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Journal ArticleDOI: 10.1080/24725854.2020.1766729

A Bayesian deep learning framework for interval estimation of remaining useful life in complex systems by incorporating general degradation characteristics

04 Mar 2021-Vol. 53, Iss: 3, pp 326-340
Abstract: Deep learning has emerged as a powerful tool to model complicated relationships between inputs and outputs in various fields including degradation modeling and prognostics. Existing deep learning-b...

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Topics: Prognostics (63%), Deep learning (53%), Interval estimation (51%)
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Open accessJournal ArticleDOI: 10.3390/ELECTRONICS10010039
29 Dec 2020-Electronics
Abstract: Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

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Topics: Deep learning (57%), Prognostics (55%), Support vector machine (53%)

7 Citations


Journal ArticleDOI: 10.1080/00224065.2021.1960934
Minhee Kim1, Jing-Ru C. Cheng2, Kaibo Liu1Institutions (2)
Abstract: Recent advances in sensor technology have made it possible to monitor the degradation of a system using multiple sensors simultaneously. Accordingly, many neural network-based prognostic models hav...

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Topics: Prognostics (64%), Recurrent neural network (53%)

2 Citations


Journal ArticleDOI: 10.1016/J.RESS.2021.107871
Lei Xiao1, Junxuan Tang1, Xinghui Zhang, Eric Bechhoefer  +1 moreInstitutions (1)
Abstract: The accurate remaining useful life (RUL) prediction is the foundation of prognostics and health management (PHM). The accuracy of RUL prediction model depends on not only the quality and quantity of degradation feature but also the prediction model. In most of the existing deep-learning based RUL prediction models, noise is considered harmful and has to be removed. Further, the correlation among sensory measurements is ignored. However, noise can boost the prediction performance if judiciously used. This paper proposes a new RUL prediction method where noise is intentionally added into a long short-term memory (LSTM) network. Additionally, correlation analysis is conducted among the sensory measurements to construct new degradation features as the inputs of the LSTM network. Validation of the proposed method was carried out on the C-MAPSS aero-engine lifetime dataset. Finally, the proposed RUL prediction model is compared to other the-state-of-the-art techniques.

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Topics: Prognostics (53%), Noise (50%)

2 Citations


Journal ArticleDOI: 10.1016/J.MEASUREMENT.2021.110276
01 Jan 2022-Measurement
Abstract: In the Engineering discipline, prognostics play an essential role in improving system safety, reliability and enabling predictive maintenance decision-making. Due to the adoption of emerging sensing techniques and big data analytics tools, data-driven prognostic approaches are gaining popularity. This paper aims to deliver an extensive review of recent advances and trends of data-driven machine prognostics, with a focus on their applications in practice. The primary purpose of this review is to categorize existing literature and report the latest research progress and directions to support researchers and practitioners in acquiring a clear comprehension of the subject area. This paper first summarizes fundamental methodologies on data-driven approaches for predictive maintenance. Then, the article further conducts a comprehensive investigation on the different fields of applications of machine prognostics. Finally, a discussion on the challenges, opportunities, and future trends of predictive maintenance is presented to conclude this paper.

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Topics: Prognostics (70%), Predictive maintenance (56%), Big data (52%)

1 Citations


Open accessJournal ArticleDOI: 10.1155/2021/8815241
Gang Zhang1, Weige Liang1, Bo She1, Fuqing Tian1Institutions (1)
Abstract: Remaining useful life (RUL) prediction plays a significant role in developing the condition-based maintenance and improving the reliability and safety of machines. This paper proposes a remaining useful life prediction scheme combining deep-learning-based health indicator and a new relevance vector machine. First, both one-dimensional time-series information and two-dimensional time-frequency maps are input into a hybrid deep-learning structure network consisting of convolutional neural network (CNN) and long short-term memory network (LSTM) to construct health indicator (HI). Then, the prediction results and confidence interval are calculated by a new RVM enhanced by a polynomial regression model. The proposed method is verified by the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed method in improving the prediction accuracy and analyzing the prediction uncertainty.

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Topics: Relevance vector machine (56%), Deep learning (53%)

1 Citations


References
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39 results found


Journal ArticleDOI: 10.1162/NECO.1989.1.4.541
Yann LeCun1, Bernhard E. Boser1, John S. Denker1, D. Henderson1  +3 moreInstitutions (1)
01 Dec 1989-Neural Computation
Abstract: The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.

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Topics: Backpropagation (52%)

7,328 Citations


Journal ArticleDOI: 10.1109/72.279181
Abstract: Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases. These results expose a trade-off between efficient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered. >

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Topics: Vanishing gradient problem (64%), Gradient descent (64%), Recurrent neural network (63%) ... show more

5,798 Citations


Journal ArticleDOI: 10.1162/089976600300015015
01 Oct 2000-Neural Computation
Abstract: Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset. Without resets, the state may grow indefinitely and eventually cause the network to break down. Our remedy is a novel, adaptive "forget gate" that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review illustrative benchmark problems on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve continual versions of these problems. LSTM with forget gates, however, easily solves them, and in an elegant way.

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Topics: Recurrent neural network (52%)

3,135 Citations


Open accessBook
09 Feb 2012-
Abstract: Recurrent neural networks are powerful sequence learners. They are able to incorporate context information in a flexible way, and are robust to localised distortions of the input data. These properties make them well suited to sequence labelling, where input sequences are transcribed with streams of labels. The aim of this thesis is to advance the state-of-the-art in supervised sequence labelling with recurrent networks. Its two main contributions are (1) a new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and (2) an extension of the long short-term memory network architecture to multidimensional data, such as images and video sequences.

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Topics: Recurrent neural network (57%)

1,682 Citations


Journal ArticleDOI: 10.1080/00401706.1993.10485038
C. Joseph Lu1, William O. Meeker1Institutions (1)
01 May 1993-Technometrics
Abstract: Some life tests result in few or no failures. In such cases, it is difficult to assess reliability with traditional life tests that record only time to failure. For some devices, it is possible to obtain degradation measurements over time, and these measurements may contain useful information about product reliability. Even with little or no censoring, there may be important practical advantages to analyzing degradation data. If failure is defined in terms of a specified level of degradation, a degradation model defines a particular time-to-failure distribution. Generally it is not possible to obtain a closed-form expression for this distribution. The purpose of this work is to develop statistical methods for using degradation measures to estimate a time-to-failure distribution for a broad class of degradation models. We use a nonlinear mixed-effects model and develop methods based on Monte Carlo simulation to obtain point estimates and confidence intervals for reliability assessment.

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Topics: Point estimation (50%)

990 Citations


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