151 research outputs found

    CNN-LSTM-based prognostics of bidirectional converters for electric vehicles’ machine

    Get PDF
    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

    Get PDF
    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

    Full text link
    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

    Full text link
    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

    Get PDF
    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

    Get PDF
    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
    • …
    corecore