38results about How to "Easy fault detection" patented technology

The invention discloses an intelligent operation and maintenance network fault monitoring method, system and device and a medium, a communication network architecture is visualized through full-link topology reduction and real-time topology drawing, monitoring and maintenance are facilitated, and problems can be found and solved in time. Through combination of GIS and OTDR technologies, a communication optical cable fault point can be accurately positioned. The communication network architecture is visualized through full-link topology reduction and real-time topology drawing, monitoring and maintenance are facilitated, and problems can be found and solved in time; fault data in communication data can be quickly identified through real-time difference monitoring of communication data flow characteristics; by tracing the identified fault data and predicting the propagation path of the fault, multi-dimensional fault reason analysis and prediction can be carried out, the fault detection, positioning, tracing and prediction capabilities of the communication network are improved, and the intelligent operation and maintenance capability of the network is enhanced, so that the reliability of the network and the continuity of services are improved.

The invention discloses a bearing fault diagnosis method and device, and relates to the technical field of bearing fault diagnosis, and the method comprises a model training step, a model integration step and a fault diagnosis step, and the model training step provides a trained diagnosis model for the model integration step. The anomaly detection model output in the model integration step is used as the model algorithm input in the fault diagnosis step, and the model integration step is used for integrating a plurality of models to improve the diagnosis accuracy and the system robustness; the model integration step comprises the steps of multi-model integration, dynamic model selection and anomaly detection. According to the bearing fault diagnosis method, the model integration step is designed, the advantage integration function of multiple models is achieved, the limitation problem when a single model processes complex and changeable fault modes is solved, fault diagnosis is difficult to comprehensively and accurately conduct, the accuracy and robustness of a bearing fault diagnosis system are improved, and the fault diagnosis efficiency is improved. Fault detection under complex working conditions can be better handled, and the probability of false alarm and missing alarm is reduced.

The invention belongs to the technical field of unit fault diagnosis, and discloses a fault diagnosis method and system for a fault large-scale unit based on sharing and specific representation learning. The method not only can be applied to multi-measuring-point fault diagnosis of a large-scale unit, but also has wide applicability. In other multi-sensor systems or complex equipment, the fault detection effect can also be improved through multi-view representation learning. The invention provides a weighted sharing-specific multi-view depth representation learning fault diagnosis method for fault diagnosis of multi-measuring-point data of a large-scale unit. The method consists of two parts, namely sharing and specifically representing a learning network and weighted multiple similarity loss and orthogonal loss. A multi-measuring-point data fault diagnosis problem of a large-scale unit is solved by sharing a specific representation learning network, and weighted multiple similarity loss and orthogonal loss are used to further improve the feature extraction capability of the network and reduce redundancy.

The invention discloses a health state self-diagnosis device and method of a compressed air foam fire extinguishing system, and belongs to the technical field of fire fighting, and the health state self-diagnosis device of the compressed air foam fire extinguishing system comprises a fire extinguishing device, a control device assembly and a collector assembly, a collector assembly and a control device assembly are both arranged in the fire extinguishing device, the collector assembly is responsible for monitoring key parameters of the fire extinguishing device in real time, the collector assembly is in circuit connection with the control device assembly, the control device assembly comprises a data collection system and a processing system, the data collection system is in circuit connection with the collector assembly, and the processing system is in circuit connection with the collector assembly. The data collection system is used for collecting real-time data of all elements of the detection device, the data collection system is connected with the processing system, and the processing system processes the collected data, monitors abnormal conditions of the device, gives an alarm in time, and can perform real-time feedback correction and risk assessment.

The invention provides a fault detection method and device for a transformer bushing, equipment and a storage medium. The method comprises the following steps: acquiring an infrared image of a bushing to be detected; inputting the infrared image into a pre-trained casing pipe fault detection model to identify the fault type of the to-be-detected casing pipe; wherein the casing pipe fault detection model comprises an image segmentation module and a fault detection module, the fault detection module is constructed based on a visual Transform module, the image segmentation module is obtained by improving the uncertainty and gradient of pixels in a Point Rend module, the image segmentation module is used for performing contour segmentation processing on an infrared image of a casing pipe to be detected, and the fault detection module is used for detecting the casing pipe to be detected. And the fault detection module is used for detecting the contour image. According to the invention, the detection accuracy of the sleeve can be improved.

