204 results about "System diagnosis" patented technology

The invention provides an integrated test scene prediction method and system based on multi-dimensional data, and the method comprises the steps: carrying out the multi-source data collection of an integrated test scene based on a dynamic sampling strategy, obtaining integrated test multi-dimensional data, carrying out the multi-dimensional data preprocessing of the integrated test multi-dimensional data, and obtaining a pre-trained multi-dimensional data analysis model; inputting the pre-processed integrated test multi-dimensional data into the multi-dimensional data analysis model to carry out sub-module data analysis, obtaining a pre-trained intelligent regulation and control model, importing a system diagnosis report and a key index deviation degree report into the intelligent regulation and control model as inputs, generating an intelligent regulation and control decision, and carrying out intelligent regulation and control. By means of the method, system faults, performance bottlenecks and potential problems occurring in the integrated test scene can be accurately predicted, intelligent regulation and control decisions are provided for solving the problems, and accurate system performance evaluation and optimization services can be provided for users.

The invention relates to a sonar fault intelligent diagnosis method and system based on a knowledge graph and a large language model. The sonar fault intelligent diagnosis method comprises the following steps: receiving an initial fault description text input by a user through a natural language interaction interface; carrying out integrity analysis on the initial fault description text, and judging whether the initial fault description text contains all preset key information dimensions or not; if the judgment result of the integrity analysis is no, starting an interactive guide mechanism until a complete fault description text is obtained; based on the complete fault description text, performing reasoning in a pre-trained large language model configured with a sonar fault knowledge graph; and based on the conclusion of the joint reasoning, generating and outputting a structured fault diagnosis report. According to the invention, by introducing a fault description integrity evaluation and interaction guide mechanism, the problem that the system diagnosis accuracy is reduced due to fuzzy or incomplete fault description input by a user in the prior art is solved, and the accuracy and stability of overall diagnosis are improved.

The invention discloses an intelligent vibration analysis and fault diagnosis method, and particularly relates to the technical field of rotating machinery state monitoring and fault diagnosis. Collecting a signal through a vibration sensor and carrying out standardization preprocessing; performing parallel analysis on the preprocessed signal by using the constructed multi-scale feature extraction and fusion network, extracting low-frequency trend and high-frequency detail features, and fusing the low-frequency trend and high-frequency detail features to generate a comprehensive feature vector; inputting the vector into a classifier based on an attention mechanism to carry out preliminary fault identification; meanwhile, performing similarity matching on the comprehensive feature vector of the current case and a fault knowledge base in which historical cases are stored; and finally, an enhanced diagnosis conclusion is generated by fusing an output result of the classifier and matched historical case experience, and a new case subjected to field verification is continuously updated to the knowledge base. According to the method, adaptive extraction of deep features of the vibration signals, interpretability enhancement of a diagnosis decision process and continuous evolution of system diagnosis knowledge are realized.

A method includes receiving an indication of an inspection of an industrial automation device being triggered, displaying, via a display of a mobile computing device, instructions for performing the inspection of the industrial automation device, receiving, via one or more sensors of the mobile computing device, one or more sensors communicatively coupled to the mobile computing device, or both, inspection data associated with the industrial automation device, processing the inspection data associated with the industrial automation device to generate results of the inspection of the industrial automation device, and displaying, via the display of the mobile computing device, the results of the inspection of the industrial automation device.

The invention relates to a thermal power plant fault early warning and diagnosis method based on a nebula system, and the method comprises the steps: collecting the multi-dimensional operation time sequence data of a thermal power plant, carrying out the feature extraction and lexical element processing of the multi-dimensional operation time sequence data through an encoder, and obtaining a unified equipment state lexical element sequence; based on the equipment state lexical element sequence, constructing a dynamic star map representing the operation state of the whole power plant; inputting the dynamic star map into a space-time fusion backbone network; the space-time fusion backbone network performs iterative processing on the dynamic star map and generates a health degree attenuation trajectory; when the slope of the health degree attenuation trajectory exceeds a preset threshold value, dynamic early warning and system diagnosis are triggered, and a natural language diagnosis report containing a causal reasoning chain is generated; new multi-dimensional operation time sequence data are collected in real time, and an incremental learning algorithm is used to update the encoder and the space-time fusion backbone network online; compared with the prior art, the system can continuously adapt to working condition changes and has high self-optimization capacity.

