A Gaussian Process Based Method for Data- Efficient Remaining Useful Life Estimation

Abstract

The task of remaining useful life (RUL) estimation is a major challenge within the field of prognostics and health management (PHM). The quality of the RUL estimates determines the economical feasibility of the application of predictive maintenance strategies, that rely on accurate predictions. Hence, many effective methods for RUL estimation have been developed in the recent years. Especially deep learning methods have been among the best performing ones setting new record accuracies on bench mark data sets. However, those approaches often rely on numerous and representative run-to-failure sequences of the components under investigation. In real-world use cases, this kind of data (i.e. run-to-failure sequences and RUL labels) is hardly ever present. Therefore, this paper proposes a new, data-efficient method, which is based on Gaussian process classification to derive abstract health indicator (HI) values in a first step, and warped, monotonic Gaussian process regression for indirect RUL estimation in a second step. The proposed approach does neither rely on entire run-to-failure sequences nor on any RUL labels and was tested on the benchmark C-MAPSS turbo fan and FEMTO bearing data sets, achieving comparable results to the state-ofthe art whilst using only a small fraction of the available training data. Hence, the proposed approach allows RUL estimation in use cases, in which gathering enough failure data for the application of deep learning models is infeasible.

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

“…The proposed framework places a higher emphasis on the latter stages through assigning non-linear temporal class boundaries between the five wear state classes. This has been achieved using one minus the negative exponential function, as shown in Equation (14). The proposed bearing lifetime model is non-linear, where class one (healthy) represents the entire first 63.2% of the bearing's lifetime.…”

“…Data-driven bearing prognostic systems are constructed using signal processing techniques with real measured sensor acquired signals, to analyse and detect trends providing valuable evidence of system degradation [12,13]. Sensing modalities to acquire bearing degradation signatures that have been widely explored in recent years include vibration signals [1,3,14,15], acoustic emissions [16,17], stator current measurements [18][19][20], thermalimaging [21], and multiple sensor fusion [22,23]. Of these, vibration signals, acquired from mounted accelerometers is often attributed as the most favourable approach for conditionbased monitoring (CbM) in general, due to the non-invasive nature of the measurement data, low cost, robustness and ease of implementation in practice [24].…”

“…In recent years, a variety of Machine Learning (ML) algorithms have been proposed to perform the task of bearing RUL predictions. These algorithms vary in complexity and efficiency and often incorporate supervised ML approaches such as k-Nearest Neighbour (k-NN) and Support Vector Machines (SVM) [3,15,16], regression model approaches [14,39], deep learning models including convolutional neural networks (CNN) and deep belief networks (DBN) [41,42], blind deconvolution methods [43,44], and Bayesian probabilistic prediction models such as Kalman and particle filtering [45,46]. This paper introduces a low complexity prognostic monitoring approach for RUL estimation of mechanical rolling element bearings that builds further on previous work by the authors, which achieved RUL classification accuracy percentages of 74.3% using signal EA combined with novel feature engineering techniques [1].…”

Data mining in predictive maintenance systems: A taxonomy and systematic review

“…Thus, the model specifies a prior function, calculates the posterior function using training data, and computes the predictive posterior distribution on the points of interest. In Benker et al (2021), it is used a GPR model with the aim of estimating RUL. The proposed approach focuses on data efficiency, needing very less data and time to achieve the competitive results compared to the state of the art.…”

Remaining Useful Life Prediction using Gaussian Process Regression Model

“…Another interesting multi step framework is proposed wherein the first step uses a Gaussian process clas-sification (GPC) model to determine if component is healthy or degraded based on HIs. Then, another multiple input GPR model is used to predict the RUL (Benker, Bliznyuk, & Zaeh, 2021). Finally, in a RUL study on lubricating oil, a multiple output GPR (MO-GPR) model is used that correlates the historical degradation trends with current degradation trends (Tanwar & Raghavan, 2020).…”