Sensor abnormality detection in multistage compressor units: A "white box" approach using tree-based genetic programming

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@Article{WONG:2023:prime,

• author = "Pauline Wong and W. K. Wong and Filbert H. Juwono and Basil Andy Lease and Lenin Gopal and I. M. Chew",
• title = "Sensor abnormality detection in multistage compressor units: A {"}white box{"} approach using tree-based genetic programming",
• journal = "e-Prime - Advances in Electrical Engineering, Electronics and Energy",
• volume = "5",
• pages = "100209",
• year = "2023",
• ISSN = "2772-6711",
• DOI = "doi:10.1016/j.prime.2023.100209",
• URL = "https://www.sciencedirect.com/science/article/pii/S2772671123001043",
• keywords = "genetic algorithms, genetic programming, Sensor abnormality, Fault detection",
• abstract = "Sensors are crucial in detecting equipment problems in various plant systems. In particular, detecting sensor abnormality is challenging in the case of using the data which are acquired and stored offline (data logs). These data are normally acquired using Internet of Things (IoT) system and stored in a dedicated server. This situation presents both opportunities and challenges for exploration in sensor abnormality detection task. In this paper, we propose a multistage compressor sensor fault detection method using data logs. In the proposed method, the compressor sensor output is modeled as a function of other sensors using static approach. Subsequently, the model output is used for detecting abnormality by observing the residuals. It has been shown that the histogram of residuals offers rich information to predict abnormality of the targeted sensor. In particular, we explore the concept using Genetic Programming (GP) to generate regression model which offers more {"}white box{"} solution to the operators. There are various advantages in this approach. Firstly, the conventional {"}black box{"} approach lacks model transparency and, thus, is highly undesirable in critical systems. Secondly, equations are more easily applied in Programmable Logic Controller (PLC) if autonomous flagging is required. We also compare the proposed model with Multiple Linear Regression (MLR) and Neural Network Regression (ANN). Results show that the best generated models are comparable with the latter but with more crisp {"}white box{"} mathematical equations using lesser feature inputs (four features only). This model yields R2 of 0.991 and RMSE of 2.1times10-2",

}

Genetic Programming entries for Pauline Wong Wei Kitt Wong Filbert H Juwono Basil Andy Lease Lenin Gopal I M Chew

Citations