Bearing residual service life prediction method based on SMA optimization algorithm

Abstract

The invention discloses a bearing residual service life prediction method based on an SMA optimization algorithm, and the method comprises the steps: data preprocessing, feature vector extraction, model label construction and data set division, SMA-LSTM prediction model construction and training, and the like, wherein during data preprocessing, horizontal vibration signals containing more bearing information are selected from a bearing data set to serve as a data set of the experiment, during feature vector extraction, time domain and frequency domain parameters capable of reflecting the bearing degradation performance are extracted from the vibration signals in the data set, and normalization processing is conducted on the extracted feature parameters. The method has the advantages that aiming at the problems that the hyper-parameter optimization of the neural network is difficult and the optimization algorithm is easy to fall into local optimum, the colistia algorithm with a dynamic search structure is provided, the balance between global search and local search can be kept, the local search capability is very strong, the falling into local optimum can be effectively avoided, and the prediction precision of the model is improved.

Description

technical field [0001] The invention relates to the technical field of motor equipment fault prediction and health management, in particular to a method for predicting the remaining service life of a bearing based on an SMA optimization algorithm. Background technique [0002] With the rapid improvement of modern manufacturing productivity and the level of electronic information technology, mechanical equipment has not only developed in the direction of mechanization and automation, but also in the direction of high integration and high intelligence, which will inevitably raise new requirements for the reliability of mechanical equipment operation. requirements. More than 70% of all mechanical equipment use rolling bearings. The quality of rolling bearings directly affects the stable operation of mechanical equipment. Therefore, it is particularly important to predict the remaining useful life (Remaining Useful Life, RUL) of bearings. [0003] The selection of the predictio...

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