Generating Real-valued Failure Data for Prognostics Under the Conditions of Limited Data Availability

Abstract

Data-driven prognostics solutions underperform under the conditions of limited failure data availability since the number of failure data samples is insufficient for training prognostics models effectively. In order to address this problem, we present a novel methodology for generating real-valued failure data which allows training datasets to be augmented so that the number of failure data samples is increased. In contrast to existing data generation techniques which duplicate or randomly generate data, the proposed methodology is capable of generating new and realistic failure data samples. To this end, we utilised the conditional generative adversarial network and auxiliary information pertaining to the failure modes. The proposed methodology is evaluated in a real-world case study involving the prediction of air purge valve failures in heavy trucks. Two prognostics models are developed using gradient boosting machine and random forest classifiers. It is shown that when these models are trained on the augmented training dataset, they outperform the best prognostics solution previously proposed in the literature for the case study by a large margin. More specifically, costs due to breakdowns and false alarms are reduced by 44%.

Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis

“…In [31], Decision Tree (DT) classifier using the training data-set generated by GAN achieves comparable results to DT trained on original data-set. Conditional GAN (CGAN) has been developed in [32] for creating synthetic data for prognostics under the conditions of limited failure data availability.…”

“…This results in generating overlapped and noisy samples [51]. Consequently, SMOTE is not guaranteed to create realistic faulty data samples for manufacturing applications [32].…”

A Novel Linear Classifier for Class Imbalance Data Arising in Failure-Prone Air Pressure Systems

“…Ranasinghe and Parlikad [2] investigated the use of Conditional Generative Adversarial Network (CGAN) in APS failure prediction. They proposed the usage of CGAN for generating artificial samples of the minority class.…”

“…A critical domain in this research area, known as APS failure detection, is to detect whether an APS failure is the cause of the overall system failure or not. With the advent of Industrial Internet of Things (IIoT) and Industry 4.0, machine-learning based methods for APS failure detection (e.g., [2]) is gaining popularity. One of the major challenges in APS failure detection using machine learning is the presence of high-imbalance class distribution.…”

Evaluating Machine Learning Classification Using Sorted Missing Percentage Technique Based on Missing Data

“…About the limited data due to missing data, in contrast to existing data generation techniques. Ranasinghe and Parlikad (2019) proposed a methodology capable of generating new and realistic failure data samples [27].…”