91,363 research outputs found
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
Predictive Maintenance on the Machining Process and Machine Tool
This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces
Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of
batteries is necessary to ensure reliable system operation and to minimise
maintenance costs. The complex nature of battery degradation has meant that
mechanistic modelling of capacity fade has thus far remained intractable;
however, with the advent of cloud-connected devices, data from cells in various
applications is becoming increasingly available, and the feasibility of
data-driven methods for battery prognostics is increasing. Here we propose
Gaussian process (GP) regression for forecasting battery state of health, and
highlight various advantages of GPs over other data-driven and mechanistic
approaches. GPs are a type of Bayesian non-parametric method, and hence can
model complex systems whilst handling uncertainty in a principled manner. Prior
information can be exploited by GPs in a variety of ways: explicit mean
functions can be used if the functional form of the underlying degradation
model is available, and multiple-output GPs can effectively exploit
correlations between data from different cells. We demonstrate the predictive
capability of GPs for short-term and long-term (remaining useful life)
forecasting on a selection of capacity vs. cycle datasets from lithium-ion
cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201
Verbal Autopsy Methods with Multiple Causes of Death
Verbal autopsy procedures are widely used for estimating cause-specific
mortality in areas without medical death certification. Data on symptoms
reported by caregivers along with the cause of death are collected from a
medical facility, and the cause-of-death distribution is estimated in the
population where only symptom data are available. Current approaches analyze
only one cause at a time, involve assumptions judged difficult or impossible to
satisfy, and require expensive, time-consuming, or unreliable physician
reviews, expert algorithms, or parametric statistical models. By generalizing
current approaches to analyze multiple causes, we show how most of the
difficult assumptions underlying existing methods can be dropped. These
generalizations also make physician review, expert algorithms and parametric
statistical assumptions unnecessary. With theoretical results, and empirical
analyses in data from China and Tanzania, we illustrate the accuracy of this
approach. While no method of analyzing verbal autopsy data, including the more
computationally intensive approach offered here, can give accurate estimates in
all circumstances, the procedure offered is conceptually simpler, less
expensive, more general, as or more replicable, and easier to use in practice
than existing approaches. We also show how our focus on estimating aggregate
proportions, which are the quantities of primary interest in verbal autopsy
studies, may also greatly reduce the assumptions necessary for, and thus
improve the performance of, many individual classifiers in this and other
areas. As a companion to this paper, we also offer easy-to-use software that
implements the methods discussed herein.Comment: Published in at http://dx.doi.org/10.1214/07-STS247 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
- …