fault diagnosis, and particularly discloses an attention mechanism CNN-based electric actuator fault feature extraction method and system. The method comprises the following steps: through a mode of combining simulation modeling and field acquisition, obtaining a fault feature of an electric actuator; obtaining multi-source data of the electric actuator in a normal state, a known fault state and an unknown fault state, and preprocessing and enhancing the multi-source data; and constructing a lightweight convolutional neural network model containing a cross-modal attention mechanism. According to the method, multi-source signals such as current, voltage and displacement can be effectively fused, weights of different modes and characteristics are automatically distributed, and the extraction capability of key fault characteristics is enhanced; accurate distinguishing between known faults and unknown faults is realized through multi-scale feature complexity measurement and a dynamic discrimination mechanism; in combination with domain self-adaption and an incremental learning mechanism, the model can migrate between simulation and real working conditions, and has self-expansion capability for a novel fault mode.

To provide a game machine that improves management of game media number display according to the game state.SOLUTION: A game machine of the present invention has three game states: a first normal state, a second normal state, and a specific game state. Control can be performed so that the degrees of advantage for auxiliary games differ between the first and second game states. A game machine information notification command containing information indicating the game state can be generated and transmitted to virtual game media number control means. Standby processing can be executed when a situation in which a response command from the virtual game media number control means cannot be received occurs a predetermined number of consecutive times. When it is determined that a power interruption has occurred, after storing a checksum in a used area of the memory of main control means and a back flag to be referenced when recovering from the power interruption, the standby processing can be executed. A power-on operation accompanied by a RAM clear clears the backup information in the main control means, but does not clear the virtual game media number which can be played.SELECTED DRAWING: Figure 40

The invention provides a monitoring data determination method, a simulation device, a simulation device test method, electronic equipment and a storage medium, and relates to the field of data processing, the simulation device comprises a displacement device, a plurality of target anchor chains and sensors corresponding to the target anchor chains, the monitoring data determination method comprises the following steps: acquiring displacement parameters of a displacement device; controlling the displacement device to displace in a preset space range at a preset speed, wherein the preset space range is determined based on the displacement parameter; a top end tension data set of the corresponding target anchor chain is collected through a sensor, the top end tension data set comprises top end tension of the target anchor chain in multiple different target states, and the target states comprise at least one of a normal state, an anchor dragging state and an anchor breaking state; and storing the top tension data set, wherein the top tension data set is used for monitoring the target anchor chain in the actual environment.

The invention discloses a silicon-based new material compressor fault detection method based on multi-source heterogeneous data, and the method comprises the steps: firstly obtaining normal historical operation data of vibration, pressure and temperature through a compressor state detection platform, carrying out the data preprocessing, and constructing a training set; then, a star-graffiti optimization algorithm is adopted to carry out joint optimization on variational modal decomposition (VMD) parameters and decompose signals, and modal features are extracted; secondly, realizing cross-channel adaptive fusion through convolutional neural network (CNN) multi-scale coding and introduction of channel and time attention; performing time sequence modeling on the fusion feature sequence by adopting a bidirectional long short-term memory network (BiLSTM), and training an anomaly detection model through training set data; and finally, inputting the test set data into the initial anomaly detection model through the same process, and realizing fault judgment by calculating the deviation from a normal state. And when the evaluation index of the test set reaches the standard, solidifying the model into an optimal anomaly detection model for on-line reasoning application of on-site real-time data.

Provided in the present disclosure is a data stream processing method. The method comprises: in response to the failure of a first load sharing link in a load sharing link group, determining an actual load sharing ratio of each second load sharing link among at least one second load sharing link, wherein the second load sharing link is a link in the load sharing link group which is in a normal state, and the actual load sharing ratio is used for representing an actual ratio of the second load sharing link in a forwarding table of the load sharing link group; on the basis of the actual load sharing ratio of each second load sharing link, determining at least one target load sharing link from among the at least one second load sharing link; and processing a data stream on the first load sharing link by means of the at least one target load sharing link. Further provided in the present disclosure are an electronic device, a computer-readable storage medium and a computer program product.

