Remaining Useful Life Estimation Using Functional Data Analysis

TL;DR

This paper introduces a novel Functional Data Analysis method called functional MLP for estimating the Remaining Useful Life of equipment, leveraging continuous process modeling to improve accuracy over existing algorithms.

Contribution

The paper proposes a new FDA-based functional MLP approach that models sensor data as continuous processes, capturing intra- and inter-equipment correlations for better RUL estimation.

Findings

Functional MLP outperforms state-of-the-art methods on NASA C-MAPSS data.

FDA approach captures time-varying relationships between sensors and RUL.

Method demonstrates superior accuracy in RUL prediction tasks.

Abstract

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.