Tool remaining useful life prediction using bidirectional recurrent neural networks (BRNN)
نویسندگان
چکیده
Abstract Nowadays, new challenges around increasing production quality and productivity, decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance monitor health critical components. machine tool sector, one main aspects wear cutting tools, as affects directly fulfillment tolerances, scrap, etc. Besides, prediction remaining useful life (RUL) which related their level, gaining more field predictive maintenance, being that a crucial point for an improvement process. Unlike monitoring current tools real time, diagnosis does, RUL allows know when will end its life. This key factor since optimizing planning maintenance strategies. Moreover, substantial number signals can be captured from but not all them perform optimum predictors RUL. Thus, this paper focuses on has two objectives. First, evaluate prediction, were turning process investigated by using recursive feature elimination (RFE). Second, use bidirectional recurrent neural networks (BRNN) regressive models predict machining operations investigated. The results compared traditional learning (ML) convolutional (CNN). show among captured, root mean squared (RMS) parameter forward force ( $${F}_{y}$$ F y ) prediction. As well, long-short term memory (BiLSTM) gated units (BiGRU), types BRNN, along with RMS signal, achieved lowest error (RMSE) RUL, also computationally most demanding ones.
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ژورنال
عنوان ژورنال: The International Journal of Advanced Manufacturing Technology
سال: 2023
ISSN: ['1433-3015', '0268-3768']
DOI: https://doi.org/10.1007/s00170-023-10811-9