Mechanical equipment multi-working-condition fault prediction method based on attention mechanism

Abstract

The invention provides a mechanical equipment multi-working-condition fault prediction method based on an attention mechanism, and the method comprises the following steps: 1), carrying out the preprocessing of data, carrying out the vectorization and normalization of original data, and carrying out the division through a sliding time window; 2) embedding the preprocessed data into a long-short-term memory network layer, obtaining a data long-range dependency relationship through training, and calculating a hidden vector; 3) inputting the hidden vector in the step 2) into the attention layer;4) in the network training process, optimizing the network parameters by using a particle swarm optimization technology; and 5) inputting a result obtained by network training into a full connection layer of the neural network, and performing linear regression calculation by utilizing higher-layer feature representation learned by the full connection layer to obtain RUL corresponding to the periodof the machine for manually adjusting machine operation parameters or operation conditions. According to the invention, the natural language processing technology and the attention mechanism are combined to realize fault prediction under multiple working conditions.

Description

technical field [0001] The invention relates to a fault prediction method, in particular to a multi-working-condition fault prediction method for mechanical equipment, which belongs to the technical field of mechanical equipment. Background technique [0002] With the rapid development of sensor technology and industrial systems, industrial equipment sensor data is increasing rapidly. However, in the process of industrial production and manufacturing, due to complex physical and chemical changes in the production process, mechanical equipment will inevitably experience performance degradation or even failure. Faults in the production process may cause serious industrial production problems and economic losses, so it is particularly important to realize fault prediction and health management (PHM) in industrial production. [0003] Nowadays, in order to realize the failure prediction and health management of industrial production, scholars have proposed the concept of remain...

AI Technical Summary

Problems solved by technology

Its disadvantages are: the method of obtaining data by this technology is to manually produce features, and the process is extremely complex and difficult, time-consuming and labor-intensive; secondly, this method cannot make full use of the potential time-dependent relationship in sensor data, and cannot find the correct The mechanical production process affects greater operating conditions, thereby providing artificial adjustments to the mechanical production process to facilitate

Images