The invention provides a vehicle control method and device, electronic equipment and a storage medium, and the vehicle control method comprises the following steps: when it is detected that a user triggers a crawling mode activation instruction for a target vehicle, determining state detection information of the target vehicle; when the state detection information of the target vehicle represents that the vehicle is in a normal state, responding to the crawling mode activation instruction to enable the working mode of the target vehicle to be switched into a crawling mode; and when the state detection information of the target vehicle represents that the vehicle is in an abnormal state, refusing to respond to the crawling mode activation instruction. On one hand, when the vehicle is in the normal state, the crawling mode activation instruction can be normally responded, so that the vehicle can achieve the crawling mode, on the other hand, when the vehicle is in the abnormal state, the crawling mode activation instruction can be refused to be responded, a user is prevented from mistakenly touching the crawling mode, and therefore the driving safety of the vehicle is improved.

Disclosed are an intelligent monitoring method and system for a condenser vacuum system of a thermal power unit. The method comprises: collecting operating data of a condenser vacuum system, and performing preprocessing; extracting features from the preprocessed data, and performing anomaly recognition; and on the basis of an anomaly detection output result, adjusting operating parameters of the vacuum system. In the monitoring method and system provided in the present invention, temperature features reflect the heat exchange effect of a system, quality features reflect the operating load and cooling effect of the system, and vacuum system features reflect the overall operating state of the vacuum system; when an anomaly score S exceeds 0.3, the automatic adjustment of the system is realized; and an adjustment amount is calculated on the basis of the current anomaly score and the features, thereby ensuring that the system quickly restores to a normal state.

The invention provides a magnetic superconducting memory and a preparation method and a read-write method thereof. According to the magnetic superconducting memory, the superconducting layer maps the magnetization state by using the resistance difference between the superconducting state and the normal state; the ferromagnetic layer serves as a free layer capable of adjusting magnetization, and the magnitude of superconductive critical current of the superconductive layer is influenced through the magnetization direction. The heavy metal layer generates spin orbit torque through a spin Hall effect to drive the ferromagnetic layer to be magnetized and overturned; an infinite switching ratio between a superconducting state and a normal state is realized through a superconducting diode magnetoresistance effect, a high-efficiency and high-reliability nonvolatile storage scheme is realized in combination with a current-driven spin-orbit torque magnetization overturning technology, inherent defects of a traditional TMR are thoroughly overcome, and the signal contrast of a storage unit is remarkably improved; the heterostructure designed by the invention replaces a traditional magnetic tunnel junction, reduces material stacking and process complexity, and adopts a material compatible with a CMOS (Complementary Metal-Oxide-Semiconductor Transistor) process to promote large-scale integration of the low-temperature memory.

The invention discloses a phase deviation testing method based on a rotation test of a self-aligning rolling bearing, relates to the technical field of bearing detection, and solves the technical problems that potential risks in a normal state are lack of dynamic monitoring, a real-time operation state and a risk level are not combined, and the deviation between a prediction result and the reality is large. According to the method, by analyzing the stability of the periodic offset characteristic and the association rule of the periodic offset characteristic, the rotating speed and the load, an abnormal reason can be directly positioned, a basis is provided for targeted maintenance, blind shutdown and maintenance cost are reduced, and for a bearing in a normal state, the change rate of a key index is calculated in real time by establishing a phase-deviation-free reference database, so that the reliability of the bearing is improved. And comparing with a preset comparison value, so that a potential risk can be found before a fault occurs, precautions are taken in the bud, and a residual life calculation formula is dynamically corrected based on a risk early warning level in combination with a basic rated dynamic load, an actual equivalent dynamic load and a working condition correction coefficient, so that a prediction result is more fit with an actual operation state of equipment.