According to the aircraft front wheel turning control valve fault detection method, a fault detection program resides in a turning control unit, and the fault detection program comprises a fault detection condition judgment module and a fault detection instruction sending module. The fault detection condition judgment module is used for receiving a pressure signal of a hydraulic system, a wheel load signal of an undercarriage folding and unfolding system, a speed signal of a brake system, a turning release signal of a turning release switch and a fault signal of a front wheel turning system; the fault detection instruction sending module sends a turn-off instruction to a turn-off valve of the turning control valve and sends a turning instruction to a servo valve of the turning control valve, if the servo valve cannot control the action of a turning mechanism and enables front wheels not to deflect, it is indicated that the turn-off valve is in a normal working state, and if the servo valve controls the action of the turning mechanism and enables the front wheels to deflect, it is indicated that the turn-off valve is in a normal working state. And indicating that the shut-off valve is in a hidden fault state.

The invention provides a fault detection method and a related product, and relates to the technical field of fault detection. The fault detection method comprises the steps of obtaining current state information of a to-be-detected system in a current time period, wherein the current state information comprises at least one of a current operation condition, configuration, performance and a health state of the to-be-detected system in the current time period; predicting a fault injection action of the to-be-detected system according to the current state information of the to-be-detected system so as to generate a fault scene through the fault injection action; and determining that the to-be-detected system has a fault in the fault scene. According to the embodiment of the invention, the system fault detection speed and accuracy can be improved.

The invention belongs to the technical field of heating ventilation air conditioner fault detection, and discloses a water chilling unit fault detection method and system based on working condition and fault feature separation, and the method comprises the steps: collecting the operation data of a water chilling unit; working condition features and fault features of the training data are extracted and decoded into a working condition mode and a fault mode, operation data are reconstructed, a loss function is defined, and the model is trained; fault features of normal operation data and test data in the training data are extracted, low-dimensional flow patterns of the normal operation data and the test data are extracted, the fault features are reconstructed, reconstruction errors are calculated, distribution of the low-dimensional flow patterns and distribution of the reconstruction errors are fitted respectively, and fault detection indexes are calculated; comparing whether the fault detection index value exceeds a threshold value or not, and judging whether the water chilling unit breaks down or not. The method fully considers the influence of the working condition on the fault features, can extract the complete fault features from the operation data of the water chilling unit and eliminate the working condition features, and improves the fault detection performance of the model in an unknown variable working condition scene.

This invention discloses a unified management method and system for provincial and municipal station dispatching, relating to the field of integrated operation and management technology. The method includes: receiving data from provincial and municipal station monitoring equipment, including timestamps, event codes, and associated parameters; analyzing the data to identify data fluctuations; and filtering potential fault signals by comparing historical patterns to obtain initial fault screening results. The unified management method for provincial and municipal station dispatching provided by this invention achieves early identification of fault signals and accurate classification of abnormal patterns by receiving and analyzing data from monitoring equipment. It improves the efficiency and accuracy of data analysis by comparing real-time and historical data, accelerates fault detection and enhances the handling capability of complex abnormal situations by identifying data changes in different time periods, sets alarm response times and processing priorities for different anomaly types to improve emergency response efficiency, optimizes network traffic management strategies and data transmission paths, reduces processing latency, improves network performance and stability, and effectively enhances operational efficiency and user experience.

