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Data-driven health condition and RUL prognosis for liquid filtration systems
이승현,Seungju Lee,Kwonneung Lee,Sangwon Lee,Jaemin Chung,김창완,윤장혁 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.4
Because the clogging of filters leads to degradation and failure of mechanical systems, it is important to assess the health condition of a filter and predict its remaining useful life (RUL). Despite prior studies of liquid filtration systems, adequate attention has not been paid to data-driven health condition and RUL prognosis. Therefore, this study suggests a datadriven prognosis approach for liquid filtration systems. We define a health index (HI) for the filter under study and then predict HI values from the degradation point to the end-of-life using recurrent neural network algorithms, thereby yielding the filter’s RUL. As a result, the bidirectional LSTM achieved the best performance, and the RUL measured through HI prediction was close to the actual RUL. The proposed approach can be used for the maintenance of liquid filtration systems in various industries.