7,588 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
Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks
In industrial applications, nearly half the failures of motors are caused by
the degradation of rolling element bearings (REBs). Therefore, accurately
estimating the remaining useful life (RUL) for REBs are of crucial importance
to ensure the reliability and safety of mechanical systems. To tackle this
challenge, model-based approaches are often limited by the complexity of
mathematical modeling. Conventional data-driven approaches, on the other hand,
require massive efforts to extract the degradation features and construct
health index. In this paper, a novel online data-driven framework is proposed
to exploit the adoption of deep convolutional neural networks (CNN) in
predicting the RUL of bearings. More concretely, the raw vibrations of training
bearings are first processed using the Hilbert-Huang transform (HHT) and a
novel nonlinear degradation indicator is constructed as the label for learning.
The CNN is then employed to identify the hidden pattern between the extracted
degradation indicator and the vibration of training bearings, which makes it
possible to estimate the degradation of the test bearings automatically.
Finally, testing bearings' RULs are predicted by using a -support
vector regression model. The superior performance of the proposed RUL
estimation framework, compared with the state-of-the-art approaches, is
demonstrated through the experimental results. The generality of the proposed
CNN model is also validated by transferring to bearings undergoing different
operating conditions
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification
This paper presents a study on power grid disturbance classification by Deep
Learning (DL). A real synchrophasor set composing of three different types of
disturbance events from the Frequency Monitoring Network (FNET) is used. An
image embedding technique called Gramian Angular Field is applied to transform
each time series of event data to a two-dimensional image for learning. Two
main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent
Neural Network) are tested and compared with two widely used data mining tools,
the Support Vector Machine and Decision Tree. The test results demonstrate the
superiority of the both DL algorithms over other methods in the application of
power system transient disturbance classification.Comment: An updated version of this manuscript has been accepted by the 2018
IEEE International Conference on Energy Internet (ICEI), Beijing, Chin
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