The invention belongs to the technical field of fault diagnosis, and particularly discloses a numerical control system fault diagnosis system and method fusing forward design and reverse case data, the numerical control system fault diagnosis system comprises a knowledge base and a large model, the knowledge base is used for determining cue words based on user questions, and the large model is used for outputting fault diagnosis results according to the cue words; the knowledge base is a fault diagnosis knowledge graph, a knowledge graph entity is extracted from forward design data and reverse case data of the numerical control system, the forward design data comprises a PLC file and a bottom layer alarm file of the numerical control system, and the reverse case data comprises historical fault diagnosis work order information of the numerical control system. According to the method, the interactivity and flexibility of the knowledge base can be improved, the multi-round interaction capability in the fault diagnosis process is optimized, and the method is of great significance in improving the system diagnosis efficiency and accuracy.

The invention discloses a heat supply energy consumption diagnosis and analysis system fusing a big language model and cloud-mist edge collaboration, and the system comprises an edge processing layer which is used for monitoring the energy consumption related data and heat supply operation data of a heat supply system diagnosis object; the fog processing layer is used for extracting strong correlation data characteristics influencing energy consumption and establishing an energy consumption prediction model of a heat supply system diagnosis object; the analysis module is also used for establishing an energy consumption anomaly analysis knowledge graph according to the heat supply operation data and energy consumption anomaly analysis mechanism knowledge; the cloud processing layer is used for training a heat supply system energy consumption abnormity diagnosis large model according to the energy consumption predicted value of the heat supply system diagnosis object in combination with a preset energy consumption diagnosis standard value; and the server is also used for obtaining an energy consumption abnormality diagnosis type and an abnormality diagnosis object according to the heat supply system energy consumption abnormality diagnosis large model, performing question and answer intention recognition and constructing an energy consumption abnormality analysis question and answer agent based on the large language model in combination with the energy consumption abnormality analysis knowledge graph, and generating an energy consumption abnormality analysis and energy-saving recommendation scheme answer.

The invention relates to the technical field of computer and intelligent mine program operation and maintenance, in particular to an operation and maintenance system and method for mine program management and state monitoring. A database operation and maintenance module, a port monitoring module, a service management module, a system diagnosis module, a subscription data publishing and tracking module, a distributed service detection module and a data management module are fused, so that the capability of monitoring general configuration or states of machines and services is realized, and data formats are unified; and fault diagnosis and troubleshooting are carried out on modules with problems rapidly. According to the application, various operations of related applications of the intelligent mine platform in a whole business process period such as deployment, updating, troubleshooting and configuration information updating are fused, and the method is very suitable for deployment maintenance and troubleshooting operation and maintenance of deployment personnel and developers on the intelligent mine application in a mine scene.

The invention discloses a digital twinning-based fault diagnosis system, diagnosis method and application, and the system comprises a hardware platform, a digital twinning fault diagnosis model and a cloud, and the hardware platform is used for collecting vibration, temperature and sound signals during the operation of equipment; the cloud is used for receiving data sent by the hardware platform and the controller and sending a fault state; the digital twinning fault diagnosis model comprises a physical data input layer, a digital twinning modeling layer, a virtual-real data fusion layer, a causal reasoning layer and an output layer, data collected by the hardware platform is input into the physical data input layer, modeling is carried out on key components of equipment through the digital twinning modeling layer, virtual fault data is generated, and the virtual fault data is sent to the hardware platform; and according to the virtual-real data fusion layer and the causal reasoning layer, distributing weights to each branch, and displaying a fault diagnosis result through an output layer. The invention can be applied to refrigeration equipment. According to the invention, fault prediction is carried out in advance, the fault type can be identified, the labor cost and the time cost are saved, and the identification accuracy is improved.