The invention discloses a PT turn-to-turn short circuit online detection method and system based on a Lissajous figure, and the method comprises the following steps: S1, collecting PT primary side voltage # imgabs0 # and PT secondary side voltage # imgabs1 # and PT secondary side current signals # imgabs2 #; S2, calculating the characteristic parameters of the Lissajous figure according to the collected PT primary side voltage, PT secondary side voltage and PT secondary side current signals; the characteristic parameters comprise a long axis a, a short axis b and an inclination angle beta; s3, judging whether a set turn-to-turn short circuit rule is met or not according to the calculated characteristic parameters of the Lissajous figure; s4, if the set turn-to-turn short circuit rule is met, a PT primary side switch is switched off, and a PT turn-to-turn short circuit alarm signal is sent out; otherwise, turning to the step S1. According to the invention, the problem that the traditional detection method cannot accurately monitor the PT turn-to-turn short circuit fault in real time is solved.

The invention discloses a vehicle multi-screen combined display method and system, a vehicle and a storage medium, and the method comprises the steps: controlling an instrument screen to display an image processed by a micro-control unit in response to a vehicle starting signal; after the system-on-chip is successfully started and display content is output to the instrument screen, the instrument screen is controlled to be switched to an image output by the system-on-chip, and the instrument screen is connected with the micro-control unit and the system-on-chip at the same time; and continuously monitoring the state of the system-on-chip by using the micro-control unit, and controlling the instrument screen to switch back to the display content output by the control unit when the system-on-chip fails or is stuck. According to the method and the device, the technical problem that an instrument does not display content directly when an SOC (System On Chip) fails in the prior art is solved.

The invention discloses a data acquisition method and system for a sensor network, and relates to the technical field of sensor networks, and the method comprises the steps: constructing a standard change formula and extracting a standard data set for calculation, and can respectively calculate the standard change rate V of each processing equipment state parameter. And on the basis, when the calculated standard change rate Vi (t) exceeds a set change rate threshold value Vth, the system judges that equipment operation is abnormal, and an event triggering stage is entered. At the stage, the operation frequency of the sensor module can be increased by 50%, so that more abnormal state parameters such as power fluctuation P, vibration frequency F and noise spectrum S are collected. And abnormal characteristic values Yc are calculated for the abnormal state parameters and are summarized into an abnormal characteristic set O, so that the identification capability of the system on equipment abnormity is further enhanced, potential faults are found in time, a basis is provided for a subsequent fault response stage, and the risk of fault occurrence is reduced.

The present invention relates to the field of image processing technology, and specifically discloses a sub-pixel edge detection method, including: a coordinate system establishment step, establishing a coordinate system in the collected image information; image preprocessing, performing image preprocessing on the image information to obtain a target image; an edge detection step, performing edge detection on the laser spot and the marking circle in the target image respectively, obtaining the spot edge information and the marking circle edge information; a position calculation step, calculating the spot position information of the laser spot and the marking circle position information of the marking circle in the coordinate system according to the spot edge information and the marking circle edge information; an offset calculation step, calculating the position offset of the laser spot moving to the marking circle according to the spot position information and the marking circle position information; and a correction step, controlling and adjusting the laser spot position projected by the laser cross-section detector according to the position offset. The alignment accuracy of the laser spot and the marking circle is higher when the technical solution of the present invention is adopted.

The utility model discloses a portable multifunctional machine tool fault detection device, and relates to the technical field of machine tool maintenance. The device comprises a mounting plate, a detection assembly is arranged on the upper surface of the mounting plate, and a grip assembly is further arranged on the lower surface of the mounting plate; the detection assembly comprises a detection shell fixedly connected to the upper surface of the mounting plate. According to the utility model, the odor sensor and the visual camera in the detection shell are used for carrying out fault detection on the machine tool in various modes, and the driving rack drives the gas collecting hood to rotate in a reciprocating manner, so that air on the machine tool is guided into the detection shell through the gas collecting hood for detection; the problems that in the prior art, the function is single, and fault detection cannot be conveniently conducted on the interior of the machine tool are solved, meanwhile, the mounting plate and the detection structure are conveniently held through the holding rod, the position and angle of the detection structure are adjusted through the second lead screw, the second U-shaped frame and the third U-shaped frame, and the problem that in the prior art, carrying is not convenient is solved.

