A data identification method and system for a high-noise industrial process

Abstract

The invention discloses a data recognition method and system for high-noise industrial processes. The method includes: acquiring historical time series data of an industrial process; preprocessing the historical time series data; determining a Busterworth filter based on the noise period in the historical time series data; The Worth filter filters the preprocessed historical time series data; constructs the frequency domain transfer function model; trains the frequency domain transfer function model through the filtered historical time series; converts the trained frequency domain transfer function model to In the time domain, a time domain transfer function model is obtained as a system identification model, and the data of a high-noise industrial process is identified by using the system identification model. The invention can perform online prediction in an industrial control environment and obtain prediction results in real time, thereby meeting the needs of actual industrial processes, effectively analyzing frequency characteristics of industrial processes, and explicitly identifying and filtering high-frequency fluctuating noises.

Description

technical field [0001] The invention relates to the field of industrial control, in particular to a data identification method and system for high-noise industrial processes. Background technique [0002] In recent years, with the vigorous development of industrial intelligence and big data technology, more and more intelligent detection and control facilities such as DCS and PLC have been integrated into the industrial production process, which has improved the automation level of the industrial field. This also brings opportunities for grasping the operation rules of complex equipment, predicting it based on past data and existing status, and then estimating the output. However, due to the complexity of the application environment, the sensor data in the actual environment will inevitably be affected by high-frequency noise such as electromagnetic interference and power grid fluctuations. As a result, the volatility of the recorded data in the system is much higher than th...

AI Technical Summary

Problems solved by technology

The linear regression method has low complexity and is not suitable for complex industrial processes; the sliding autoregressive method and the state space model are developed from linear regression, assuming that the same variable x has a linear relationship between each moment, and using the previous state at each moment to predict the current state, This method can eliminate random fluctuations in forecasting, but it is often difficult to represent non-zero mean and periodic noise

Neural networks such as BPNN are difficult to capture the long-term dependence of the system, and the interpretability and robustness are not strong.

Methods such as support vector machines can reduce the dimensionality of high-dimensional features, but the necessary kernel function or distance function cannot be automatically selected.

The above-mentioned industrial process modeling algorithms still have two common problems. One is that they cannot explicitly identify periodic noise. When the amplitude of the noise component is large, the system cannot correctly identify the target model; Only discrete-time models can be built for discrete data, and these models often need to be retrained and predicted when the system control or sampling frequency changes

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