The invention belongs to the technical field of PH detection, particularly relates to a sauce PH detection technology, and particularly discloses an online PH value detector based on artificial intelligence, sauce distribution uniformity monitoring is synchronously achieved in the sauce pH detection process, vibration control is implemented based on sauce distribution uniformity feedback, it is ensured that detection is in a full mixing state, and the detection accuracy is improved. Meanwhile, through linkage analysis of pH sudden change recognition and the uniformity state, process disturbance and probe faults can be effectively distinguished under the continuous monitoring condition, false alarm and invalid maintenance are avoided, the intelligent level of system diagnosis is improved, and stable and reliable online pH detection is achieved; and meanwhile, liquid level offset monitoring is introduced when vibration control is implemented based on sauce uniformity feedback, and vibration adjustment is performed in combination with a dynamic coupling relationship between a uniformity index and liquid level offset, so that stirring is ensured to operate within a structural safety range, excessive vibration is avoided, and the system stability and operation safety are improved.

1. Name of the Design Product: Graphical User Interface for Controlling Numerical Control Machine Tools of Electronic Devices. 2. Use of the Design Product: Display information and run programs. 3. Design Key Points of the Design Product: Lie in the graphical user interface in the electronic device. 4. Picture or Photograph Best Showing the Design Key Points: Front View. 5. Use of the Graphical User Interface: Display the numerical control machine tool operation interface. 6. Human-Machine Interaction Method of the Graphical User Interface: The front view is the login interface. After entering the user name and password and clicking "Login", it enters the variant state diagram 1; clicking "Load File F1" enters the variant state diagram 2; clicking "Program View F2" enters the variant state diagram 3; clicking "Start Option" enters the variant state diagram 4; clicking "Tool Selection" enters the variant state diagram 5; clicking "Pattern Selection" enters the variant state diagram 6; clicking "Free Box Selection" enters the variant state diagram 7; clicking "Program Zero Point" enters the variant state diagram 8; clicking "Upper PIN" enters the variant state diagram 9; clicking "Settings" enters the variant state diagram 10; clicking "Forming Analysis" enters the variant state diagram 11; clicking "Errors and Warnings" enters the variant state diagram 12; clicking "System Diagnosis F3" enters the variant state diagram 13; clicking "Input" enters the variant state diagram 14; clicking "Output" enters the variant state diagram 15; clicking "Tool Management F11" enters the variant state diagram 16; clicking "Parameters" enters the variant state diagram 17; clicking "Dynamic Parameters" enters the variant state diagram 18; clicking "Tolerance" enters the variant state diagram 19; clicking "Tool Measurement" enters the variant state diagram 20; clicking "Broken Tool" enters the variant state diagram 21; clicking "Diameter Table" enters the variant state diagram 22; clicking "Index Table" enters the variant state diagram 23; clicking "Tool Length Calibration" enters the variant state diagram 24; clicking "Tool Diameter Calibration" enters the variant state diagram 25; clicking "Job Interface F5" enters the variant state diagram 26; clicking "Custom Function" enters the variant state diagram 27; clicking "Incremental Key" enters the variant state diagram 28; clicking "Z Plane" enters the variant state diagram 29; clicking "Processing Status" enters the variant state diagram 30; clicking "Mark" enters the variant state diagram 31; clicking "User Mark" enters the variant state diagram 32; clicking "Shift" enters the variant state diagram 33.

The invention discloses an AI-based power distribution equipment partial discharge online diagnosis and positioning method, which relates to the technical field of equipment detection, and comprises the following steps: acquiring high-frequency signals in power distribution equipment in real time, sequentially calculating a topological inconsistency index, an echoing characteristic response abnormal index and a burst pulse ring chaos index, and calculating a false index based on the three results, so as to realize the partial discharge online diagnosis and positioning of the power distribution equipment. The method is used for judging whether the signal is a real signal generated by partial discharge of equipment. And if the false index is lower than a threshold value, determining that the discharge signal is a real discharge signal, and performing type identification and accurate positioning through a preset AI model. According to the method, non-discharge interference signals can be effectively identified, the fault judgment accuracy is improved, the false alarm rate is reduced, the trust of operation and maintenance personnel on a system diagnosis result is enhanced, the manual inspection pressure is relieved, and the maintenance cost is reduced.

