TrajecNets: Online Failure Evolution Analysis in 2D Space

Nauman Shahid and Anarta Ghosh
Publication Target: 
Publication Issue: 
Special Issue on Deep Learning and Emerging Analytics
Submission Type: 
Full Paper
ijphm_19_029.pdf2.38 MBJanuary 5, 2020 - 9:57am

We propose a novel Recurrent Neural Network (RNN) based autoencoder for embedding the run-to-failure time series sensor data in a 2D feature space. The embedding, extracted
from the network, is in the form of a smooth trajectory, which represents the temporal evolution of data from healthy to failure states, hence the name TrajecNets. The visualizable 2D trajectory can be used directly for highly intuitive and interpretable health monitoring, which can in turn be used for Remaining Useful Life (RUL) estimation task, without compromising the performance. We also propose a novel unsupervised failure prediction methodology which uses the 2D trajectories and health curve of the time series to compute evolving failure mode probabilities. Together, the visualizable 2D trajectories and the interpretable failure mode probabilities, health curve and RUL are envisaged to provide system and maintenance engineers, insight into failure dynamics. Experiments on NASA CMAPSS Turbofan benchmark dataset show promising results on degradation tracking, health monitoring, failure prediction and RUL estimation tasks.

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Submission Keywords: 
Remaining Useful Life Estimation
failure evolution
Predictive Maintenance; Prognostics; Remaining Useful Life Estimation; Machine learning; Deep learning; Recurrent Neural Networks; Aeronautics; Case Study
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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