An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

Abstract

The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statistical methods and machine learning techniques. Then, the extracted knowledge can be used for predictive maintenance or prognostic health management. However, it is difficult to gather complete data due to several issues in IIoT, such as devices breaking down, running out of battery, or undergoing scheduled maintenance. Data with missing values are often ignored, as they may contain insufficient information from which to draw conclusions. In order to overcome these issues, we propose a novel, effective missing data handling mechanism for the concepts of symmetry principles. While other existing methods only attempt to estimate missing parts, the proposed method generates a whole set of data set using Gaussian process regression and a generative adversarial network. In order to prove the effectiveness of the proposed framework, we examine a real-world, industrial case involving an air pressure system (APS), where we use the proposed method to make quality predictions and compare the results with existing state-of-the-art estimation methods.

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

“…The model finds all patterns, that is, succession of failure messages related to given target, and from them, it creates all possible combinations of patterns increasing the training dataset for the given target. Other example is presented in Oh and Lee (2020). First, it estimates missing values using Gaussian Process Regression (GPR), and then, it uses DL, specifically a Generative Adversarial Network (GAN), to perform data augmentation.…”

“…The interest in PdM has result in numerous research works in last decade, and specifically from 2018, that apply DM predictive techniques to different applications: motors (Aremu, Hyland‐Wood, & McAree, 2020; Pang et al, 2020; Wang, Zhang, et al, 2020), manufacturing plants (Axenie et al, 2020; Yu et al, 2020), medical equipment (Shamayleh et al, 2020), energy production plants (de Carvalho Chrysostomo et al, 2020; Gohel et al, 2020) or vehicle fleets (C. Chen et al, 2020; Oh & Lee, 2020) among others. However, there is not a direct application of DM to PdM.…”

A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data

“…GPR thus provides a non-parametric method for accommodating statistical uncertainty measures in a regression problem. It has been used in a wide range of applications and has been found to produce superior results when used to interpolate missing data in multivariate timeseries [11], [47].…”

Generative Adversarial Network-based Missing Data Handling and Remaining Useful Life Estimation for Smart Train Control and Monitoring Systems

“…Missing input and output data issues have to be resolved prior to training a deep learning-based predictive maintenance model. is is one of the most common issues in manufacturing [22][23][24], transportation, and other data handling processes. Table 4 summaries various methods for handling missing values.…”

“…Several research studies applied GAN for generating fault data in automotive [22], semiconductor [23], and steel production processes [24]. is study used GAN to handle the missing value issues in the TCMS data as well as to predict RULs in train components.…”