Domain Adaptation in Predicting Turbocharger Failures Using Vehicle’s Sensor Measurements



Published Jun 29, 2022
Mahmoud Rahat Peyman Sheikholharam Mashhadi Sławomir Nowaczyk Thorsteinn Rognvaldsson Atabak Taheri Ataollah Abbasi


The discrepancy in the distribution of source and target domains is usually referred to as a domain shift. It is one of the reasons for the inferior performance of machine learning solutions at deployment. We illustrate that the domain shift issue is pertinent to the readings of the vehicles’ operational sensors. This is due to the fact that these measurements are collected over a period of time and are susceptible to various changes that happen in the meantime. Examples of these changes are usage pattern variations, aging of the vehicles, seasonal shifts, and driver changes. However, domain adversarial neural networks (DANN) have shown promising results to reduce the negative impact of the domain shift. The present study investigates domain adaptation (DA) in the predictive maintenance field by estimating the remaining useful life (RUL) of turbochargers. The devices are operating on a fleet of VOLVO trucks, and the information about their services is collected over four years between 2016 and 2019. The input features to the model are a set of bi-weekly collected measurements called logged vehicle data (LVD). The contributions of this paper are two-fold. First, we propose a new approach for detecting domain (covariate) shift using an autoencoder. Second, we adapt domain adversarial neural networks to the specific application of predicting turbocharger failures. Finally, we deploy a recurrent feature extraction layer in the DANN architecture to incorporate temporal aspect of the data. The experimental results demonstrate the superiority of the proposed method over the traditional approach.

How to Cite

Rahat, M. ., Mashhadi, P. S. ., Nowaczyk, S. ., Rognvaldsson, T. ., Taheri, A. ., & Abbasi, A. . (2022). Domain Adaptation in Predicting Turbocharger Failures Using Vehicle’s Sensor Measurements. PHM Society European Conference, 7(1), 432–439.
Abstract 36 | PDF Downloads 42



Remaining useful life, Prognostics, turbocharger, Domain adversarial neural networks

Technical Papers