A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction

Gao, Yuanyuan; Wen, Yuxin; Wu, Jianguo

doi:10.1109/tnnls.2020.2977132

Cited by 46 publications

(16 citation statements)

References 31 publications

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“…To further improve the prediction performance, many researchers applied artificial neural networks (ANNs) to predict the RUL [15]. For example, Gao et al [16] proposed a joint prognostic model, where ANN is applied to model the non-linear relationship between the multiple sensory signals and RUL. Li et al [17] presented a shape-constrained neural network to construct a composite health index, which facilitates the following-up RUL prediction.…”

Section: A Neural-network-based Approaches For Diagnosis and Rul Pred...mentioning

confidence: 99%

A Deep Branched Network for Failure Mode Diagnostics and Remaining Useful Life Prediction

Yue

et al. 2022

IEEE Trans. Instrum. Meas.

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In complex systems, the operating units often suffer from multiple failure modes, and each failure mode results in distinct degradation path and service life. Thus, it is critical to perform the failure mode diagnostics and predict the remaining useful life (RUL) accordingly in modern industrial systems. However, most of the existing approaches consider the prognostic problem under a single failure mode, or treat the failure mode classification and RUL prediction as two independent tasks, despite the fact that they are closely related and should be synergistically performed to enhance the generalization performance. Motivated by these issues, we propose a deep branched network (DBNet) for failure mode classification and RUL prediction. In this approach, the two tasks are jointly learned in a sequential manner, wherein the feature extraction layers are shared by both tasks, while the neural network branches into individualized subnetworks for RUL prediction of each mode based on the output of the diagnostic subnetwork. Different from the traditional multi-task learning-based methods, where the diagnostics and RUL prediction are performed in parallel, the proposed DBNet innovatively couples these two tasks sequentially to boost the prognostic accuracy. The effectiveness of the proposed method is thoroughly demonstrated and evaluated on an aircraft gas turbine engine with multiple failure modes.

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Section: A Neural-network-based Approaches For Diagnosis and Rul Pred...mentioning

confidence: 99%

A Deep Branched Network for Failure Mode Diagnostics and Remaining Useful Life Prediction

Yue

et al. 2022

IEEE Trans. Instrum. Meas.

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“…Remote Sens. 2021, 13, x FOR PEER REVIEW 3 of 18 speed and multistep prediction [23,24]. At the same time, the change of drift rate is cumulative, and the drift rate has different influence on the future drift rate at different historical times.…”

Section: Description Of Research Objectmentioning

confidence: 99%

“…The neural network has strong nonlinear fitting ability and can map any complex nonlinear relationship. It has the advantages of strong fault tolerance, fast prediction speed and multistep prediction [23,24]. At the same time, the change of drift rate is cumulative, and the drift rate has different influence on the future drift rate at different historical times.…”

Section: Introductionmentioning

confidence: 99%

A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

et al. 2021

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An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

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“…The RUL is estimated from condition data measured by different kinds of sensors placed on the component [ 15 ]. The RUL prediction method can be classified into three types, namely model-based, data-driven, and hybrid methods [ 16 ]. The model-based methods consist of developing physical and statistical models to represent the degradation process of a component, which are then used to estimate the RUL.…”

Section: Introductionmentioning

confidence: 99%

A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction

Chen

2023

Sensors

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Health assessment and remaining useful life prediction are usually seen as separate tasks in industrial systems. Some multitask models use common features to handle these tasks synchronously, but they lack the usage of the representation in different scales and time-frequency domain. A lack of balance also exists among these scales. Therefore, a gated multiscale multitask learning model known as GMM-Net is proposed in this paper. By using the time frequency representation, GMM-Net can obtain features of different scales via different kernels and compose the features by a gating network. A detailed loss function whose weight can be searched in a smaller scale is designed. The model is tested with different weights in the total loss function, and an optimal weight is found. Using this optimal weight, it is observed that the proposed method converges to a smaller loss and has a smaller model size than long short-term memory (LSTM) and gated recurrent unit (GRU) with less training time. The experiment results demonstrate the effectiveness of the proposed method

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A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction

Cited by 46 publications

References 31 publications

A Deep Branched Network for Failure Mode Diagnostics and Remaining Useful Life Prediction

A Deep Branched Network for Failure Mode Diagnostics and Remaining Useful Life Prediction

A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction

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