The invention discloses a grid-connected inverter IGBT (Insulated Gate Bipolar Translator) fault diagnosis method and a grid-connected inverter IGBT fault diagnosis system based on a combined observer, and aims to improve the fault diagnosis robustness and accuracy of a switching device by an inverter system. Inverter inductive current, filter capacitor voltage, power grid inductive current and total disturbance including IGBT fault effect are considered; then, designing and operating a superhelix total disturbance observer, and estimating and separating the total disturbance of the system in real time; disturbance compensation is carried out through an estimation result of the interval sliding mode observer, and interval estimation of a system state and system output is generated; and finally, realizing IGBT fault diagnosis by combining two criteria of disturbance estimation deviation and state interval consistency. According to the method, the problems that a traditional method is poor in disturbance resistance and prone to false alarm and missing alarm are effectively solved, the stability, accuracy and reliability of a diagnosis system are enhanced, and the method is suitable for photovoltaic grid-connected inverter IGBT fault detection and diagnosis under the complex operation condition.

A method for operating and diagnosing faults in a laboratory instrument comprising a plurality of subsystems can include conducting an analysis sequence and a set of diagnostic steps. Such a method can be conducted using diagnostic reagents that contain paramagnetic particles but lack an antibody component. Such a method can also include evaluating a set of instrument subsystems in an order that is opposite to the order in which the subsystems are used during an analysis.

A method for heating, ventilation, and air conditioning (HVAC) system diagnostics includes sending a first instruction to a thermostat to shut down an HVAC system. A user is instructed to minimize background noise. A second instruction is sent to the thermostat to turn on the HVAC system. A third instruction is sent to the thermostat to set a temperature setpoint below or above a value of a room temperature. Outdoor unit sound data is captured for a second time period. The baseline sound data is subtracted from the outdoor unit sound data to determine normalized outdoor unit sound data. Expected sound signatures of the outdoor unit are identified. The normalized outdoor unit sound data is compared to the expected sound signatures. In response to determining that an expected sound signature for a compressor is missing from the normalized outdoor unit sound data, it is determined that the compressor has failed.

The invention discloses a Fast DDS-based QNX system soft diagnosis method and system and electronic equipment, and relates to the field of system diagnos.The method comprises the steps that S1, a Fast DDS-based global data space is constructed and serves as a real-time communication bus between a QNX system and an Android system, and decentralized interaction of dual-system diagnosis data is achieved; s2, deploying a soft diagnosis service in the QNX system; the soft diagnosis service comprises four core function modules: a black screen detection and self-healing module, a resource dynamic monitoring module, a virtual machine monitoring module and a burying point analysis module; s3, a diagnosis agent service is deployed in the Android system; the diagnosis agency service receives the alarm and data issued by the QNX system through the Fast DDS client; the method comprises the following steps: reading a log packaging file and a tcpdump file stored in a QNX system through a shared directory; and summarizing the data and uploading the summarized data to a cloud platform to realize visual display of the diagnostic data.

Methods, computer systems, and computer storage media are provided for utilizing system diagnostics focused in areas of disruption to improve inventory and workforce management in a revenue cycle management system. Inventory input is received from a revenue cycle management system. The inventory input includes inventory in the revenue cycle management system, types of claims in the inventory, pair agreements of the claims, and a length of time the claims have been in the inventory. Workforce input is also received from the revenue cycle management system. The workforce input includes experience, type of education or technical training, specialty, and outcomes corresponding to a workforce of the client. A portion of the inventory is automatically assigned to an associate of the workforce. The portion of the inventory tailored to the associate based on the inventory input and the workforce input.

A cloud-edge collaborative processing system includes: an edge computing system, an ICU diagnosis and treatment device, a service terminal device, and a cloud platform. The edge computing system collects multi-source heterogeneous medical data output from the ICU diagnosis and treatment devices and performs preprocessing, stores preprocessed data into an edge database, and connects to the cloud platform for data transmission and business interaction. The cloud platform connects to a plurality of edge computing systems to perform computation and processing of massive data. The medical-staff handheld terminals and the data terminals of wards are used to issue service instructions to the cloud platform to obtain the required third-party-business services.