The application discloses a kind of long and narrow channel-oriented wireless access device and method, device includes a plurality of sequentially connected access point units, access point unit includes: directional antenna, for realizing the signal coverage of specified channel area;Photoelectric converter group, for completing the photoelectric / electro-optical conversion of serial signal;Environment sensor, for monitoring the environment in channel;Central processing unit, for receiving, processing and forwarding data;External interface, for the connection of host computer and central processing unit, realizes data transceiving;Access point unit is connected by photoelectric converter group each other.The long and narrow channel of the application is split into multiple sections, each long and narrow channel is covered by directional antenna on wireless access device, and wireless access device is connected by optical fiber, supports multiple connection modes, designs special network configuration frame, solves the problem that existing wireless router or base station is high in cost and poor in reliability in long and narrow channel environment.

The invention relates to the field of support stability testing, and provides a stability testing method and system based on a cable support, and the method comprises the steps: recognizing components, including a support body, a fixing unit, a connecting unit and a clamp, of a sample support, judging a bearing point and a supporting point according to the geometrical characteristics of the support body, and calculating the bearing capacity of the support body; querying a fixing element corresponding to the fixing unit, testing the stress condition of the fixing element through a fixing testing machine, extracting characteristic parameters to calculate the fixing force, obtaining an accessory unit table corresponding to the connecting unit, selecting the connecting element to calculate the connecting force, determining the type of the clamp, and extracting application characteristics to calculate the clamping force. And constructing a support structure model based on the bearing force, the fixing force, the connecting force and the clamping force, performing state analysis on the sample support to obtain a current state, performing interference removal processing on the current state, compensating an inclination component and an overall offset, and calculating a stability test value. The method can improve the stability of the cable support.

The invention relates to the technical field of hydropower construction, provides a feeding device and method for water conservancy and hydropower construction, and solves the problem that an existing feeding control method cannot be adaptively adjusted according to field conditions and construction requirements. The system comprises five cooperative work units including a data acquisition unit, a task scheduling unit, a task execution unit, a task detection unit and a task scheduling optimization unit, and a mobile feeding device, a buffer device and a concrete feeding device can be accurately controlled. And through clustering analysis and a model optimization technology, a feeding strategy is dynamically adjusted, so that efficient execution of a feeding task is ensured. The use efficiency of the feeding device for water conservancy and hydropower construction in the water conservancy and hydropower construction process is remarkably improved.

The invention relates to the technical field of direct-current power supply detection, and discloses a direct-current power supply instantaneous grounding fault searching system which realizes accurate acquisition, preprocessing, calculation, analysis and feedback of current data through close cooperation of a plurality of modules. The system not only can provide more comprehensive and accurate data support, but also has real-time performance and flexibility, accurate reference coefficient calculation, a dynamic alarm mechanism and a real-time feedback mechanism enable the system to quickly identify and position faults, delay response, false alarm or missing alarm in a traditional method are avoided, and compared with the prior art, the system has the advantages that the system is easy to popularize and use. According to the system, the fault positioning capacity, the data processing capacity and the response speed are remarkably improved, the stability, the reliability and the fault response capacity of the system are greatly enhanced, and the overall operation efficiency of the power system is optimized.

The application discloses a method for detecting equipment failure, a related device and system. In the method, when an electronic device fails, another electronic device can be used to detect the failure of the electronic device. The method has low requirements for other electronic devices that fail, and can accurately and quickly detect the failure even if the detected electronic device has limited running space, does not have self-checking capability, is not configured with an input device, or is large in size and not easy to carry. The method is suitable for any type of electronic device, can conveniently detect failure, and ordinary users can independently initiate failure detection, thereby improving operability and convenience, reducing the dependence of users on after-sales service, and improving user experience.

The invention discloses an air conditioning system detection method and device, a program product, a medium and an air conditioning system.The air conditioning system comprises at least two outdoor heat exchange modules, each outdoor heat exchange module comprises a heat exchanger, and the method comprises the steps that temperature data of all the heat exchangers before the air conditioning system operates stably are obtained; and based on the temperature data of all the heat exchangers, fault detection is conducted on an outdoor heat exchange module of the air conditioning system. According to the technical scheme, the fault detection capacity of the air conditioning system can be improved.