The invention discloses a coal gasification device key parameter diagnosis method and system based on digital twinning, and relates to the field of coal gasification device parameter diagnosis, and the method comprises the following operation steps: S1, multi-source data acquisition and intelligent preprocessing, S2, dynamic digital twinning hybrid modeling, S3, dynamic digital twinning hybrid modeling, S4, dynamic digital twinning hybrid modeling, and S5, dynamic digital twinning hybrid modeling. Constructing a dynamic digital twinborn body in which the mechanism model and the data driving model are deeply coupled; s3, performing real-time diagnosis on the health degree of the key parameters; s4, fault root cause tracing and model dynamic correction; and S5, optimizing the coal types. According to the coal gasification device key parameter diagnosis method and system based on the digital twinning, based on strong correlation parameter linkage verification of a function network topology library, the misjudgment rate of a single parameter can be greatly reduced; the graded response mechanism realizes stepped processing of digital twinborn optimization in a normal state, automatic fine adjustment of an early warning state and rapid alarm in an abnormal state, so that potential faults can be found in advance, and the problem of non-planned shutdown is avoided.

To provide a diagnostic device or the like for improving accuracy of gas leakage determination of a steam trap.SOLUTION: A diagnostic device includes: measurement means that measures vibration of a trap to be diagnosed and calculates first vibration data from measurement data; estimation means that estimates a drain amount of the trap to be diagnosed on the basis of the first vibration data; acquisition means that acquires a pressure value of the trap to be diagnosed; storage means that stores first relationship information in which a pressure value of the trap in a normal state in which there is no leakage of gas is associated with a determination threshold value, and second relationship information in which a pressure value of the trap in a state in which there is leakage of gas, and a drain amount is equal to or greater than a predetermined amount is associated with the determination threshold value; first determination means that specifies a determination threshold value based on the acquired pressure value on the basis of the first relationship information and performs leakage determination of the gas to be diagnosed on the basis of the first vibration data and the determination threshold value; and display means that displays a determination result by the first determination means and the drain amount. The first determination means executes the leakage determination again on the basis of the second relationship information in response to the satisfaction of a predetermined condition.SELECTED DRAWING: Figure 10

The invention relates to the technical field of sensors, and discloses a signal optimization method based on a diagnosability evaluation result, and the method comprises the following steps: S1, obtaining monitoring signal data collected by a plurality of candidate sensors in a normal state and a plurality of fault states of equipment, and S2, carrying out the optimization of the signals based on the monitoring signal data, according to the method, a fault detectability model is constructed, the fault detectability model is used for quantifying the difference between a fault state and a normal state on sensor signals, and when sensor signal optimization is carried out, the fault detectability model and the fault isolability model are constructed in a unified manner; and a detectability evaluation index and an isolability evaluation index are calculated based on a joint mean-variance difference value, so that comprehensive quantitative evaluation of the signal fault detection capability and the fault isolation capability is realized, and it is ensured that performance requirements of different dimensions of fault diagnosis in a signal optimization process are balanced. And the comprehensiveness and accuracy of fault diagnosis are improved.

The invention designs an equipment operation state identification and prediction method and device based on an improved W-GAN, the method and device are applied to intelligent prediction of the operation state of key equipment of a welding machine, the function of predictive maintenance facing the operation state of the key equipment of the welding machine is added in a platform, and the device comprises a data acquisition module, a construction module and a predictive maintenance module. The data acquisition module is used for collecting equipment operation state data through equipment such as a monocular camera and a monitoring system, and the equipment operation state data comprise data in a normal state and data in an abnormal state; the construction module is used for generating rich and vivid data by using a WGAN model and simulating various conditions possibly occurring in the equipment; the predictive maintenance module is a butt welding machine equipment state judgment module, and is used for carrying out maintenance in advance, monitoring the operation state of equipment in real time based on a network model prediction result, and more accurately judging when a fault occurs. If a fault hidden danger is found, an alarm or repair command is automatically triggered. The method is high in reliability, safety and efficiency.

The invention relates to a wirelessly usable sensor system, wherein the sensor system is suitable for being attached to the skin of a person and comprises the following components: (a) a measuring sensor which is suitable for measuring the oxygen saturation or the tissue perfusion of the skin covered by the sensor system and / or of the underlying tissue and outputting said oxygen saturation or tissue perfusion as a measured value; (b) an evaluation unit which is connected to the measuring sensor, is suitable for evaluating the measured value output by the measuring sensor, for comparing said measured value with a reference value and for outputting a signal if the measured value deviates from the reference value by a predetermined amount, and is suitable for learning the normal state of the tissue of a patient, for identifying patterns in the measured values measured by the measuring sensor by searching for repeating regularities in the sensor data and analyzing deviations from the determined normal state over a certain period of time, and for outputting a signal if the pattern deviates from a regularity; (c) a rechargeable energy store which supplies energy to the measuring sensor and to the evaluation unit and to further optional electronic components, and (d) a housing in which the components (a) to (c) and further optional electronic components are contained, wherein the housing is closed and impermeable to liquid such that the components (a) to (c) and further optional electronic components are protected from liquid. The sensor system is preferably used in medicine in decubitus prophylaxis.

