Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery

Wang, Yuan; Lei, Yaguo; Li, Naipeng; Yan, Tao; Si, Xiaosheng

doi:10.1016/j.ress.2022.109006

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…The fusion of RNN and CNN can be achieved through either a serial connection or a parallel layout, depending on the specific requirements of the task. (4) Multiple parallel subnets: Wang et al [155] constructed multiple parallel subnets, each stacked with TCN and BiLSTM in series. This approach avoids information confusion among multi-source signals and extracts spatiotemporal features separately.…”

Section: Typical Dl-based Methodsmentioning

confidence: 99%

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Zhou,

Yang,

Qian

et al. 2024

Meas. Sci. Technol.

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Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets. A crucial component of PdM is the remaining useful life (RUL) prediction for machines, which has garnered increasing attention. With the rapid advancements in industrial Internet of Things (IoT) and artificial intelligence (AI) technologies, RUL prediction methods, particularly those based on pattern recognition (PR) technology, have made significant progress. However, a comprehensive review that systematically analyzes and summarizes these state-of-the-art PR-based prognostic methods is currently lacking. To address this gap, this paper presents a comprehensive review of PR-based RUL prediction methods. Firstly, it summarizes commonly used evaluation indicators based on accuracy metrics, prediction confidence metrics, and prediction stability metrics. Secondly, it provides a comprehensive analysis of typical machine learning methods and deep learning networks employed in RUL prediction. Furthermore, it delves into cutting-edge techniques, including advanced network models and frontier learning theories in RUL prediction. Finally, the paper concludes by discussing the current main challenges and prospects in the field. The intended audience of this article includes practitioners and researchers involved in machinery PdM, aiming to provide them with essential foundational knowledge and a technical overview of the subject matter.

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Section: Typical Dl-based Methodsmentioning

confidence: 99%

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Zhou,

Yang,

Qian

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…To verify the prediction performance of the proposed framework and quantify the difference between TTAFPred and contrastive models, three performance evaluation metrics, i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R 2 , are used to measure the prediction ability (Wang et al, 2023). For MAE and RMSE, the lower these values are, the higher the prediction accuracy of the model is.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Since TTAFPred aims to predict specific values of TTAF, we mainly compare TTAF-Pred with some state-of-the-art regression models by using collected run-to-failure data. Specially, we constructed seven different baseline models as comparisons in this study, including MLP (Yan, 2020a), LSTM (Qiao et al, 2018;Vinícius et al, 2022), GRU (Chung et al, 2014), 1DCNN (Espinosa et al, 2021), BiGRU (Zhang et al, 2022), BiLSTM (Jin et al, 2022;Wang et al, 2023), and 1DCNN-BiGRU-Attention , since they are commonly used models in regression prediction.…”

Section: Baseline Modelsmentioning

confidence: 99%

TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network

Jia,

Yu,

Zhang

et al. 2023

Preprint

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Software aging refers to the performance degradation and failure crash phenomena in long-running systems. As a proactive remedy, software rejuvenation can be scheduled timely to mitigate aging effects. Inescapably, how to accurately predict the time to aging failure (TTAF) of software is a prerequisite for implementing effective rejuvenation. However, the characterization of software aging is relatively complicated, leading to the selection of aging indicators case by case, which means that only fitting the variation trend of a single indicator for prediction models to formulate a rejuvenation schedule may be limited. To fill this gap, this paper proposes a novel framework called TTAFPred, which directly constructs the direct mapping relationships between the software aging process considering multiple system indicators and TTAF. Specifically, this framework includes three parts, i.e., data preprocessing, software degradation feature extraction , and TTAF prediction modules. First, the raw data is processed into the input form required by the network. Secondly, a temporal relationship extraction stream integrating bidirectional gated recurrent unit (BiGRU) with attention mechanism is used to extract temporal features from raw inputs. Synchronously, a spatial relationships extraction stream is adopted to extract the spatial features 1 for enhancing the representation ability of degraded features by using the multi-scale one-dimensional convolutional neural network (1DCNN) with the residual connection. Then, extracted temporal-spatial features from the two streams are further fused. Finally, two fully-connected layers are constructed to estimate the TTAF. The experiments are performed on two mainstream software systems (OpenStack and Android), and four sets of real run-to-failure data for each software system are collected. The effectiveness of the proposed TTAFPred is verified through extensive experiments with its seven competing models, and the prediction performance can be improved by 9.1%, 8.0%, and 8.0%, respectively, in terms of three evaluation metrics, compared to the best baseline model.

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“…For rotating machinery, such as an aero-engine, gas turbine, or machine tool [1], advanced and effective on-line condition monitoring methods are essential to detect the faults and deterioration of rotor systems, including shaft fractures, turbine disc cracks, rubimpact between the rotor and stator, and bearing spalling [2]. On-line condition monitoring is crucial to achieve the safe operation of rotor systems, reduce downtime and improve production efficiency [3].…”

Section: Introductionmentioning

confidence: 99%

On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis

Zhao,

Liu,

Zhang

et al. 2024

Nonlinear Dyn

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This paper proposes a novel on-line rotor system condition monitoring approach using nonlinear data-driven modelling and model frequency analysis. First, the dynamic process model of the vibration transmission path between the vibration measurement points of two fulcrum structures is established by utilizing nonlinear data-driven modelling. Then, the unique frequency properties are extracted from the established model to reveal, in real time, the health condition of the rotor system. Finally, using the frequency properties as features, the unsupervised learning technology is applied to the on-line monitoring of the rotor system. Compared to conventional condition monitoring methods, the proposed approach can output an early warning 26 min before a shaft fracture occurs, without generating false alarms. Consequently, this approach can greatly enhance diagnostic accuracy, demonstrating its potential to contribute to the advancement of rotor system condition monitoring techniques.

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Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery

Cited by 17 publications

References 22 publications

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network

On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis

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