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A new approach for remaining useful life prediction of bearings using 1D-ternary patterns with LSTM

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Bearings frequently experience malfunctions in mechanical systems, directly impacting system performance. Accurate prediction of bearing failures is crucial for maintenance planning and preventing unexpected system breakdowns. Data-driven prognostic techniques are commonly employed to estimate the remaining useful life (RUL) of high-speed bearings. RUL prediction relies on establishing the fundamental relationship between bearing degradation and its current health status, with the accuracy depending on effective feature extraction from the bearing data. In this study, a novel approach is proposed for the RUL prediction of bearings. The 1D-TP method is applied to vibration signals, resulting in two feature vectors, LOWER and UPPER, which are then utilized in combination with LSTM for RUL prediction. The proposed approach is evaluated using a dataset from the PRONOSTIA platform, and performance metrics including MAE, RMSE, SMAPE, RA, and Score are determined. The results demonstrate that the 1D-TP + LSTM method successfully predicts the remaining life of bearings. Accurate RUL assessment and reliability analysis aid personnel in making informed maintenance decisions, preventing losses from mechanical system damage, improving production safety, and safeguarding the mechanical system from harm.

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Availability of data and materials

The dataset analyzed during the study is available in the NASA web site (https://www.nasa.gov/intelligent-systems-division.

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Authors and Affiliations

  1. School of Technical Sciences, Siirt University, 56100, Siirt, Turkey

    Eyyüp Akcan

  2. Faculty of Engineering and Architecture, Batman University, 72100, Batman, Turkey

    Yılmaz Kaya

Authors
  1. Eyyüp Akcan
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  2. Yılmaz Kaya
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Contributions

EA: Proposed Method, Write Manuscript, Checking language. YK: Organized manuscript, Coding, Check manuscript.

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Correspondence to Yılmaz Kaya.

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The authors declare that they have no conflict of interest.

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Technical Editor: Jarir Mahfoud.

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Akcan, E., Kaya, Y. A new approach for remaining useful life prediction of bearings using 1D-ternary patterns with LSTM. J Braz. Soc. Mech. Sci. Eng. 45, 378 (2023). https://doi.org/10.1007/s40430-023-04309-4

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  • DOI: https://doi.org/10.1007/s40430-023-04309-4

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