Vibration-based anomaly pattern mining for remaining useful life (RUL) prediction in bearings

Kamat, Pooja; Kumar, Satish; Sugandhi, Rekha

doi:10.1007/s40430-024-04872-4

J Braz. Soc. Mech. Sci. Eng.

2024

DOI: 10.1007/s40430-024-04872-4

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Vibration-based anomaly pattern mining for remaining useful life (RUL) prediction in bearings

Pooja Kamat,

Satish Kumar,

Rekha Sugandhi

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Cited by 2 publications

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Remaining useful life prognostics of bearings based on convolution attention networks and enhanced transformer

Sun,

Tang,

et al. 2024

Heliyon

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Remaining useful life prognostics of bearings based on convolution attention networks and enhanced transformer

Sun,

Tang,

et al. 2024

Heliyon

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A method for predicting remaining useful life using enhanced Savitzky–Golay filter and improved deep learning framework

Li,

Wang,

Wang

et al. 2024

Sci Rep

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Ensuring operational integrity in large-scale equipment hinges on effective fault prediction and health management. Prognostics and health management (PHM) face the challenge of accurately predicting remaining useful life (RUL) using multivariate sensor data. Traditional methods often require extensive prior knowledge for indicator construction and processing. Deep learning offers a promising alternative. This study presents a multi-channel multi-scale deep learning approach. Initially, an improved Savitzky‒Golay filter (ISG) addresses challenges posed by large and rapidly changing data volumes, enhancing data preprocessing. Subsequently, a framework integrates convolutional neural networks (CNNs) with long short-term memory (LSTM) to capture hierarchical signal information and make integrated predictions. The CNN extracts spatial features from multi-channel input data, while the LSTM captures temporal dependencies. By fusing outputs from both components, the framework enhances predictive accuracy and robustness for complex operational datasets. Experimental validation on the C-MAPSS dataset tests various fusion methods and CNN depths, determining parameters and evaluating filtering effectiveness. Comparative analyses show promising performance, particularly under dynamic conditions. While not optimal for predicting multiple fault types, it outperforms classical algorithms, especially in single fault type prediction tasks.

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Vibration-based anomaly pattern mining for remaining useful life (RUL) prediction in bearings

Cited by 2 publications

References 48 publications

Remaining useful life prognostics of bearings based on convolution attention networks and enhanced transformer

Remaining useful life prognostics of bearings based on convolution attention networks and enhanced transformer

A method for predicting remaining useful life using enhanced Savitzky–Golay filter and improved deep learning framework

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