Inference of faults through symbolic regression of system data

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@Article{HALE:2022:CCE,

• author = "William T. Hale and Efi Safikou and George M. Bollas",
• title = "Inference of faults through symbolic regression of system data",
• journal = "Computer \& Chemical Engineering",
• volume = "157",
• pages = "107619",
• year = "2022",
• ISSN = "0098-1354",
• DOI = "doi:10.1016/j.compchemeng.2021.107619",
• URL = "https://www.sciencedirect.com/science/article/pii/S0098135421003975",
• keywords = "genetic algorithms, genetic programming, Machine learning, Symbolic regression, Fault detection, Soft sensors, Inferential sensors",
• abstract = "We present the development of inferential sensors that use system input and output measurements to improve the accuracy and robustness of fault detection and isolation. These inferential sensors transform and augment the sensed information of a system to: (i) minimize the evidence of uncertainty in the inferred variables, decreasing the rates of false alarms and nondetections; and (ii) provide distinguishable estimates of the existence and/or severity of faults, decreasing the rate of misdiagnoses. The proposed method symbolically regresses the noisy and uncertain system measurements, using genetic programming, to evolve uniquely explainable mathematical functions that minimize a least-squares objective of the fault inference. A standard workflow using the proposed algorithm for fault diagnostics is presented and illustrated in the classification of the severity of fouling in a cross-flow plate-fin heat exchanger. The effectiveness and robustness of the method are explored at different test designs, assessed using k-nearest neighbors classification, and compared to other traditional fault classification methods. The extension of the inferential sensors to information theoretic metrics, where the system model is augmented to improve the evidence of fault(s) is also discussed",

}

Genetic Programming entries for William T Hale Efstathia Safikou George M Bollas

Citations