Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network

Wang, Jiujian; Wen, Guanghui; Yang, Shaopu; Liu, Yongqiang

doi:10.1109/phm-chongqing.2018.00184

Cited by 148 publications

(76 citation statements)

References 14 publications

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“…The same number of filters is used for all the convolution layers in the same dashed box. The initial number of filters can be determined by Equation (38). The number of filters used for the convolution layer in the three dashed boxes are Num Filters , 2 × Num Filters , and 3 × Num Filters respectively.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

2020

IEEE Access

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The prediction of remaining useful life (RUL) of mechanical equipment provides a timely understanding of the equipment degradation and is critical for predictive maintenance of the equipment. In recent years, the applications of deep learning (DL) methods to predict equipment RUL have attracted much attention. There are two major challenges when applying the DL methods for RUL prediction: (1) It is difficult to select the prediction model structure and hyperparameters such as network depth, learning rate, batch size, and etc. (2) The developed prediction model is domain dependent, i.e., it can only give good prediction performance in one data domain (one particular type of working conditions and fault modes). In order to meet the challenges, a novel RUL prediction method developed using a deep convolutional neural network (DCNN) combined with Bayesian optimization and adaptive batch normalization (AdaBN) is presented in this paper. The proposed RUL prediction model is validated by the turbofan engine degradation simulation dataset provided by NASA. The prediction results show that the proposed prediction model provides better prediction results than model structures obtained by random search and grid search. The results also show that the domain adaptation capability of the prediction model has been improved. INDEX TERMS Remaining useful life prediction, Bayesian optimization, adaptive batch normalization, domain adaptation.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…The commonly used linear RUL function was replaced by the piecewise linear RUL function. According to the literature [38], the maximum service life of the engine is set to 125. As shown in Fig.…”

Section: Validation Of the Proposed Methods A C-mapss Dataset Desmentioning

confidence: 99%

A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

2020

IEEE Access

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“…The hyperparameters of the model are tuned and optimized by Genetic Algorithms (GA). The results showed that the combination of RBM and LSTM achieves the state-of-the-art score function (SF) and root mean squared error (RMSE) (Wang et al [26]). The authors showed that Bi-LSTM's hidden layers are able to implicitly extract degradation features without unsupervised pretraining of the model.…”

Section: Current Deep Learning Solutionsmentioning

confidence: 99%

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Rengasamy

Jafari

Rothwell

et al. 2020

Sensors

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Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions.

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“…The characteristics of the LSTM make it a natural choice for machinery RUL estimation due to the considerable time lag between inputs and their corresponding outputs. A simple LSTM is employed in [65] and a bidirectional LSTM is proposed in [66] for RUL estimation. A hybrid deep learning model combining CNN and LSTM is demonstrated for machine health monitoring in [67], where a CNN is employed for local features extraction and bi-directional LSTM [68] is demonstrated and built on CNN outputs for the temporal information encoding and representation learning.…”

Section: B Machine Learning Based Approachesmentioning

confidence: 99%

Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion

et al. 2020

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Estimation of Remaining Useful Life (RUL) is a crucial task in Prognostics and Health Management (PHM) for condition-based maintenance of machinery. In order to transmit and store the sensor data for archiving and long term analysis, data compression techniques are regularly used to reduce the requirements of bandwidth, energy and storage in modern remote PHM systems. In these systems the challenge arises of how the compressed sensor data affects the RUL estimation algorithms. A main drawback of conventional statistical modeling approaches is that they require expert prior knowledge and a significant number of assumptions. Alternative regression based approaches and deep neural networks are known to have issues when modeling long-term dependencies in the sequential data. Recently Long Short-Term Memory (LSTM) neural networks have been proposed to overcome these issues and in this paper we create a LSTM network and data fusion approach that can estimate the RUL with compressed (distorted) data. The experimental results indicate that the proposed method is able to estimate RUL reliably with narrower error bands compared to other state-of-the-art approaches. Moreover, the proposed method is able to predict RUL from both the raw and compressed datasets with comparable accuracy. INDEX TERMS Machine health monitoring, remaining useful life (RUL), long-short term memory, recurrent neural network, data compression.

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Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network

Cited by 148 publications

References 14 publications

A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion

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