LSTM RNNs model tool wear transition and observation functions from vibration data to enable one- and two-step ahead predictions and RUL estimation, outperforming simple RNNs.
Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder
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abstract
Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g., exponential degradation. However, in many domains degradation may not follow a pattern. We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is used to compute HI which is then used for RUL estimation. We evaluate our approach on publicly available Turbofan Engine and Milling Machine datasets. We also present results on a real-world industry dataset from a pulverizer mill where we find significant correlation between LSTM-ED based HI and maintenance costs.
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eess.SP 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Online subspace tracking models turbo-engine damage via health index deviations from a static low-dimensional manifold and estimates RUL on CMAPSS datasets with claimed complexity reduction.
citing papers explorer
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Recurrent Neural Networks with Long Term Temporal Dependencies in Machine Tool Wear Diagnosis and Prognosis
LSTM RNNs model tool wear transition and observation functions from vibration data to enable one- and two-step ahead predictions and RUL estimation, outperforming simple RNNs.
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Online Subspace Tracking for Damage Propagation Modeling and Predictive Analytics: Big Data Perspective
Online subspace tracking models turbo-engine damage via health index deviations from a static low-dimensional manifold and estimates RUL on CMAPSS datasets with claimed complexity reduction.