(334ai) Hybrid Monitoring Methods for Detection, Diagnosis, and Classification

Authors: 
Sheriff, M. Z., Texas A&M University
Many industrial processes collect an abundance of data from different sensors. These sensors often measure a wide variety of physical properties in order to ensure that these parameters are adhering to expected values. This is essential to ensure plant and operator safety, increase economic benefits, and maintain product quality.

A number of algorithms have been developed in order to improve existing fault detection and diagnosis performance. These algorithms integrate a number of different data-driven driven tools and methods. Multiscale wavelet-based representation of data can be used in order to handle data that is autocorrelated, non-Gaussian, and noisy (Bakshi, 1998). Hypothesis testing methods such as the Generalized Likelihood Ratio (GLR) technique can be used in order to provide the best possible detection for a fixed false alarm rate (Reynolds & Lou, 2010). Moreover, certain model-based methods have been developed in order to monitor process drifts and degradations in the process model, even when a process is operating under control (Sheriff et al., 2019).

This work will discuss different hybrid monitoring algorithms that were developed, their features, and practical applications, which include fault detection, diagnosis and classification of the benchmark Tennessee Eastman Process (TEP), and online monitoring of fouling in industrial heat exchangers (Basha et al., 2020; Sheriff et al., 2017, 2018, 2019).

Research Interests: Process systems engineering with an emphasis on the development of machine learning-based methods for process modeling, estimation, fault detection, and control. The algorithms and tools developed are utilized in many applications to improve the operation of various chemical, environmental, biological, and electrical systems.

References

Bakshi, B. R. (1998). Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal, 44(7), 1596–1610. https://doi.org/10.1002/aic.690440712

Basha, N., Sheriff, M. Z., Kravaris, C., Nounou, H., & Nounou, M. (2020). Multiclass data classification using fault detection-based techniques. Computers and Chemical Engineering, 136, 1–11. https://doi.org/10.1016/j.compchemeng.2020.106786

Reynolds, M. R., & Lou, J. (2010). An Evaluation of a GLR Control Chart for Monitoring the Process Mean. Journal of Quality Technology, 42(3), 287–310. https://doi.org/10.1080/00224065.2010.11917825

Sheriff, M. Z., Karim, M. N., Nounou, H. N., & Nounou, M. N. (2018). Process monitoring using PCA-based GLR methods: A comparative study. Journal of Computational Science, 27, 227–246. https://doi.org/10.1016/j.jocs.2018.05.013

Sheriff, M. Z., Mansouri, M., Karim, M. N., Nounou, H., & Nounou, M. (2017). Fault detection using multiscale PCA-based moving window GLRT. Journal of Process Control, 54, 47–64. https://doi.org/10.1016/j.jprocont.2017.03.004

Sheriff, M. Z., Nounou, H., Nounou, M., & Karim, M. N. (2019). Monitoring process degradation through operating regime based process monitoring. AIChE Spring Meeting and Global Congress on Process Safety: Process Control Monitoring and Analytics.

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