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.