6results about How to "Realize failure prediction" patented technology

The application discloses an industrial robot predictive maintenance system based on machine learning, and relates to the technical field of industrial robot maintenance.The application comprises a data acquisition module, a preprocessing module, a feature engineering module, a hybrid prediction model unit, a meta-learning and field adaptation module, a federal learning coordination module, a digital twin sample generation module and a dynamic decision engine unit.The application has the following advantages: through the collaborative mechanism of the meta-learning and field adaptation module, a model-agnostic meta-learning algorithm is used to train a cross-brand general feature extractor;different brand data distributions are confused by combining an adversarial field adaptation network;the "data gap" caused by the definition of sensor parameters and the difference in sampling frequency in traditional modeling is broken through;the model is collaboratively trained under the premise that the data of each brand is not out of the local;the global model containing the commonness of cross-brand faults is generated;and the "data island" problem caused by commercial security requirements in the industrial scene is solved.

The application relates to a power conversion equipment fault prediction method and device and a power conversion system. The power conversion equipment fault prediction method comprises the following steps: obtaining the output voltage of a detection coil multiple times in a preset time period, extracting each output frequency corresponding to each output voltage of the detection coil, and predicting the fault occurrence time of the power conversion equipment according to the change trend of each output frequency. The power conversion equipment fault prediction device comprises a detection coil and a data processing device, the detection coil is connected with the data processing device, and the detection coil is a closed coil; the data processing device is used for obtaining the output voltage of the detection coil, extracting the corresponding output frequency, and predicting the fault occurrence time according to the change trend of each output frequency. The power conversion equipment fault prediction method, device and power conversion system realize fault prediction of the power conversion equipment, and further improve the reliability of the power conversion equipment operation.

The application discloses a transformer fault prediction method and system, and relates to the technical field of power equipment.The method comprises the following steps: collecting the start-stop action times of each time window, and determining the temperature control response lag coefficient of each time window by analyzing the temperature control lag level of the cooling control system in the power transformer when the cooling control system faces power mutation; analyzing the non-steady-state operation degree of the cooling control system in the corresponding time window to determine whether the temperature control state of the cooling control system affects the normal operation of the power transformer, and issuing a transformer fault prediction instruction; combining the power consumption drift and voltage frequency spectrum disturbance of the power transformer in each time window, analyzing the non-normal operation degree of the power transformer in the corresponding time window, performing digital model analysis on the non-normal operation index of the power transformer in each time window, generating a corresponding grade fault prediction signal, and performing corresponding voice broadcast reminding.

This utility model discloses an energy meter with a fault self-diagnosis function circuit, comprising: a main control module, a metering module, a communication module group, a multi-level interlocking protection circuit, a diagnostic bus matrix, a fault tracing memory, and a power supply module. The main control module is connected to the metering module, the communication module group, the multi-level interlocking protection circuit, the diagnostic bus matrix, and the fault tracing memory. The diagnostic bus matrix is ​​connected to the metering module and the communication module group. The fault tracing memory is connected to the multi-level interlocking protection circuit. The power supply module is connected to the main control module, the metering module, the communication module group, the multi-level interlocking protection circuit, the diagnostic bus matrix, and the fault tracing memory. This technical solution enables comprehensive fault self-diagnosis of the energy meter, increasing the detection coverage to 12 key nodes, improving response speed, supporting fault prediction and reliable data storage, ensuring operational safety, and improving the level of intelligent power grid management.

The invention relates to the technical field of data processing and signal analysis, and discloses a steam turbine generator unit vibration-power cross-domain feature fusion analysis method and system, and the method comprises the steps: obtaining vibration signal time sequence data and power signal time sequence data of multiple operation cycles of a steam turbine generator unit, carrying out the preprocessing of the two types of data, and obtaining a power signal time sequence data; obtaining standardized time sequence data which can be used for feature extraction; vibration signal features and power signal features are extracted from the standardized time sequence data, and cross-domain feature vectors of all operation cycles are formed through fusion; inputting the cross-domain feature vector of each operation cycle into a fusion analysis model for processing, and outputting a comprehensive evaluation index of each operation cycle; and on the basis of the multiple operation cycle comprehensive evaluation indexes, operation abnormity identification and early warning judgment of the steam turbine generator unit are executed, and accurate monitoring of the unit operation state is achieved. According to the invention, the technical problem of insufficient evaluation precision caused by a single signal analysis method in the prior art is solved.

The application discloses a short-circuit cumulative distortion evaluation method and system of a power transformer spiral winding and a medium, relates to the technical field of electrical equipment reliability evaluation, and is based on structure parameters and historical fault parameters to fit out peak current plastic deformation and peak current plastic deformation generated by the first peak current in short-circuit impact; and according to the peak current plastic deformation, single-impact deformation reduction and reclosing-impact deformation reduction are calculated; finally, multiple-impact deformation accumulation is calculated, and distortion evaluation is performed on the last impact deformation accumulation result; the application provides a short-circuit cumulative distortion calculation method based on the structure and historical fault parameters of a target power transformer, can effectively evaluate the spiral winding distortion deformation condition, enhances the reliability of the transformer and the stability of the power grid operation, ensures the continuous and stable power transmission, and realizes fault prediction and health management of the transformer spiral winding.