A control unit includes plural control devices configured to control drive of an electric motor. The plural control devices have general functions used for operating the electric motor both during a normal state and during a fault state, the normal state being where the plural control devices is working properly, the fault state being where any one of the plural control devices is having a fault. The plural control devices have normal-time functions distributed among the plural control devices, the normal-time functions being for use during the normal state but unnecessary during the fault state.

The invention discloses an intelligent defect detection system and method applied to a PCBA (Printed Circuit Board Assembly), and relates to the technical field of big data analysis. By dividing mutually independent sub-detection areas, feature signal interference among different bonding pad areas is eliminated, and a detection range is accurately anchored; the first weight value is determined by counting the proportion of each characteristic exceeding threshold value of the abnormal bonding pad data, the second weight value is calculated by combining with the local time sequence sample data intercepted by the sliding window, and the dynamic weight value is obtained, so that the global historical data universality and the local time sequence data timeliness are balanced; the characteristic reference drift caused by technological parameter fluctuation and consumable batch difference is inhibited; an analysis threshold value is generated by calculating a feature floating value, a fine system of defect judgment and approaching abnormity early warning is constructed, the limitation of single-dimension judgment of a traditional fixed threshold value is broken through, samples which do not meet defect standards but deviate from a normal state are recognized, the problems of detection misjudgment and missed judgment are solved, and the precise quality control requirement of the dynamic production working condition of a production line is met.

The invention provides a node control method which can be applied to the fields of big data and distribution. The node control method comprises the following steps: acquiring state data uploaded by a first actuator and at least one second actuator respectively, the state data being generated by independently monitoring front nodes in one-to-one correspondence based on each actuator, the front nodes corresponding to the first actuator and the second actuator and the target system are in a connection activated state and a connection disabled state respectively; when it is detected that the state data uploaded by the first actuator is abnormal, a third actuator is determined from the second actuators with normal state data based on a preset performance evaluation rule or an operation instruction of a user; and controlling the first actuator to set the connection between the corresponding front node and the target system to be in a forbidden state, and controlling the third actuator to set the connection between the corresponding front node and the target system to be in an activated state. The invention further provides a node control method and device, equipment, a medium and a program product.

The invention discloses a surface-mounted permanent magnet synchronous motor short-circuit fault diagnosis method under a servo working condition, belongs to the field of servo motor fault diagnosis, and aims to solve the problems that harmonic-wave-based surface-mounted permanent magnet synchronous motor short-circuit fault characteristics under the servo working condition are influenced by working conditions, the diagnosis speed is low, and the accuracy is not high. The method comprises the following steps: 1, constructing a motor mathematical model of a servo working condition, deducing a q-axis current analytic expression, and calculating and generating a decoupled theoretical current characteristic signal based on a theoretical coordinate transformation relation; 2, collecting the three-phase current and the rotor position of the motor in real time, and extracting an actual current characteristic signal through a coordinate transformation chain including Clarke transformation, Park transformation, coordinate stretching transformation and Servo-Park transformation; and 3, calibrating the average value and the standard deviation of the actual characteristic signals in the normal state of the motor as a health reference. And 4, during online diagnosis, comparing an actual characteristic signal extracted in real time with a health reference, and carrying out fault judgment according to a preset statistical threshold value.

The control section 152 determines that the starting state is the normal state when the time TA is within a reference range decided based on a reference time decided in advance. The control section 152 determines that the starting state is the misalignment state when the time TA is outside the reference range. The time TA is the time from when the LB signal changes from the OFF state to the ON state until when the DZ signal changes from the ON state to the OFF state.

