Service life prediction method for electromechanical system and critical components under completely truncated data condition

Abstract

The invention discloses a service life prediction method for an electromechanical system and critical components under the completely truncated data condition. The service life prediction method comprises firstly, extracting originally completely truncated data features based on wavelet packet decomposition to obtain a feature vector sequence set of the completely truncated data; then, calculating a truncation minimum quantitative error (MQE) sequence based on feature fusion of a self-organizing feature map (SOM); performing chaotic parallel multi-layer perceptron (CPMLP) modeling and determining an extending MQE sequence and the failure time based on the CPMLP model; and finally, constructing a feed forward neural network (FFNN) target vector based on survival probability calculation of an intelligent product-limit estimator (iPLE) and performing FFNN training and testing and service life prediction. According to the method, performance degradation indicators of the electromechanical system and critical components are established, service life estimation values of predicted objects are obtained by using fitting residuals, survival probabilities of the predicted objects in a period of time in the future are obtained, and the completely truncated data problem facing the service life prediction of the electromechanical system and critical components is solved.

Description

technical field [0001] The invention relates to a life prediction method for key components of an electromechanical system, which belongs to the technical field of fault diagnosis and prediction of mechanical systems and components, and specifically refers to a life prediction method for an electromechanical system and its key components under the condition of completely truncating data. Background technique [0002] Electromechanical systems are widely used in current engineering systems. However, with a large number of failures and maintenance problems, how to correctly and reasonably solve the prediction problems of electromechanical systems and their key components, and improve the availability of enterprise equipment in a practical sense, reduce It is of great significance to reduce downtime, reduce maintenance costs and safety risks. [0003] At present, life prediction models for key components of electromechanical systems, especially data-driven probabilistic models ...

AI Technical Summary

Problems solved by technology

This life prediction method can overcome the shortage that the current electromechanical system life prediction method requires a large number of life data values, and at the same time requires condition monitoring data and full life cycle data. Obtain the life estimation value of the predicted object, and obtain the survival probability of the predicted object for a certain period of time in the future; and then, solve the problem of "completely truncated data" faced by the life prediction of electromechanical systems and key components

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