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IEEE Transactions on Instrumentation and Measurement, 72, 2507612p. (2023) DOI:10.1109/TIM.2023.3249224
An effective health assessment guarantees the high accuracy of remaining useful life (RUL) prediction of machinery components. The key to health assessment is the health indicator (HI) of machinery components, which are generally constructed by feature fusion from the time and frequency domains in sensorial data. However, the existing HI construction methods are constrained due to the low-sampling rate and the susceptibility to environmental disturbances of sensorial data. To draw these issues, this article mainly proposes an HI construction method combining condensed image coding and a metrics-constrained deep learning model for improving the accuracy of RUL prediction. First, to extract more information in time series under a low-sampling rate, a recurrence plot-gray (RP-G) image coding is extended from RP images to extract and fuse both the global and local dynamics at multiple scales in time series. Then, a nested residual-convolution autoencoder (NR-CAE) model is proposed to extract degradation-related information from RP-G images containing information disturbed by the environment. Finally, to build a more solid connection between the image-based features and the RUL prediction, a metric-constrained gated recurrent unit (MC-GRU) is proposed by considering characteristics related to RUL prediction and temporal relationships in RP-G image-based features. A commercial case of wind turbine gearboxes in Liaoning, China, is investigated and demonstrates that the proposed method owns the ability to construct reliable HI of machinery components for accurate RUL prediction, using low-sampling sensorial data under environmental disturbances.
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