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

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 assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art on several metrics.Comment: Presented at 2nd ML for PHM Workshop at SIGKDD 2017, Halifax, Canad

Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters

Deep neural networks are prone to overfitting, especially in small training data regimes. Often, these networks are overparameterized and the resulting learned weights tend to have strong correlations. However, convolutional networks in general, and fully convolution neural networks (FCNs) in particular, have been shown to be relatively parameter efficient, and have recently been successfully applied to time series classification tasks. In this paper, we investigate the application of different regularizers on the correlation between the learned convolutional filters in FCNs using Batch Normalization (BN) as a regularizer for time series classification (TSC) tasks. Results demonstrate that despite orthogonal initialization of the filters, the average correlation across filters (especially for filters in higher layers) tends to increase as training proceeds, indicating redundancy of filters. To mitigate this redundancy, we propose a strong regularizer, using simple yet effective filter decorrelation. Our proposed method yields significant gains in classification accuracy for 44 diverse time series datasets from the UCR TSC benchmark repository

Economic disparity and CO2 emissions: The domestic energy sector in Greater Bangalore, India

Energy consumption constitutes one of the important sources of carbon dioxide emission which cause global warming. This paper analyses greenhouse gas (GHG) emissions due to energy consumption in the domestic sector considering household activities and socioeconomic parameters. A stratified random survey of 1967 households in Bangalore pertaining to the energy consumption reveals that annual per capita electricity consumption ranges from 9.64 to 2337 kW h/year with an average of 336 +/- 267 kW h/year. Emission from most of the wards (66 wards) is about 10-15 GM/year, while wards in ped-urban areas emit less than 10 Gg/year. Extrapolation of these, show that total carbon dioxide from all wards of Greater Bangalore accounts to 3350 Gg/Year. The energy consumption analyses reveal a proportional increase in the per capita energy consumption with the family income suggesting that economic levels in respective wards is an important parameter in the domestic energy consumption and also GHG emissions. Suggested interventions through large scale penetration of renewable sources of energy and energy conservation would help in reducing greenhouse gases and consequent warming of the Earth. (C) 2016 Elsevier Ltd. All rights reserved

Economic disparity and CO2 emissions: The domestic energy sector in Greater Bangalore, India

Energy consumption constitutes one of the important sources of carbon dioxide emission which cause global warming. This paper analyses greenhouse gas (GHG) emissions due to energy consumption in the domestic sector considering household activities and socioeconomic parameters. A stratified random survey of 1967 households in Bangalore pertaining to the energy consumption reveals that annual per capita electricity consumption ranges from 9.64 to 2337 kW h/year with an average of 336 +/- 267 kW h/year. Emission from most of the wards (66 wards) is about 10-15 GM/year, while wards in ped-urban areas emit less than 10 Gg/year. Extrapolation of these, show that total carbon dioxide from all wards of Greater Bangalore accounts to 3350 Gg/Year. The energy consumption analyses reveal a proportional increase in the per capita energy consumption with the family income suggesting that economic levels in respective wards is an important parameter in the domestic energy consumption and also GHG emissions. Suggested interventions through large scale penetration of renewable sources of energy and energy conservation would help in reducing greenhouse gases and consequent warming of the Earth. (C) 2016 Elsevier Ltd. All rights reserved