activity
20162020
most citedMulti-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder

165 citations · 303 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2020★ 24 cited

Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation

Jyoti Narwariya, Pankaj Malhotra, Vishnu TV +2

Automated equipment health monitoring from streaming multisensor time-series data can be used to enable condition-based maintenance, avoid sudden catastrophic failures, and ensure…

cs.LG2019

Meta-Learning for Few-Shot Time Series Classification

Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig +2

Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered pra…

cs.LG2019

Meta-Learning for Black-box Optimization

Vishnu TV, Pankaj Malhotra, Jyoti Narwariya +2

Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks inc…

cs.LG2019★ 2 cited

Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models

Vishnu TV, Diksha, Pankaj Malhotra +2

Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of…

cs.LG2017

Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

Narendhar Gugulothu, Vishnu TV, Pankaj Malhotra +3

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assump…

cs.LG2017★ 112 cited

TimeNet: Pre-trained deep recurrent neural network for time series classification

Pankaj Malhotra, Vishnu TV, Lovekesh Vig +2

Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained…

cs.LG2016★ 165 cited

Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder

Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan +4

Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g.…