Explainable fault diagnosis of oil-gas treatment station based on transfer learning

Jiaquan Liu, Lei Hou, Rui Zhang, Xingshen Sun, Qiaoyan Yu, Kai Yang and Xinru Zhang

Energy, 2023, vol. 262, issue PA

Abstract: Fault diagnosis is crucial for safe operation of the oil-gas treatment station. With the rapid-increasing volume of the data collected in oil-gas fields, more attention has been paid to data-driven diagnosis method. It is difficult for the traditional neural network to learn data features thoroughly without sufficient data samples, which makes transfer learning an effective solution to this problem. However, the existing diagnosis researches based on transfer learning do not involve the explainability analysis, resulting in the black-box nature of diagnosis results. This makes the model difficult to be trusted when deployed in the application scenario. An explainable diagnosis method based on transfer learning is proposed. The two-dimensional class activation map algorithm and multi-dimensional dynamic time warping theory are utilized to explain the diagnosis process of the deep residual network. Through the data collected at the oil-gas treatment station, the process of transfer diagnosis of four abnormal conditions is explained in detail. The experimental results show that this method can be applied to effectively analyze the regional similarity of samples and sample regions attentioned by diagnosis model. This can significantly improve the confidence of the diagnosis model and provide powerful auxiliary tools for fault reasoning and decision-making of human experts.

Keywords: Fault diagnosis; Class activation map; Transfer learning; Explainability (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222021442

DOI: 10.1016/j.energy.2022.125258

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