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

A mechanical equipment and multi-variable technology, which is applied in the direction of prediction, calculation, and instrumentation, can solve problems such as abnormal fault detection of mechanical equipment under changing working conditions, avoid abnormal shutdown and major economic losses, and realize early prediction and discovery. The effect of high prediction accuracy

Pending Publication Date: 2019-10-01
西安因联信息科技有限公司
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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 fau

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

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Embodiment Construction

[0042] The present invention is further described below in conjunction with accompanying drawing:

[0043] see Figure 1 to Figure 4 , an intelligent early warning method for mechanical equipment faults based on multivariate estimation and prediction, including the following steps:

[0044] Step 1, select the state parameters of the mechanical equipment under the normal working state of the mechanical equipment and the corresponding part of the working condition data of the mechanical equipment, and model the data subset;

[0045] Step 2, preprocessing the modeling data subset: perform min-max standardization on each variable feature of the modeling data subset, and normalize to [0,1] interval;

[0046] Step 3, construct the prediction model of mechanical equipment state parameters and working condition parameters: select the normalized modeling training data set to establish the corresponding normal operation space matrix D;

[0047] Step 4, the measured state parameters co...

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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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Application Information

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IPC IPC(8): G06Q10/00G06Q10/04
CPCG06Q10/04G06Q10/20
Inventor 胡翔田秦吕芳洲夏立印
Owner 西安因联信息科技有限公司
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