Learning representations with end-to-end models for improved remaining
useful life prognostics
- URL: http://arxiv.org/abs/2104.05049v1
- Date: Sun, 11 Apr 2021 16:45:18 GMT
- Title: Learning representations with end-to-end models for improved remaining
useful life prognostics
- Authors: Alaaeddine Chaoub, Alexandre Voisin, Christophe Cerisara, Beno\^it
Iung
- Abstract summary: The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure.
We propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL.
We will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.
- Score: 64.80885001058572
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The remaining Useful Life (RUL) of equipment is defined as the duration
between the current time and its failure. An accurate and reliable prognostic
of the remaining useful life provides decision-makers with valuable information
to adopt an appropriate maintenance strategy to maximize equipment utilization
and avoid costly breakdowns. In this work, we propose an end-to-end deep
learning model based on multi-layer perceptron and long short-term memory
layers (LSTM) to predict the RUL. After normalization of all data, inputs are
fed directly to an MLP layers for feature learning, then to an LSTM layer to
capture temporal dependencies, and finally to other MLP layers for RUL
prognostic. The proposed architecture is tested on the NASA commercial modular
aero-propulsion system simulation (C-MAPSS) dataset. Despite its simplicity
with respect to other recently proposed models, the model developed outperforms
them with a significant decrease in the competition score and in the root mean
square error score between the predicted and the gold value of the RUL. In this
paper, we will discuss how the proposed end-to-end model is able to achieve
such good results and compare it to other deep learning and state-of-the-art
methods.
Related papers
- Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction [53.88231294380083]
We introduce a novel Multi-Epoch learning with Data Augmentation (MEDA) framework, suitable for both non-continual and continual learning scenarios.
MEDA minimizes overfitting by reducing the dependency of the embedding layer on subsequent training data.
Our findings confirm that pre-trained layers can adapt to new embedding spaces, enhancing performance without overfitting.
arXiv Detail & Related papers (2024-06-27T04:00:15Z) - Supervised Contrastive Learning based Dual-Mixer Model for Remaining
Useful Life Prediction [3.081898819471624]
The Remaining Useful Life (RUL) prediction aims at providing an accurate estimate of the remaining time from the current predicting moment to the complete failure of the device.
To overcome the shortcomings of rigid combination for temporal and spatial features in most existing RUL prediction approaches, a spatial-temporal homogeneous feature extractor, named Dual-Mixer model, is proposed.
The effectiveness of the proposed method is validated through comparisons with other latest research works on the C-MAPSS dataset.
arXiv Detail & Related papers (2024-01-29T14:38:44Z) - Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data.
One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is.
This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
arXiv Detail & Related papers (2024-01-18T08:12:23Z) - To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs.
We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting.
Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
arXiv Detail & Related papers (2023-05-22T17:02:15Z) - On the Soundness of XAI in Prognostics and Health Management (PHM) [0.0]
This work presents a critical and comparative revision on a number of XAI methods applied on time series regression model for Predictive Maintenance.
The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification.
The model used during the experimentation is a DCNN trained to predict the Remaining Useful Life of an aircraft engine.
arXiv Detail & Related papers (2023-03-09T13:27:54Z) - SAL-CNN: Estimate the Remaining Useful Life of Bearings Using
Time-frequency Information [0.0]
In modern industrial production, the prediction ability of the remaining useful life (RUL) of bearings directly affects the safety and stability of the system.
In this paper, an end-to-end RUL prediction method is proposed, which uses short-time Fourier transform (STFT) as preprocessing.
Considering the time correlation of signal sequences, a long and short-term memory network is designed in CNN, incorporating the convolutional block attention module.
arXiv Detail & Related papers (2022-04-11T12:27:31Z) - Accurate Remaining Useful Life Prediction with Uncertainty
Quantification: a Deep Learning and Nonstationary Gaussian Process Approach [0.0]
Remaining useful life (RUL) refers to the expected remaining lifespan of a component or system.
We devise a highly accurate RUL prediction model with uncertainty quantification, which integrates and leverages the advantages of deep learning and nonstationary Gaussian process regression (DL-NSGPR)
Our computational experiments show that the DL-NSGPR predictions are highly accurate with root mean square error 1.7 to 6.2 times smaller than those of competing RUL models.
arXiv Detail & Related papers (2021-09-23T18:19:58Z) - Imputation-Free Learning from Incomplete Observations [73.15386629370111]
We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation.
We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation.
Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
arXiv Detail & Related papers (2021-07-05T12:44:39Z) - Uncertainty-aware Remaining Useful Life predictor [57.74855412811814]
Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset can be expected to operate.
In this work, we consider Deep Gaussian Processes (DGPs) as possible solutions to the aforementioned limitations.
The performance of the algorithms is evaluated on the N-CMAPSS dataset from NASA for aircraft engines.
arXiv Detail & Related papers (2021-04-08T08:50:44Z) - Long short-term memory networks and laglasso for bond yield forecasting:
Peeping inside the black box [10.412912723760172]
We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks.
We calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures.
arXiv Detail & Related papers (2020-05-05T14:23:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.