The invention relates to a block storage component fault detection method and device, computer equipment, a computer readable storage medium and a computer program product, and is applied to the technical field of big data, and the method comprises the following steps: obtaining real-time operation data of a block storage component, and obtaining historical operation data of the block storage component; according to the real-time operation data and the historical operation data, the operation condition of the real-time operation data is identified, and the operation condition type of the real-time operation data is obtained; if the running state type represents that the block storage component has a fault, screening a service instruction corresponding to the block storage component and execution data for the service instruction from the real-time running data according to semantic information of the real-time running data; and detecting a fault type of the block storage component according to the service instruction and the execution data to obtain a component fault type. By adopting the method, the fault detection efficiency of the block storage component can be improved.

The invention discloses a province, region and station scheduling unified management method and system, and relates to the technical field of integrated operation management, and the method comprises the steps: receiving province, region and station monitoring equipment data, including timestamps, event codes and associated parameters, analyzing the data, recognizing data fluctuation, and obtaining a fault preliminary screening result by comparing historical modes and screening potential fault signals. According to the province, region and station scheduling unified management method, early recognition of fault signals and accurate classification of abnormal modes are achieved by receiving and analyzing the data of the monitoring equipment, the efficiency and accuracy of data analysis are improved through comparison of real-time data and historical data, and by recognizing data changes of differentiated time periods, the accuracy of data analysis is improved. Fault detection is accelerated, the processing capability of complex abnormal conditions is enhanced, alarm response time and processing priorities are set for abnormal types, emergency response efficiency is improved, a network flow management strategy and a data transmission path are optimized, processing delay is reduced, network performance and stability are improved, and operation efficiency and user experience are effectively improved.

The invention relates to the technical field of wind power generation, in particular to a fault detection method and system for a wind driven generator based on deep learning. According to the wind driven generator fault detection method based on deep learning, a network model is constructed by introducing a bidirectional long-short-term memory network and a Softmax classifier through an improved convolutional neural network, and model pre-training is carried out based on operation historical data and a fault type so as to obtain a pre-trained fault detection network model. Introducing a transfer learning mechanism training model to obtain a trained fault detection network model; the trained fault detection network model can more comprehensively and accurately capture fault features by extracting multi-scale local features and carrying out time sequence feature learning, so that the fault detection precision is improved, and misinformation and missing report conditions are reduced. Meanwhile, by introducing a transfer learning mechanism, the model can quickly adapt to different types of wind driven generators.

The present invention discloses a memory fault detection method for an uncertain networked system under random saturation, the method comprising the following steps: step 1, establishing an uncertain networked system model with random sensor saturation; step 2, designing a memory-based fault detection filter and obtaining a residual dynamic system; step 3, based on Finsler's lemma, obtaining a residual system that is finite-time stable and satisfies H ∞ The judgment basis of performance index constraint; Step 4, solve the fault detection filter gain matrix by the judgment basis and matrix inequality method; Step 5, substitute the filter gain matrix into the fault detection filter to obtain the residual; Step 6, construct a fault detection mechanism with residual evaluation function and threshold, and judge whether the fault occurs by calculating the residual evaluation function and threshold. This method can reduce the impact of saturation and uncertainty on the performance of the fault detection filter and detect the occurrence of faults more quickly.

The utility model provides electric proportional valve equipment convenient for troubleshooting. The electric proportional valve equipment comprises a valve body, integrated components and a liquid flow channel, the valve element is arranged in the valve body pressure measuring sealing cover and is in sliding connection with the liquid channel. The spring cover assembly is detachably connected with one side of the valve body, and a spring seat is arranged in the spring cover assembly and matched with a spring set to be connected with the valve element. The displacement measuring assembly can replace the spring cover assembly to be connected with the valve body and is used for detecting the displacement of the valve element; and the proportional electromagnetic valve is arranged at the bottom of the valve body and is used for controlling the movement of the valve core. The equipment is used for solving the technical problems that the displacement of a main valve element is difficult to test in the application of an existing electric proportional valve structure, and if a control system cannot sense the actual displacement of the main valve element, internal faults cannot be eliminated and detected after a valve body breaks down.

