151 research outputs found
Remaining useful life estimation using Long Short-term Memory (LSTM) neural networks and deep fusion
CNN-LSTM-based prognostics of bidirectional converters for electric vehicles’ machine
This paper proposes an approach to estimate the state of health of DC-DC converters that
feed the electrical system of an electric vehicle. They have an important role in providing a smooth
and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status
and predict the future performance of the converter and specifically of the electrolytic capacitors,
in order to avoid malfunctioning and failures, since it is known they have the highest failure rates
among power converter components. To this end, accelerated aging tests of the electrolytic capacitors
are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM
model that is capable of predicting the future values of the capacitance and the equivalent series
resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful
life of the device, thus, increasing the reliability of the system and ensuring an adequate operating
condition of the electric motor.Peer ReviewedPostprint (published version
Data-driven Models for Remaining Useful Life Estimation of Aircraft Engines and Hard Disk Drives
Failure of physical devices can cause inconvenience, loss of money, and sometimes even deaths. To improve the reliability of these devices, we need to know the remaining useful life (RUL) of a device at a given point in time. Data-driven approaches use data from a physical device to build a model that can estimate the RUL. They have shown great performance and are often simpler than traditional model-based approaches. Typical statistical and machine learning approaches are often not suited for sequential data prediction. Recurrent Neural Networks are designed to work with sequential data but suffer from the vanishing gradient problem over time. Therefore, I explore the use of Long Short-Term Memory (LSTM) networks for RUL prediction. I perform two experiments. First, I train bidirectional LSTM networks on the Backblaze hard-disk drive dataset. I achieve an accuracy of 96.4\% on a 60 day time window, state-of-the-art performance. Additionally, I use a unique standardization method that standardizes each hard drive instance independently and explore the benefits and downsides of this approach. Finally, I train LSTM models on the NASA N-CMAPSS dataset to predict aircraft engine remaining useful life. I train models on each of the eight sub-datasets, achieving a RMSE of 6.304 on one of the sub-datasets, the second-best in the current literature. I also compare an LSTM network\u27s performance to the performance of a Random Forest and Temporal Convolutional Neural Network model, demonstrating the LSTM network\u27s superior performance. I find that LSTM networks are capable predictors for device remaining useful life and show a thorough model development process that can be reproduced to develop LSTM models for various RUL prediction tasks. These models will be able to improve the reliability of devices such as aircraft engines and hard-disk drives
A Transformer-based Framework For Multi-variate Time Series: A Remaining Useful Life Prediction Use Case
In recent times, Large Language Models (LLMs) have captured a global
spotlight and revolutionized the field of Natural Language Processing. One of
the factors attributed to the effectiveness of LLMs is the model architecture
used for training, transformers. Transformer models excel at capturing
contextual features in sequential data since time series data are sequential,
transformer models can be leveraged for more efficient time series data
prediction. The field of prognostics is vital to system health management and
proper maintenance planning. A reliable estimation of the remaining useful life
(RUL) of machines holds the potential for substantial cost savings. This
includes avoiding abrupt machine failures, maximizing equipment usage, and
serving as a decision support system (DSS). This work proposed an
encoder-transformer architecture-based framework for multivariate time series
prediction for a prognostics use case. We validated the effectiveness of the
proposed framework on all four sets of the C-MAPPS benchmark dataset for the
remaining useful life prediction task. To effectively transfer the knowledge
and application of transformers from the natural language domain to time
series, three model-specific experiments were conducted. Also, to enable the
model awareness of the initial stages of the machine life and its degradation
path, a novel expanding window method was proposed for the first time in this
work, it was compared with the sliding window method, and it led to a large
improvement in the performance of the encoder transformer model. Finally, the
performance of the proposed encoder-transformer model was evaluated on the test
dataset and compared with the results from 13 other state-of-the-art (SOTA)
models in the literature and it outperformed them all with an average
performance increase of 137.65% over the next best model across all the
datasets
Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction
A core part of maintenance planning is a monitoring system that provides a
good prognosis on health and degradation, often expressed as remaining useful
life (RUL). Most of the current data-driven approaches for RUL prediction focus
on single-point prediction. These point prediction approaches do not include
the probabilistic nature of the failure. The few probabilistic approaches to
date either include the aleatoric uncertainty (which originates from the
system), or the epistemic uncertainty (which originates from the model
parameters), or both simultaneously as a total uncertainty. Here, we propose
ensemble neural networks for probabilistic RUL predictions which considers both
uncertainties and decouples these two uncertainties. These decoupled
uncertainties are vital in knowing and interpreting the confidence of the
predictions. This method is tested on NASA's turbofan jet engine CMAPSS
data-set. Our results show how these uncertainties can be modeled and how to
disentangle the contribution of aleatoric and epistemic uncertainty.
Additionally, our approach is evaluated on different metrics and compared
against the current state-of-the-art methods.Comment: 6 pages, 2 figures, 2 tables, conference proceedin
Degradation Vector Fields with Uncertainty Considerations
The focus of this work is on capturing uncertainty in remaining useful life (RUL) estimates for machinery and constructing some latent dynamics that aid in interpreting those results. This is primarily achieved through sequential deep generative models known as Dynamical Variational Autoencoders (DVAEs). These allow for the construction of latent dynamics related to the RUL estimates while being a probabilistic model that can quantify the uncertainties of the estimates
Survey on Deep Learning applied to predictive maintenance
Prognosis Health Monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0
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