156 results about "System diagnosis" patented technology

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.

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 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 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.

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.

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.

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.

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.

The present disclosure provides a diagnostic method and device of a vehicle-mounted system and an electronic device, and relates to the field of vehicle-mounted system diagnosis. The specific steps are: establishing the connection between a diagnostic instrument and a vehicle controller through the diagnostic instrument interface of a vehicle; controlling the SOC corresponding to the vehicle-mounted system through the vehicle controller, so that the SOC enters a diagnostic mode; and diagnosing the SOC through the diagnostic instrument. The present disclosure controls the vehicle controller by connecting the diagnostic instrument to the diagnostic instrument interface of the vehicle, so as to diagnose the fault of the vehicle controller, realizes the diagnosis of the vehicle controller, avoids the disassembly and damage of the vehicle structure during diagnosis, and improves the diagnosis efficiency of the vehicle.

The invention belongs to the technical field of fault intelligent diagnosis and positioning, and discloses an optical cable fault intelligent diagnosis and positioning method and system based on multi-mode fusion. The system comprises a multi-modal feature extraction module, a fault type intelligent classification module, a precise positioning integration module and a fault diagnosis positioning instruction output module, input preparation processing is carried out based on a multi-source processing data sequence, feature extraction is carried out according to a deep learning model, a fusion feature vector is obtained, and a fault diagnosis positioning instruction is output. Fault type classification is carried out based on the fusion feature vector, fault type confidence is calculated, a preliminary fault diagnosis report is obtained, a final fault positioning report is obtained, strategy instruction matching is carried out based on the final fault positioning report, and a matching result is output. The method has the remarkable advantages of high multi-modal data comprehensive degree, good system diagnosis intelligent effect and high operation and maintenance decision integration capability.

The utility model provides a fault detection device for a high-voltage system of an electrically-driven passenger car, which relates to the technical field of vehicle maintenance and comprises a touch screen, a detection device, a storage device, an adaptive port and an expansion port, the detection device is used for receiving operation data of the high-voltage system of the electrically-driven passenger car sent by the adaptive port, and the storage device is used for storing the operation data of the high-voltage system of the electrically-driven passenger car. Storing the operation data and the detection result in a storage device, and sending the operation data and the detection result to a touch screen for display; when the remote support platform is accessed, running data is sent to the remote support platform, a control instruction returned by the remote support platform is received, and the control instruction is displayed on the touch screen and sent to the adaptive port; the adaptive port is connected with the expansion port, is configured with an electrically-driven passenger car high-voltage system diagnosis protocol and is used for receiving operation data of the electrically-driven passenger car high-voltage system and sending a control instruction to the electrically-driven passenger car high-voltage system; the expansion port is connected with connection ports of various electric drive passenger car high-voltage systems and used for being connected with various types of electric drive passenger car high-voltage systems in an adaptive mode.

A detection system for a battery pack 100 contained in a housing 102 comprising one or more monitoring devices 108 to detect response signals corresponding to an electrochemical impedance spectroscopy

The present application relates to the technical field of power system diagnosis, in particular to a power distribution network single-phase grounding fault multi-edge collaborative diagnosis and positioning method, comprising the following steps: S1: real-time acquisition of current and voltage signals in the power distribution network through a sensor network; S2: preprocessing of the current and voltage signals; S3: analysis of the current unbalance degree and voltage mutation point to identify whether a single-phase grounding fault has occurred; S4: calculation of the propagation time difference of the fault signal using the time difference of arrival method to preliminarily estimate the location of the fault; S5: correction of the fault location to locate the specific fault point; S6: generation of a fault location report; S7: triggering of a processing measure to send a maintenance instruction to relevant personnel according to the fault location result. The present application provides accurate fault location and automated processing procedures by combining multi-data source analysis and power grid topology, effectively improving fault detection accuracy and processing efficiency, and ensuring stable operation of the power grid.

The invention discloses a diagnosis method and device of a vehicle control system and a vehicle, which are applied to the technical field of vehicle fault diagnosis and can realize fault collaborative diagnosis of each module and accurate positioning of a root fault. According to the diagnosis method of the vehicle control system, diagnosis hierarchy division can be carried out based on the upstream and downstream dependency relationship of modules in the automatic driving control system, fault diagnosis is carried out in sequence according to the sequence of the divided diagnosis hierarchy, and the fault diagnosis result of the module with the lowest hierarchy serves as the fault diagnosis result of the automatic driving control system. The module corresponding to the fault is accurately positioned.

The invention relates to a diagnostic communication method based on an AUTOSAR architecture and an electronic control unit, and belongs to the field of diagnostic communication. In the method, two isolated diagnosis processing paths are configured for diagnosis request messages of a CAN type and a CAN FD type, when the diagnosis request messages are received, diagnosis response messages belonging to the same message type as the diagnosis request messages are generated through the diagnosis processing paths corresponding to the message types of the diagnosis request messages, and the diagnosis response messages are sent to the diagnosis request messages. And sending the diagnosis response message through a reverse path of the diagnosis processing path corresponding to the message type. Since different types of messages are processed through different paths, the same type of diagnosis response message is replied according to the received type of diagnosis request message, so that the electronic control unit can flexibly adapt to different types of diagnosis request messages sent by the external equipment, and is adaptively compatible with different types of diagnosis request messages, thereby improving the diagnosis accuracy of the electronic control unit. And the flexibility and expansibility of diagnosis communication of the automobile electronic system are improved.

A computer system and method for intelligent system diagnostics and management is provided. The system comprises at least one processor and a memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises receiving resource data and usage data, preprocessing the resource data and the usage data into operational data, training and updating a foresight model using the operational data, receiving a forecast generated by the foresight model, and sending a notification for a recommended action based on the forecast. The forecast may be associated with a future resource state or event associated with the operational data.

Methods, systems, and devices for host system diagnostic testing are described. A diagnostic tool including a diagnostic executable stored to an external memory may evaluate a system including a host subsystem and a memory subsystem. Upon initialization, the diagnostic executable may configure trace points in one or more layers (e.g., associated with an operating system) of the host subsystem based on dependencies (e.g., libraries) stored to the external memory, and may receive, from the host subsystem, first data collected at the trace points, directly from a host system buffer, or from the memory subsystem. Concurrent to the collection procedure, the diagnostic executable may perform processing operations on the first data to generate second data, which may be associated with one or more metrics of system performance. The second data is stored to the external memory, and may be utilized to evaluate the system.

The application discloses a multi-source domain rolling bearing fault diagnosis method and system based on a diffusion model. T In the forward process of the diffusion model, the source domain label is converted into a one-dimensional vector, and noise is added to destroy the original distribution to obtain y T In the reverse process, the variable of the Gaussian distribution is used to replace y T , noise distribution features added in the forward distribution are learned through a noise prediction network, and gradual denoising is performed to obtain y0 The source domain data is input into the noise prediction network to extract data features, the target domain data is input into the noise prediction network, global noise prior and local noise prior corresponding to the target domain data are obtained, the source domain data and the target domain data are input into the noise prediction network, the multi-source domain adaptation algorithm is used to map the source domain data features to the target domain, noise distribution features related to the target domain data are learned, and gradual denoising is performed to obtain the fault type of the target domain data.