# An online intelligent detection method for slurry density in concept drift data streams based on collaborative computing

## TL;DR

This paper introduces a new method for detecting slurry density in industrial settings that adapts to changing data conditions in real time.

## Contribution

A novel online detection method using collaborative computing and adaptive models to handle concept drift in slurry density data streams.

## Key findings

- The proposed method outperforms existing algorithms in density estimation metrics.
- Collaborative computing ensures real-time detection and model adaptability in industrial applications.
- The method effectively mitigates concept drift by focusing on recent data.

## Abstract

In industrial environments, slurry density detection models often suffer from performance degradation due to concept drift. To address this, this article proposes an intelligent detection method tailored for slurry density in concept drift data streams. The method begins by building a model using Gaussian process regression (GPR) combined with regularized stochastic configuration. A sliding window-based online GPR is then applied to update the linear model’s parameters, while a forgetting mechanism enables online recursive updates for the nonlinear model. Network pruning and stochastic configuration techniques dynamically adjust the nonlinear model’s structure. These approaches enhance the mechanistic model’s ability to capture dynamic relationships and reduce the data-driven model’s reliance on outdated data. By focusing on recent data to reflect current operating conditions, the method effectively mitigates concept drift in complex process data. Additionally, the method is applied in industrial settings through collaborative computing, ensuring real-time slurry density detection and model adaptability. Experimental results on industrial data show that the proposed method outperforms other algorithms in all density estimation metrics, significantly improving slurry density detection accuracy.