Data-Driven Fault Detection and Process Monitoring in Industrial Systems

Industrial systems generate vast quantities of operational data from sensors, control logs and supervisory systems. Data-driven fault detection and process monitoring harness this wealth of information to identify abnormal conditions, diagnose root causes and forecast potential failures without relying exclusively on first-principles models. Techniques range from multivariate statistical process control using principal component analysis and partial least squares, to advanced machine learning methods such as kernel methods, support vector machines and deep neural networks. Recent advances in autoencoder-based anomaly detection, recurrent neural networks and transfer learning have improved sensitivity to subtle deviations in high-dimensional, non-stationary data streams. These approaches support real-time monitoring in chemical plants, power generation facilities and manufacturing lines, enabling timely intervention and predictive maintenance. By integrating fault diagnosis and prognosis, modern frameworks also assess remaining useful life, optimise maintenance schedules and reduce unplanned downtime. The global drive towards Industry 4.0 and the Industrial Internet of Things has further accelerated the adoption of data-driven monitoring, coupling cloud-based analytics with edge computing and digital-twin technologies. Despite these gains, challenges persist in managing noisy, incomplete or unbalanced datasets, ensuring robustness across operating regimes and delivering interpretable results to engineers. Future work seeks to blend data-driven models with physical insights, creating hybrid digital twins that marry rigorous process knowledge with adaptive learning, thereby elevating safety, productivity and sustainability across diverse industrial sectors.

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Data-Driven Fault Detection and Process Monitoring in Industrial Systems publication trend

The graph below shows the total number of articles in data-driven fault detection and process monitoring in industrial systems across all publications each year (not limited to Nature Index journals).

Technical terms

Data-driven fault detection: A methodology that uses historical and real-time process data to identify deviations from normal operation without explicit physical modelling.

Process monitoring: Continuous oversight of key variables and performance indicators to detect abnormalities and ensure safe, efficient operation.

Autoencoder: A neural network trained to recreate its input, widely used for anomaly detection by measuring reconstruction error.

Long short-term memory (LSTM): A type of recurrent neural network designed to capture temporal dependencies in sequential data, useful for dynamic fault diagnosis.

Principal component analysis (PCA): A statistical technique that transforms correlated variables into orthogonal components to simplify fault detection in multivariate data.

References

1. Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis. Processes (2017).
2. Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network. Sensors (2019).
3. A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data. Processes (2022).