با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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1 |
A novel deep learning method based on attention mechanism for bearing remaining useful life prediction
یک روش جدید یادگیری عمیق مبتنی بر مکانیسم توجه برای تحمل پیش بینی عمر مفید باقیمانده-2020 Rolling bearing is a key component in rotation machine, whose remaining useful life (RUL) prediction
is an essential issue of constructing condition-based maintenance (CBM) system. However, recent
data-driven approaches for bearing RUL prediction still require prior knowledge to extract features,
construct health indicate (HI) and set up threshold, which is inefficient in the big data era. In this paper,
a pure data-driven method for bearing RUL prediction with little prior knowledge is proposed. This
method includes three steps, i.e., features extraction, HI prediction and RUL calculation. In the first step,
five band-pass energy values of frequency spectrum are extracted as features. Then, a recurrent neural
network based on encoder–decoder framework with attention mechanism is proposed to predict HI
values, which are designed closely related with the RUL values in this paper. Finally, the final RUL
value can be obtained via linear regression. Experiments carried out on the dataset from PRONOSTIA
and comparison with other novel approaches demonstrate that the proposed method achieves a better
performance. Keywords: Remaining useful life prediction | Recurrent neural network | Attention mechanism |
مقاله انگلیسی |
2 |
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
پیش بینی عمر مفید باقی مانده: رویکرد یادگیری عمیق قابل تفسیر از طریق استنتاج های متغیر بیزی-2019 Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive
maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a
better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a
common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the
(conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice
because of its high explanatory power. A more accurate alternative is promised by machine learning, where
forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine
learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability.
As our primary contribution, we propose a structured-effect neural network for predicting the remaining
useful life which combines the favorable properties of both approaches: its key innovation is that it
offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational
Bayesian inferences. The different approaches are compared based on the actual time-to-failure for aircraft
engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss
implications for decision support. Keywords: Forecasting | Remaining useful life | Machine learning | Neural networks | Deep learning |
مقاله انگلیسی |
3 |
Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning
تجزیه و تحلیل داده های صنعتی بزرگ برای پیش بینی عمر مفید باقی مانده بر اساس یادگیری عمیق-2018 Due to the recent development of cyber-physical systems, big data, cloud computing, and
industrial wireless networks, a new era of industrial big data is introduced. Deep learning, which brought a
revolutionary change in computer vision, natural language processing, and a variety of other applications,
has significant potential for solutions providing in sophisticated industrial applications. In this paper,
a concept of device electrocardiogram (DECG) is presented, and an algorithm based on deep denoising
autoencoder (DDA) and regression operation is proposed for the prediction of the remaining useful life of
industrial equipment. First, the concept of electrocardiogram is explained. Then, a problem statement based
on manufacturing scenario is presented. Subsequently, the architecture of the proposed algorithm called
integrated DDA and the algorithm workflow are provided. Moreover, DECG is compared with traditional
factory information system, and the feasibility and effectiveness of the proposed algorithm are validated
experimentally. The proposed concept and algorithm combine typical industrial scenario and advance
artificial intelligence, which has great potential to accelerate the implementation of industry 4.0.
INDEX TERMS : Cyber-physical systems, deep learning, device electrocardiogram, industrial big data,industry 4.0 |
مقاله انگلیسی |