  Remaining Useful Life (RUL) of an equipment or one of its components is
defined as the time left until the equipment or component reaches its end of
useful life. Accurate RUL estimation is exceptionally beneficial to Predictive
Maintenance, and Prognostics and Health Management (PHM). Data driven
approaches which leverage the power of algorithms for RUL estimation using
sensor and operational time series data are gaining popularity. Existing
algorithms, such as linear regression, Convolutional Neural Network (CNN),
Hidden Markov Models (HMMs), and Long Short-Term Memory (LSTM), have their own
limitations for the RUL estimation task. In this work, we propose a novel
Functional Data Analysis (FDA) method called functional Multilayer Perceptron
(functional MLP) for RUL estimation. Functional MLP treats time series data
from multiple equipment as a sample of random continuous processes over time.
FDA explicitly incorporates both the correlations within the same equipment and
the random variations across different equipment's sensor time series into the
model. FDA also has the benefit of allowing the relationship between RUL and
sensor variables to vary over time. We implement functional MLP on the
benchmark NASA C-MAPSS data and evaluate the performance using two
popularly-used metrics. Results show the superiority of our algorithm over all
the other state-of-the-art methods.
