Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering

Baptista, Márcia; Henriques, Elsa; Medeiros, Ivo Paixão de; Malere, Joao P.; Nascimento, Cairo Lúcio; Prendinger, Helmut

doi:10.1016/j.ress.2018.01.017

Cited by 93 publications

(33 citation statements)

References 12 publications

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“…Sensors can monitor the vibration signal, acoustic signal, temperature and other status information of the device through wired or wireless data transmission technology. Data mining technologies and information fusion technologies are used to analyze and process the information collected from the equipment and to construct HI through the information, which can well evaluate the degradation level of equipment and predict the remaining life [1,2].As a common component in mechanical systems, rolling bearing works in complex environments, and the probability of failure is high. Its state can directly affect the safe and smooth operation of the entire system [3].…”

Section: Introductionmentioning

confidence: 99%

A method for constructing rolling bearing lifetime health indicator based on multi-scale convolutional neural networks

Wu¹,

Feng²,

Wu³

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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The degradation of rolling bearings is complex, and traditional methods of degenerating feature degradation are highly dependent on previous research and expertise. However, the traditional methods' ability for learning the complex relationship between degraded features and large amounts of measured data is limited. So, it is difficult to construct a single health indicator (HI) to predict the degradation state of the bearing. In order to solve this problem, a multi-scale convolutional neural network (MSCNN) is proposed, which uses the frequency signal as input to construct HI. In combination with the "bathtub curve," the HI we proposed is constructed by the inverse hyperbolic tangent function (atanh) method which is different from the proportional HI construction method. Five kinds of processing methods, such as original vibration signal, frequency signal and three kinds of modal decomposition preprocessed signals, are tested with PHM 2012 bearing lifetime data. And the proposed method is compared with other three HI construction methods. The experimental results show that under the HI construction method of atanh, the MSCNN model with the input of frequency signal can better represent the degradation state of the bearing compared with other methods. Meanwhile, the remaining life prediction is more accurate compared.

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Section: Introductionmentioning

confidence: 99%

A method for constructing rolling bearing lifetime health indicator based on multi-scale convolutional neural networks

Wu¹,

Feng²,

Wu³

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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“…Thus, remaining useful life (RUL) is very crucial for predicting health of the rotating machinery in the long term, which can provide early warning. We can then act to shut down the machinery in advance and avoid catastrophic consequences [ 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery

Gao

Feng

et al. 2018

Entropy

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To reduce the maintenance cost and safeguard machinery operation, remaining useful life (RUL) prediction is very important for long term health monitoring. In this paper, we introduce a novel hybrid method to deal with the RUL prediction for health management. Firstly, the sparse reconstruction algorithm of the optimized Lasso and the Least Square QR-factorization (Lasso-LSQR) is applied to compressed sensing (CS), which can realize the sparse optimization for long term health monitoring data. After the sparse signal is reconstructed, the minimum entropy de-convolution (MED) is used to identify the fault characteristics and to obtain significant fault information from the machinery operation. Health indicators with Skip-over, sample entropy and approximate entropy are then performed to track the degradation of the machinery process. The performance analysis of the Skip-over is superior to other indicators. Finally, Fractal Autoregressive Integrated Moving Average model (FARIMA) is employed to predict the Skip-over using the R/S method. The analysis results evidence that the novel hybrid method yields a good performance, and such method can achieve highly accurate RUL prediction and safeguard machinery operation for long term monitoring.

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“…A support vector machine was applied to predict the RUL of a Li-ion battery [30] and a microwave component [31]. Another study investigated the applicability of the Kalman filter to fuse the estimates of the RUL from five learning methods such as generalized linear models, neural networks, K-nearest neighbors, random forests, and support vector machines, using the field data of an aircraft bleed valve [15].This literature review indicated that the process of developing data-driven diagnostics and prognostics methods involved some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine-learning in the fault-type and/or RUL prediction problems. In addition, the best performing algorithm in each subtask was varied across the characteristics of the given dataset.…”

mentioning

confidence: 99%

“…fault-type classification [10][11][12][13][14] and RUL estimation [15][16][17][18]). Hybrid-based approaches attempt to utilize the strengths of both approaches, if applicable, by combining knowledge related to the physical process and information obtained from the observed data (see example studies about fault-type classification [19][20][21][22] and RUL prediction [23][24][25][26]).…”

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confidence: 99%

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A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Trinh

Kwon

2020

Applied Sciences

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Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.

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Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering

Cited by 93 publications

References 12 publications

A method for constructing rolling bearing lifetime health indicator based on multi-scale convolutional neural networks

A method for constructing rolling bearing lifetime health indicator based on multi-scale convolutional neural networks

Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

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