The embodiment of the invention provides a visual analysis method for diagnosis and optimization of an urban drainage system and related equipment, and belongs to the technical field of smart city and data visualization. According to the method, a closed-loop analysis framework formed by collaboration of a map view, an analysis view and a comparison view is constructed. The map view integrates a hydrological and hydraulic model simulation result, visualizes a drainage system state and supports interactive modification; the analysis view is used for extracting a key drainage subsystem based on rules, and performing causal visualization by adopting an optimized mulberry-based graph integrated with terrain elevation information, so that overflow causes are accurately positioned; a comparison view is visualized through a parallel histogram and version inheritance relation, and multi-scheme comparison and iterative optimization management are supported. According to the method, the problems of difficulty in causal analysis, poor interpretability and lack of iterative optimization support in the prior art are solved, the whole-process integrated support from problem identification and diagnosis to optimization verification is realized, and the analysis efficiency and decision scientificity of the urban drainage system are remarkably improved.

The invention discloses a servo motor driver parameter management method and system, and relates to the technical field of motor parameter management, and the method comprises the steps: initializing a parameter database, and outputting a database initialization result; executing a state machine control flow based on a database initialization result; according to the state information of the state machine, executing a corresponding parameter synchronization strategy and outputting a parameter synchronization state; based on the parameter synchronization state and the state machine state information, performing hierarchical error detection and processing, identifying abnormity through a fault detection algorithm based on a time window, performing fault processing according to a hierarchical recovery strategy, and outputting a system health state; and executing system diagnosis and performance optimization according to the system health state and the parameter synchronization state, performing read-write optimization through the parameter cache layer, and implementing predictive maintenance to complete parameter management. Through unified storage and management of data parameters, the reliability of parameter management of the servo motor is improved, and the system has the advantages of high fault identification precision and remarkably improved system stability.

The invention belongs to the field of electromagnetic emission, and discloses an electromagnetic track emission system and method for friction coefficient full-period measurement. The electromagnetic track launching system comprises an electromagnetic track launching device, a pulse power system, a diagnosis system and a control system. According to the electromagnetic track launching system, full-launching-period measurement of the friction coefficient in an extreme electromagnetic environment can be achieved, and the system is used for monitoring the contact state between central tracks in each launching process and predicting the service life of a device; the full-emission period measurement of the local temperature of the guide rail can be realized; and full emission period measurement of contact pressure and contact resistance between the pivot rails is realized. According to the contact state between the guide rails in the launching process, the pretightening force of the guide rails or the size of the armature is adjusted, so that the service life of the guide rails is prolonged. The local temperature of the guide rail and the contact pressure between the pivot rails are monitored by utilizing the intrinsic physical characteristics of the Bragg grating, the reliability is high, the wavelength self-reference characteristic is realized, the absolute measurement can be realized, the temporal-spatial resolution is high, and the structure is compact.

In the present invention, a result collection unit collects actual values including an inlet-side temperature actual value and an outlet-side temperature actual value of a cooling device and an speed actual value of a rolled material, and collects the open / close state of a fill valve of each spray system. A predicted value calculation unit calculates an outlet-side temperature predicted value of the cooling device for each representative point by using the actual value of each representative point. An error change amount calculation unit calculates an error between the outlet-side temperature actual value and the outlet-side temperature predicted value of each representative point, selects two representative points from the plurality of representative points, and calculates the amount of change in the error between the two representative points. A unit-error change amount holding unit specifies, as a state-change spray system, a spray system in which the open / close state of the fill valve has changed between the two representative points, calculates a unit error change amount that is a change amount of each state-change spray system, and holds the unit error change amount for each open / close state of the fill valve of each state-change spray system. A failure diagnosis unit diagnoses a failure of the spray system on the basis of the unit error change amount held for each open / close state of the fill valve.

A processing system for processing consumables, comprising: a container in which the consumables are arranged, the container comprising an RFID tag with at least one RFID tag storage section for storing consumables status information indicative of a filling-status of the consumables arranged in the container; an RFID reading unit configured to read information from the RFID tag; an RFID writing unit configured to write information on the RFID tag; and a removing unit configured to remove a part of the consumables from the container; wherein the processing system is configured to: read, by means of the RFID reading unit, the consumables status information from the at least one RFID tag storage section, remove, by means of the removing unit, a part of the consumables from the container, determine, based on the consumables status information read by the RFID reading unit, whether a predetermined prohibition condition is met after the removal of the part of the consumables from the container by the removing unit, and in case the prohibition condition is not met, update, by means of the RFID writing unit, the consumables status information on the RFID tag, and in case the prohibition condition is met, irreversibly modify the RFID tag such that the processing system is able to identify from the RFID tag that the prohibition condition is met.

