Dimension reduction-based layered time memory industrial anomaly detection method and device

Abstract

The invention provides a dimension reduction-based layered time memory industrial anomaly detection method and device. The method comprises the steps of obtaining a data set corresponding to a multi-dimensional time sequence generated by a to-be-detected industrial sensor; performing denoising processing on the original data; abstracting the number of dimensions of the data set into the corresponding number of dimension information vertexes; calculating a correlation numerical value between the dimensions and endowing the correlation numerical value to a corresponding edge in the graph as a weight value; performing minimum spanning tree selection clustering according to the relevancy between the dimensions; performing PCA dimension reduction on the formed block cluster; and carrying out layered time memory model abnormality judgment on the obtained feature data after dimension reduction. According to the invention, dimensionality reduction is carried out through correlation dimensionality selection, redundant features are removed, the calculated amount is reduced, the time point abnormity in the industrial sensor time sequence data flow can be found in time, and the trouble that anormal data set in the industry is collected to train the model is avoided.

Description

Technical field [0001] The invention relates to the field of industrial sensor multi-dimensional time data anomaly detection, in particular to a dimensionality reduction-based hierarchical time memory industrial anomaly detection method suitable for industrial generation of fast data streams. Background technique [0002] In recent years, with the development of the Internet of Things and artificial intelligence, revolutionary changes have taken place in various fields. Industry 4.0 is one of them. Industry 4.0 is also called industrial intelligent manufacturing. Designed to achieve a highly flexible automated production process. More and more real data are collected by the sensor equipment of the factory production line, forming a continuous data flow. Often such data streams can be represented by time series patterns, and most of them exist in the form of multi-dimensional time series. It is extremely important and meaningful to conduct data mining and effective data analys...

AI Technical Summary

Problems solved by technology

[0007] Purpose of the invention: Aiming at the characteristics of large data volume and strong dynamics of the data flow generated by industrial production sensors, there are problems such as high dimensionality and long model training time. The purpose of the invention is to provide a layered time memory industry based on dimensionality reduction An anomaly detection method and device do not require pre-training in advance, and can directly capture the context of the data for anomaly detection, thereby improving the speed and accuracy of anomaly detection

Images