45,463 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
A simple state-based prognostic model for filter clogging
In today's maintenance planning, fuel filters are replaced or cleaned on a regular basis. Monitoring and implementation of prognostics on filtration system have the potential to avoid costs and increase safety. Prognostics is a fundamental technology within Integrated Vehicle Health Management (IVHM). Prognostic models can be categorised into three major categories: 1) Physics-based models 2) Data-driven models 3) Experience-based models. One of the challenges in the progression of the clogging filter failure is the inability to observe the natural clogging filter failure due to time constraint. This paper presents a simple solution to collect data for a clogging filter failure. Also, it represents a simple state-based prognostic with duration information (SSPD) method that aims to detect and forecast clogging of filter in a laboratory based fuel rig system. The progression of the clogging filter failure is created unnaturally. The degradation level is divided into several groups. Each group is defined as a state in the failure progression of clogging filter. Then, the data is collected to create the clogging filter progression states unnaturally. The SSPD method consists of three steps: clustering, clustering evaluation, and remaining useful life (RUL) estimation. Prognosis results show that the SSPD method is able to predicate the RUL of the clogging filter accurately
A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction
Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition
Prognostics: Design, Implementation, and Challenges
Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life
(RUL) of a system. It is also a key technology for an integrated vehicle health management (IVHM) system that leads
to improved safety and reliability. A vast amount of research has been presented in the literature to develop prognostics
models that are able to predict a system’s RUL. These models can be broadly categorised into experience-based models,
data-driven models and physics-based models. Therefore, careful consideration needs to be given to selecting which
prognostics model to take forward and apply for each real application. Currently, developing reliable prognostics models
in real life is challenging for various reasons, such as the design complexity associated with a system, the high uncertainty
and its propagation in the degradation, system level prognostics, the evaluation framework and a lack of prognostics
standards. This paper is written with the aim to bring forth the challenges and opportunities for developing prognostics
models for complex systems and making researchers aware of these challenges and opportunities
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
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