Citation: Cho, A.D.; Carrasco, R.A.;
Ruz, G.A. A RUL Estimation System
from Clustered Run-to-Failure
Degradation Signals. Sensors 2022, 22,
5323. https://doi.org/10.3390/
s22145323
Academic Editors: Ningyun Lu,
Hamed Badihi and Tao Chen
Received: 30 May 2022
Accepted: 14 July 2022
Published: 16 July 2022
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Article
A RUL Estimation System from Clustered Run-to-Failure
Degradation Signals
Anthony D. Cho
1,2
, Rodrigo A. Carrasco
1,3
and Gonzalo A. Ruz
1,4,5,
*
1
Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Santiago 7941169, Chile;
acholo@alumnos.uai.cl (A.D.C.); rax@uai.cl (R.A.C.)
2
Faculty of Sciences, Engineering and Technology, Universidad Mayor, Santiago 7500994, Chile
3
School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
4
Data Observatory Foundation, Santiago 7941169, Chile
5
Center of Applied Ecology and Sustainability (CAPES), Santiago 8331150, Chile
* Correspondence: gonzalo.ruz@uai.cl
Abstract:
The prognostics and health management disciplines provide an efficient solution to improve
a system’s durability, taking advantage of its lifespan in functionality before a failure appears.
Prognostics are performed to estimate the system or subsystem’s remaining useful life (RUL). This
estimation can be used as a supply in decision-making within maintenance plans and procedures.
This work focuses on prognostics by developing a recurrent neural network and a forecasting method
called Prophet to measure the performance quality in RUL estimation. We apply this approach to
degradation signals, which do not need to be monotonical. Finally, we test our system using data
from new generation telescopes in real-world applications.
Keywords: prognostics; fault detection; recurrent neural networks; prophet
1. Introduction
Modern industry has evolved significantly in the past decades, building more complex
systems with greater functionality. This evolution has added many sensors for better control,
higher system reliability, and information availability. Given this improvement in data
availability, new adequate maintenance policies can be developed [
1
]. Thus, maintenance
policies have evolved from waiting to fix the system when a failure appears (known as
reactive maintenance) to predictive maintenance, where intervention is scheduled with the
information obtained from fault detection methods.
Various researchers confirm that sensors play a crucial role in preserving the proper
functioning of the system or subsystem [
2
,
3
] as they provide information about the oper-
ating status in real-time such as possible failure patterns, level of degradation, abnormal
states of operation, and others. Taking this into account, various methodologies have been
developed for fault detection [
4
], testability design for fault diagnosis [
5
,
6
], detection of
fault conditions malfunction using deep learning techniques [
7
,
8
], and test selection design
for fault detection and isolation [
9
], just to name a few. Most of them share the same goal of
being able to help increase the reliability, availability, and performance of a system.
The two main extensions of predictive maintenance are Condition Based Mainte-
nance (CBM) and Prognostics and Health Management (PHM); both terms have been
used as a substitute for predictive maintenance in the literature [
10
,
11
]. According to
Jimenez et al. [
11
], they aligned these terms by adopting predictive maintenance as the first
term to refer to a maintenance strategy, CBM as an extension of predictive maintenance,
and adding alarms to warn when there is a fault in the system. Later, Vachtsevanos and
Wang [
12
] introduced prognostics algorithms as tools for predicting the time-to-failure
of components; from this insight emerged PHM [
13
] as an extension of CBM to improve
the predictability and remaining useful life (RUL) estimation of a component in question
Sensors 2022, 22, 5323. https://doi.org/10.3390/s22145323 https://www.mdpi.com/journal/sensors