The embodiment of the invention provides a wind power master control PLC safe operation method and system based on fault diagnosis, and the method comprises the steps: building a multi-dimensional digital model which can represent the overall health state of a wind turbine generator under variable working conditions through deep analysis of massive historical operation data; specifically, the model evaluates the consistency between the actual running state vector of the current unit and the dynamic health domain defined by the model in real time. An anomaly score is generated by quantifying the degree of deviation between the actual state and the health baseline, any statistically significant deviation will be identified as a potential early fault signal. According to the method, normal state drifting and real fault germination under the extreme working condition can be intelligently distinguished from the system level, so that the problems of fault false report and fault missing report of a static threshold strategy are solved, and accurate early-stage fault early warning and grading are realized.

According to the technical scheme, the garbage can with the pressing ring comprises a can body and the pressing ring buckled on the top of the can body, and is characterized in that the top of the can body extends outwards to form an annular positioning table, the pressing ring comprises an inner side wall and an outer side wall, and a containing cavity allowing the top of the can body to be inserted is formed between the inner side wall and the outer side wall. The outer side wall of the pressing ring extends downwards to the position below the annular positioning table, the left outer side and the right outer side of the pressing ring extend out of mounting bases, transverse through holes are correspondingly formed in the positions, corresponding to the mounting bases, of the outer side wall of the pressing ring, and sliding blocks and compression springs are mounted in the mounting bases. Under the normal state, the front end of the lower sliding block penetrates through the transverse through hole under extrusion of the compression spring and then is clamped on the bottom surface of the annular positioning table for limiting; after the sliding blocks are pulled outwards, the sliding blocks overcome spring force to move outwards, the front ends of the sliding blocks retreat from the bottom face of the annular positioning table, and the sliding blocks and the pressing ring are lifted upwards to separate the pressing ring from the barrel body. When being fixed, the pressing ring can be locked on the garbage can body so as to press and fix the garbage can, and when being taken out, the pressing ring can be easily separated from the can body, so that a garbage bag can be conveniently taken out.

The utility model relates to the technical field of water meters, in particular to a rotary wing type water meter convenient to observe, which comprises a character wheel box, the character wheel box comprises a box body and a character wheel metering mechanism, the box body is provided with an inner cavity, the character wheel metering mechanism is arranged in the inner cavity, the character wheel metering mechanism comprises a rotatable character wheel set, the side wall of the character wheel set is provided with numbers, and the number of the character wheel set is larger than the number of the character wheel set. The character wheel box further comprises a cover body, a glass sealing cover and a turning cover, the cover body is provided with a reading face, a set of numbers on the side wall of the character wheel set are exposed on the reading face, the reading face is arranged in an inclined mode, the glass sealing cover seals a cavity opening of the inner cavity, the turning cover is installed on the box body in a rotating mode, and after the turning cover rotates by a preset angle, the turning cover covers or exposes the glass sealing cover. According to the utility model, the reading surface is arranged in an inclined manner, and the flip cover covers the glass sealing cover in a normal state, so that the advantages of reading in a side-looking manner and effectively preventing dust and stains from blocking the reading area are realized.

The invention discloses an online fault diagnosis method for a thermal resistor of a temperature measurement element, and the method comprises the following steps: S1, data collection and preprocessing: collecting a measurement resistance value (Rt) of the thermal resistor and a corresponding environment temperature (Tenv) in real time, and obtaining a stable resistance value (Rs) through digital filtering processing; s2, initial health state diagnosis: comparing the stable resistance value (Rs) with a theoretical resistance value (Rc) calculated based on the current environment temperature (Tenv), and if the deviation exceeds a first preset threshold value (delta R1), triggering an initial diagnosis process; and S3, preliminary fault diagnosis: starting a parallel diagnosis process comprising at least two different diagnosis sub-modules after triggering the preliminary diagnosis, not only detecting the occurrence of the fault, but also distinguishing possible types of the fault (such as slow response, continuous drifting, instantaneous interference and the like), and providing fault confidence, so that the fault diagnosis accuracy is improved, and the fault diagnosis accuracy is improved. And more accurate guidance is provided for maintenance decision making.