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Mechanical element’s remaining useful life prediction using a hybrid approach of CNN and LSTM

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Abstract

For the safety and reliability of the system, Remaining Useful Life (RUL) prediction is considered in many industries. The traditional machine learning techniques must provide more feature representation and adaptive feature extraction. Deep learning techniques like Long Short-Term Memory (LSTM) achieved an excellent performance for RUL prediction. However, the LSTM network mainly relies on the past few data, which may only capture some contextual information. This paper proposes a hybrid combination of Convolution Neural Network (CNN) and LSTM (CNN+LSTM) to solve this problem. The proposed hybrid model predicts how long a machine can operate without breaking down. In the proposed work, 1D horizontal and vertical signals of the mechanical bearing are first converted to 2D images using Continuous Wavelet Transform (CWT). These 2D images are applied to CNN for key feature extraction. Ultimately, these key features are applied to the LSTM deep neural network for predicting the RUL of a mechanical bearing. A PRONOSTIA data is utilized to demonstrate the performance of the proposed model and compare the proposed model with other state-of-the-art methods. Experimental results show that our proposed CNN+LSTM-based hybrid model achieved higher accuracy (98%) with better robustness than existing methods.

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Data Availability

The data used for experimental purposes is available as an open source at: https://paperswithcode.com/dataset/pronostia-bearing-dataset

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Authors and Affiliations

  1. School of Engineering and Sciences (SEAS), Department of Computer Science and Engineering, SRM University-AP, Amaravati, Andhra Pradesh, India

    Neeraj Kumar Sharma & Sriramulu Bojjagani

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  1. Neeraj Kumar Sharma
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  2. Sriramulu Bojjagani
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Correspondence to Neeraj Kumar Sharma.

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Sharma, N.K., Bojjagani, S. Mechanical element’s remaining useful life prediction using a hybrid approach of CNN and LSTM. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18546-9

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