no code implementations • 5 May 2023 • Xian Yeow Lee, Aman Kumar, Lasitha Vidyaratne, Aniruddha Rajendra Rao, Ahmed Farahat, Chetan Gupta
This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig.
no code implementations • 10 Jan 2023 • Nishant Yadav, Mahbubul Alam, Ahmed Farahat, Dipanjan Ghosh, Chetan Gupta, Auroop R. Ganguly
Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains.
no code implementations • 28 Sep 2021 • Hamed Khorasgani, HaiYan Wang, Chetan Gupta, Ahmed Farahat
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years.
no code implementations • 28 Sep 2021 • Hamed Khorasgani, Ahmed Farahat, Chetan Gupta
Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation.
no code implementations • 16 Mar 2021 • Qiyao Wang, Ahmed Farahat, Chetan Gupta, Shuai Zheng
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research.
1 code implementation • NeurIPS 2020 • Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz, Ahmed Farahat, Mahbubul Alam, Chetan Gupta, Jiangzhuo Chen, Madhav Marathe
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance.
no code implementations • 5 Jun 2020 • Qiyao Wang, Ahmed Farahat, Chetan Gupta, Hai-Yan Wang
In these approaches, health indicator forecasting that constructs the health indicator curve over the lifespan using partially observed measurements (i. e., health indicator values within an initial period) plays a key role.
no code implementations • 4 Oct 2019 • Shuai Zheng, Ahmed Farahat, Chetan Gupta
GAN-FP first utilizes two GAN networks to simultaneously generate training samples and build an inference network that can be used to predict failures for new samples.
no code implementations • 12 Apr 2019 • Qiyao Wang, Shuai Zheng, Ahmed Farahat, Susumu Serita, Chetan Gupta
In this work, we propose a novel Functional Data Analysis (FDA) method called functional Multilayer Perceptron (functional MLP) for RUL estimation.
no code implementations • 18 Dec 2018 • Karan Aggarwal, Onur Atan, Ahmed Farahat, Chi Zhang, Kosta Ristovski, Chetan Gupta
Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as a long-term prediction task to estimate how much time is left in the useful life of the equipment and (2) Failure prediction (FP) as a short-term prediction task to assess the probability of a failure within a pre-specified time window.