Mechanical equipment residual service life prediction method and system

Abstract

The invention discloses a mechanical equipment residual service life prediction method and system. The method comprises the steps that a time convolution network serves as a feature extraction algorithm, a long-term and short-term memory network serves as a regression prediction algorithm, a deep neural network life prediction model is constructed, and the deep neural network life prediction modelis trained; according to the model of the tested equipment and the data acquisition time sequence, constructing the acquired real-time operation data of the tested equipment into a service life prediction data set with time sequence characteristics; and carrying out prediction processing on the life prediction data set by using the deep neural network life prediction model to obtain the residualservice life of the tested equipment. A state monitoring signal output by a sensor for monitoring mechanical equipment has the characteristics of a time sequence; a time convolution network and a longshort-term memory network are combined, a deep neural network life prediction model is established for RUL prediction of mechanical equipment, the problems of over-fitting and gradient disappearanceexisting in a common deep neural network model are solved, and the prediction accuracy is improved.

Description

technical field [0001] The present disclosure relates to the technical field of mechanical equipment maintenance prediction, and in particular, to a method and system for predicting the remaining service life of mechanical equipment. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] With the development of modern science and technology, computer technology is widely used, and the functions of various systems are becoming more and more perfect. People put forward higher requirements for the reliability of the system under long-term and high load. , aircraft, etc., it is more necessary to carry out intelligent maintenance of its key components, and perform life prediction tasks in real time. The remaining useful life (Remaining Useful Life, RUL) of mechanical equipment generally refers to the time difference between the current running time ...

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Problems solved by technology

However, the disadvantage of this method is that theoretical analysis is very difficult, and it is difficult to obtain an accurate physical degradation model in actual situations. Generally, there is a certain deviation between the built model and the real failure mechanism, so the research on the RUL prediction method based on the physical model is relatively difficult. Coupled with the problem of difficult migration, it restricts the wide application of this technology

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