What methodologies are scaling up to detect deviations in plant operations?

Data quality is essential for reliable decision-making, deviation detection, and process optimization. As highlighted by Axens through a recently published a white paper, robust data cleaning and normalization are critical first steps. Machine learning models, such as random forests, can estimate missing variables needed for accurate normalization.

Once normalized, statistical methods like Mahalanobis distance and Hampel filters effectively identify outliers, which can be visualized on original time series for easy interpretation. Monitoring moving averages and applying normality tests help detect gradual drifts in process parameters.

Dimensionality reduction techniques like PCA reveal changes in parameter correlations over time, while clustering methods such as KMeans—guided by the elbow method—extract meaningful patterns from time series data without requiring labeled inputs.

The unsupervised nature of these techniques enables rapid deployment and actionable insights without extensive data labeling. Integrating these methods within automated control systems and combining them with domain expertise and AI enhances scalability, accuracy, and reduces false positives, supporting proactive plant operation management.

All these methodologies are available on Axens digital platform Connect®.