Mechanical equipment fault intelligent early warning method based on multivariate estimation and prediction

Abstract

The invention discloses a mechanical equipment fault intelligent early warning method based on multivariate estimation and prediction. The method comprises the following steps: step 1, modeling a datasubset; step 2, preprocessing the modeling data subset; step 3, constructing a prediction model of the mechanical equipment state parameters and the working condition parameters; step 4, estimating and predicting the state parameters of the mechanical equipment corresponding to the actually measured state parameters in a normal operation state; step 5, subtracting the actual measurement result ofthe state parameter from the prediction result to obtain a residual value of the state parameter, judging whether the absolute value and the growth trend of the residual value exceed corresponding threshold values or not, further detecting the fault abnormality of the equipment and giving an alarm; the intelligent early warning model of the mechanical equipment is established based on the multivariate estimation prediction method, and then intelligent early warning of the variable working condition mechanical equipment fault is achieved. Compared with a traditional mechanical equipment faultearly warning method, the method has the advantages of being high in prediction precision, high in early warning accuracy and more timely in early warning.

Description

technical field [0001] The invention belongs to the field of mechanical equipment early warning, in particular to an intelligent early warning method for mechanical equipment faults based on multivariate estimation and prediction. Background technique [0002] In the field of predictive maintenance of mechanical equipment, for abnormal state detection of mechanical equipment, the traditional early warning methods are hard threshold alarms or trend alarms. The hard threshold alarm is generally based on the type of equipment to determine the applicable international standard or national standard alarm threshold for the vibration monitoring parameters (displacement, velocity or acceleration) of the equipment, and determine the alarm threshold corresponding to the equipment according to the equipment's operating speed, power and other information . Some enterprises will formulate more applicable enterprise standard alarm thresholds based on the equipment operation accumulated o...

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Problems solved by technology

The traditional early warning method is only applicable to equipment under stable working conditions, and cannot solve the problem of abnormal fault detection of mechanical equipment under changing working conditions (such as equipment motor speed changes, load changes, current changes, etc.)

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