An inspection system includes a stage for positioning a substrate to be inspected, one or more reticle-based diagnostic targets positioned on the stage, and a reticle inspection sub-system having a field of view encompassing the stage. The system includes a controller configured to move the field of view between portions of the stage to selectively perform substrate and run time diagnostics (RTD) of predefined image quality metrics. In embodiments, the system may be an APMI system for EUV mask inspection and the diagnostic targets may be EUV reticle-based diagnostic targets having predefined EUV performance patterning. In embodiments, the diagnostic targets may be positioned adjacent to the mask to be inspected to permit contiguous scanning to perform inspection and RTD.

The invention discloses an IMA system fault diagnosis modeling tool based on a logic algebraic model, which comprises an ICD information management module and a fault diagnosis logic definition module, and is characterized in that the ICD information management module is used for realizing reading, analysis and data storage of an ICD file set and obtaining messages and parameters defined in ICD files; and the fault diagnosis logic definition module is used for defining various fault diagnosis logic algebraic models by defining variable names and logic operators according to the messages and parameters analyzed by the ICD information management module. According to the method, a visual and efficient logic modeling tool can be provided for IMA fault diagnosis function design and developers, and a diagnosis logic model is modified and solidified aiming at an object system, so that a high-adaptability agile design and development way is provided for the IMA system diagnosis function.

Systems and methods for determining a diagnosis of a screening system are disclosed. Such systems and methods include identifying defect results based on inline characterization tool data, identifying electrical test results based on electrical test data, generating one or more correlation metrics based on the defect results and the electrical test results, and determining at least one diagnosis of the screening system based on the one or more correlation metrics, the diagnosis corresponding to a performance of the screening system.

The present application provides an intelligent monitoring and diagnosis method, a vehicle-mounted central control system, a diagnostic system, and a vehicle. The method includes obtaining the vehicle's current state type in response to receiving abnormal health status information of a target user sent by a smart home monitoring device; determining the information type corresponding to the abnormal health status information; and generating and sending health status information corresponding to the abnormal health status information based on the vehicle's current state type and the information type. The intelligent health monitoring and diagnosis method provided in the present application can promptly send the abnormal health status information of the target user to relevant personnel associated with the target user, so that the relevant personnel can promptly understand the abnormal health status of the target user, thereby enabling the target user to receive timely diagnosis and treatment with the help of relevant personnel, thereby avoiding the dangerous situation where the target user cannot receive timely help and diagnosis due to abnormal health.

To make it possible to diagnose a state of a machine tool with high precision.SOLUTION: A diagnostic device comprises: a time-series data acquisition unit that acquires time-series data for indicating an active state of a machine tool 200 of a diagnostic target; a work-piece state data acquiring unit that acquires work-piece state data for indicating a state of a work-piece 1 before machining, the work-piece being machined by the machine tool 200; a feature amount calculating unit that calculates a feature amount from the time-series data acquired by the time-series data acquiring unit; and a diagnostic unit that determines whether the machine tool 200 is normal or abnormal by determining whether the feature amount calculated by the feature amount calculating unit falls outside a normal range of the feature amount corresponding to the work-piece state data.SELECTED DRAWING: Figure 1

The application provides a kind of detection system of infectious pathogen type based on 5 markers, the 5 markers include: GSDMD, p-MLKL, IL-6, PCT, CRP, the system includes the following modules: (1) detection model: the GSDMD of detection sample, p-MLKL, IL-6, PCT, CRP content;(2) analysis module: based on random forest algorithm, neural network algorithm is analyzed to data, constructs diagnosis bacterial infection model;(3) diagnosis module: based on machine learning algorithm model is judged to bacterial infection to be diagnosed sample.The system provided by the application reaches 0.909, the diagnostic accuracy rate, the sensitivity, specificity, positive predictive value and negative predictive value of diagnosis are as follows: 0.95, 0.85, 0.90, 0.92.

Disclosed are an automation system for building energy modeling and a method using same, the automation system diagnosing energy consumption of a building and minimizing unnecessary energy consumption.