Dilated Convolution Neural Network for Remaining Useful Life Prediction

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Xin Xu ◽  
Qianhui Wu ◽  
Xiu Li ◽  
Biqing Huang
Keyword(s):  
Life Prediction ◽  
High Performance ◽  
Economic Loss ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Sensor Data ◽  
Training Time ◽  
Dilated Convolution ◽  
Useful Life ◽  
Accuracy Of Prediction

Abstract Accurate prediction of remaining useful life (RUL) plays an important role in reducing the probability of accidents and lessening the economic loss. However, traditional model-based methods for RUL are not suitable when operating conditions and fault models are complicated. To deal with this problem, this paper proposes a novel data-driven method based on a deep dilated convolution neural networks (D-CNN). The novelties of the proposed method are triple folds. First, no feature engineering is required, and the raw sensor data are directly used as the input of the model. Second the dilated convolutional structure is used to enlarge the receptive field and further improve the accuracy of prediction. Finally, time sequences are encoded by a 2D-convolution to extract higher-level features. Extensive experiments on the C-MAPSS dataset demonstrate that the proposed D-CNN achieves high performance while requiring less training time.

Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

2020 ◽  
pp. 1-1
Author(s):  
Naipeng Li ◽  
Yaguo Lei ◽  
Nagi Gebraeel ◽  
Zhijian Wang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
Useful Life

scholarly journals Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3035
Author(s):  
Feiyue Deng ◽  
Yan Bi ◽  
Yongqiang Liu ◽  
Shaopu Yang
Keyword(s):  
Deep Learning ◽  
Rapid Development ◽  
Operational Efficiency ◽  
Remaining Useful Life ◽  
Convolution Kernel ◽  
Training Time ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Convolution Kernels ◽  
Useful Life

Remaining useful life (RUL) prediction of key components is an important influencing factor in making accurate maintenance decisions for mechanical systems. With the rapid development of deep learning (DL) techniques, the research on RUL prediction based on the data-driven model is increasingly widespread. Compared with the conventional convolution neural networks (CNNs), the multi-scale CNNs can extract different-scale feature information, which exhibits a better performance in the RUL prediction. However, the existing multi-scale CNNs employ multiple convolution kernels with different sizes to construct the network framework. There are two main shortcomings of this approach: (1) the convolution operation based on multiple size convolution kernels requires enormous computation and has a low operational efficiency, which severely restricts its application in practical engineering. (2) The convolutional layer with a large size convolution kernel needs a mass of weight parameters, leading to a dramatic increase in the network training time and making it prone to overfitting in the case of small datasets. To address the above issues, a multi-scale dilated convolution network (MsDCN) is proposed for RUL prediction in this article. The MsDCN adopts a new multi-scale dilation convolution fusion unit (MsDCFU), in which the multi-scale network framework is composed of convolution operations with different dilated factors. This effectively expands the range of receptive field (RF) for the convolution kernel without an additional computational burden. Moreover, the MsDCFU employs the depthwise separable convolution (DSC) to further improve the operational efficiency of the prognostics model. Finally, the proposed method was validated with the accelerated degradation test data of rolling element bearings (REBs). The experimental results demonstrate that the proposed MSDCN has a higher RUL prediction accuracy compared to some typical CNNs and better operational efficiency than the existing multi-scale CNNs based on different convolution kernel sizes.

Remaining useful life prediction based on a multi-sensor data fusion model

2021 ◽  
Vol 208 ◽  
pp. 107249
Author(s):  
Naipeng Li ◽  
Nagi Gebraeel ◽  
Yaguo Lei ◽  
Xiaolei Fang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Data Fusion ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Fusion Model ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Useful Life

scholarly journals An Adaptive Model-Based Framework for Prognostics of Gas Path Faults in Aircraft Gas Turbine Engines

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Ogechukwu Alozie ◽  
Yi-Guang Li ◽  
Xin Wu ◽  
Xingchao Shong ◽  
Wencheng Ren
Keyword(s):  
Gas Turbine ◽  
Fault Isolation ◽  
Performance Model ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Sensor Data ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Degradation Models ◽  
Useful Life

This paper presents an adaptive framework for prognostics in civil aero gas turbine engines, which incorporates both performance and degradation models, to predict the remaining useful life of the engine components that fail predominantly by gradual deterioration over time. Sparse information about the engine configuration is used to adapt a performance model, which serves as a baseline for implementing optimum sensor selection, operating data correction, fault isolation, noise reduction and component health diagnostics using nonlinear Gas Path Analysis (GPA). Degradation models, which describe the progression of faults until failure, are then applied to the diagnosed component health indices from previous run-to-failure cases. These models constitute a training library from which fitness evaluation to the current test case is done. The final remaining useful life (RUL) prediction is obtained as a weighted sum of individually evaluated RULs for each training case. This approach is validated using dataset generated by the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software, which comprises both training and testing instances of run-to-failure sensor data for a turbofan engine, some of which are obtained at different operating conditions and for multiple fault modes. The results demonstrate the capability of improved prognostics of faults in aircraft engine turbomachinery using models of system behavior, with continuous health monitoring data.

scholarly journals A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 176 ◽  
Author(s):  
David Verstraete ◽  
Enrique Droguett ◽  
Mohammad Modarres
Keyword(s):  
Sensor Fusion ◽  
Asset Management ◽  
Life Prediction ◽  
Health Management ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Useful Life

Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.

scholarly journals Health Index Generation Based on Compressed Sensing and Logistic Regression for Remaining Useful Life Prediction

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Christian Knoebel ◽  
Daniel Strommenger ◽  
Johannes Reuter ◽  
Clemens Guehmann
Keyword(s):  
Logistic Regression ◽  
Compressed Sensing ◽  
Condition Monitoring ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Health State ◽  
Health Index ◽  
Electromagnetic Actuators ◽  
Useful Life

Extracting suitable features from acquired data to accurately depict the current health state of a system is crucial in data driven condition monitoring and prediction. Usually, analogue sensor data is sampled at rates far exceeding the Nyquist-rate containing substantial amounts of redundancies and noise, imposing high computational loads due to the subsequent and necessary feature processing chain (generation, dimensionality reduction, rating and selection). To overcome these problems, Compressed Sensing can be used to sample directly to a compressed space, provided the signal at hand and the employed compression/measurement system meet certain criteria. Theory states, that during this compression step enough information is conserved, such that a reconstruction of the original signal is possible with high probability. The proposed approach however does not rely on reconstructed data for condition monitoring purposes, but uses directly the compressed signal representation as feature vector. It is hence assumed that enough information is conveyed by the compression for condition monitoring purposes. To fuse the compressed coefficients into one health index that can be used as input for remaining useful life prediction algorithms and is limited to a reasonable range between 1 and 0, a logistic regression approach is used. Run-to-failure data of three translational electromagnetic actuators is used to demonstrate the health index generation procedure. A comparison to the time domain ground truth signals obtained from Nyquist sampled coil current measurements shows reasonable agreement. I.e. underlying wear-out phenomena can be reproduced by the proposed approach enabling further investigation of the application of prognostic methods.

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