The present invention belongs to the field of deep learning and relates to a system multi-fault detection method based on big data. The training process of the detection model includes: obtaining historical data to obtain an initial data set; obtaining the fault source, and deriving features of the initial data set according to the fault source to obtain a structured data set; constructing a dynamic label relationship graph according to the structured data set, and inputting the dynamic label relationship graph into a dynamic graph neural network to obtain label semantic embedding; obtaining a feature representation of each fault label according to the label semantic embedding and a feature vector; obtaining a classification result according to the feature representation of the fault label; calculating a loss function value according to the classification result and the label semantic embedding, and updating the model parameters according to the loss function value until the model training is completed; the present invention updates the node features of the dynamic label relationship graph according to the dynamic graph neural network to obtain the label semantic embedding, thereby capturing the topological relationship in the fault label space while capturing the time dependency between the fault labels.

The present invention provides a method for tracing the quality anomaly of a sintering process, and relates to the technical field of sintering process. The method comprises establishing a stationary feature module for extracting the quality of the sintering process, using the collected normal sintering process data as a training set, training the stationary feature module for extracting the quality of the sintering process to obtain the stationary component of the training set and its covariance matrix, and the FeO content prediction submodule obtains the predicted value of the FeO content according to the stationary component of the training set; using the sintering process data monitored online as a sample set, processing the sample set according to the stationary feature extraction submodule based on a twin neural network to obtain the stationary component of the sample set; judging whether the sample set has abnormal data according to the stationary component and covariance matrix of the sample set, and if there is abnormal data, drawing a contribution graph, the present invention takes into account the non-stationarity, nonlinearity and non-Gaussianity of the sintering process data, extracts the stationary features related to the FeO content, and improves the fault detection performance.

The invention discloses an active early fault detection method for a high-speed train suspension system. The active early fault detection method comprises the following steps of 1, performing data modeling of an actual model and a nominal model of the high-speed train suspension system by utilizing T-S fuzzy; 2, constructing an input and output data matrix of data modeling; 3, designing a ToMFIR residual error according to the data model and the input and output data matrix; 4, constructing an auxiliary signal and an auxiliary signal distributor, and optimizing the ToMFIR residual error through the auxiliary signal and the auxiliary signal distributor; and 5, designing an evaluation function, and detecting a fault through the ToMFIR residual obtained in the step 4 and the evaluation function. By designing the auxiliary signal and the auxiliary signal distributor and injecting the auxiliary signal and the auxiliary signal distributor into the system, the ToMFIR residual error contains more fault information, so that the fault sensing capability of the system is improved.

The invention relates to the technical field of power system automation, and provides a feeder terminal device with dual-mode zero-sequence current protection, which combines two modes of traditional zero-sequence current transformer detection and zero-sequence current detection based on three-phase current calculation, compares and verifies the results of the two zero-sequence current calculation modes, and provides a feeder terminal device with dual-mode zero-sequence current protection. The zero-sequence current fault in the power system is accurately protected and quickly responded, and the accuracy and reliability of protection are improved. According to the invention, the transient method is utilized to judge the single-phase earth fault, and the fault detection capability of the feeder terminal device is further enhanced.

The invention discloses a ViT cross-domain fault diagnosis method combining adaptive space attention and patch feature weighting. The ViT cross-domain fault diagnosis method is specifically implemented according to the following steps: step 1, acquiring original vibration fault signals of different fault types from a public data set; step 2, preprocessing the original vibration fault signal obtained in the step 1; step 3, establishing a CpcsViT model for fault detection of the mechanical equipment; 4, inputting the original vibration fault signal preprocessed in the step 2 into the CpcsViT model established in the step 3, setting training parameters, and training the CpcsViT model; and 5, performing fault diagnosis by using the trained model. According to the invention, the problem of low detection accuracy in a mechanical equipment fault detection task in the prior art is solved.