Search Results for author: Ahmed Farahat

Found 10 papers, 1 papers with code

An ensemble of convolution-based methods for fault detection using vibration signals

no code implementations5 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.

Fault Detection Time Series +1

CDA: Contrastive-adversarial Domain Adaptation

no code implementations10 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.

Contrastive Learning Domain Adaptation

An Offline Deep Reinforcement Learning for Maintenance Decision-Making

no code implementations28 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.

Decision Making reinforcement-learning +1

Data-driven Residual Generation for Early Fault Detection with Limited Data

no code implementations28 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.

Fault Detection Time Series Analysis

Deep Time Series Models for Scarce Data

no code implementations16 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.

Model Selection Time Series +1

Wisdom of the Ensemble: Improving Consistency of Deep Learning Models

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.

Health Indicator Forecasting for Improving Remaining Useful Life Estimation

no code implementations5 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.

Generative Adversarial Networks for Failure Prediction

no code implementations4 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.

imbalanced classification Management

Remaining Useful Life Estimation Using Functional Data Analysis

no code implementations12 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.

Management Time Series +1

Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling

no code implementations18 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.

Multi-Task Learning

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