2results about How to "Reflect running status" patented technology

The application relates to an abnormal state recognition method of a civil aviation onboard server, which comprises the following steps: collecting onboard server running data, hardware state data, onboard environment data and flight state data, and writing the flight stage, timestamp, model identification and server number; after the multi-source monitoring data is preprocessed, statistical features, frequency domain features and time sequence dependent features are extracted to generate a multi-dimensional feature vector; the multi-dimensional feature vector is input into an onboard end lightweight self-encoding model to obtain a reconstruction error, an abnormal type, a confidence and a health score; the data uploading is determined according to the reconstruction error and the confidence; the ground end aggregates the uploaded data according to the model, flight stage and hardware batch, calculates a common mode abnormality index, trains global model parameters, and then the model is updated to the onboard end.

This invention discloses a monitoring and diagnosis method and system for electrical safety faults in lifting machinery, relating to the field of lifting machinery safety monitoring technology. The method includes: continuously collecting multi-source electrical data from the lifting machinery's electrical system at a preset sampling frequency; generating a standardized data sequence through preprocessing; calculating four operational risk characterization quantities; obtaining a comprehensive risk index through weighted fusion; and classifying the system's operating status based on preset thresholds. When a warning or fault state is determined, a risk vector is constructed based on weight coefficients, matched with vectors in a fault feature model library, and the fault type with the smallest distance is selected as the diagnostic result. Alarm information is simultaneously output and the data is stored. The weight coefficients and fault feature model library are iteratively optimized based on historical data to continuously improve fault diagnosis accuracy. Compared to existing systems that only provide alarm information, this application can clearly distinguish different fault types, providing maintenance personnel with a more intuitive and accurate basis for fault location.