Impact of pulse time uncertainty on synchronous average: Statistical analysis and relevance to rotating machinery diagnosis

Camerini, V.; Coppotelli, Giuliano; Bendisch, S.; Kiehn, Daniel

doi:10.1016/j.ymssp.2019.04.017

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…The frequency of the gathered keywords from the published papers at the 2021 International Conference on Learning Representations is depicted in figure 1. As shown, deep learning ranks top, receiving the most attention from researchers, resulting in a substantial number of publications on this subject [34][35][36].…”

Section: Definitions and Structures Of Udtlmentioning

confidence: 99%

An unsupervised intelligent fault diagnosis research for rotating machinery based on NND-SAM method

Zhang¹,

Zou²,

Sang³

et al. 2022

Meas. Sci. Technol.

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Currently, intelligent fault diagnostics of rotating machinery have significantly contributed. However, real-world labeled data obtained from high-value equipment such as gas turbine units, pumps, and other rotating components are occasionally insufficient. This article proposes an unsupervised deep transfer learning model that can directly extract features from the data itself, hence reducing the number of samples required. The well-designed neural network with domain-specific antagonism mechanism(NND-SAM) would align features between the source and target domains and then make data-driven decisions more efficiently. The parameter-free gradient reversal layer (GRL) is used as an optimizer, therefore considerably reducing the cross-domain discrepancy and accelerating convergence. The average multi-classification accuracy under transferable conditions reaches 97 % and 91% after being conducted on two cases of fault diagnosis. Moreover, the F-score of the system improves by more than 4 % while simultaneously reducing the amount of time required in comparison to the existing models The results reveal that the suggested strategy is suitable for the challenging unlabeled dataset and represents a significant improvement over existing unsupervised learning techniques.

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Section: Definitions and Structures Of Udtlmentioning

confidence: 99%

An unsupervised intelligent fault diagnosis research for rotating machinery based on NND-SAM method

Zhang¹,

Zou²,

Sang³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…The failure of bearings during the equipment's operation not only poses a direct threat to the safety of the machinery but also leads to significant economic losses. Hence, the accurate prediction of the remaining useful life (RUL) of bearings is essential for the effective prognosis and health management of the equipment [1,2].…”

Section: Introductionmentioning

confidence: 99%

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

Xu,

Ma,

Chen

et al. 2024

Lubricants

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The remaining useful life (RUL) of bearings is vital for the manipulation and maintenance of industrial machines. The existing domain adaptive methods have achieved major achievements in predicting RUL to tackle the problem of data distribution discrepancy between training and testing sets. However, they are powerless when the target bearing data are not available or unknown for model training. To address this issue, we propose a single-source domain generalization method for RUL prediction of unknown bearings, termed as the adaptive stage division and parallel reversible instance normalization model. First, we develop the instance normalization of the vibration data from bearings to increase data distribution diversity. Then, we propose an adaptive threshold-based degradation point identification method to divide the healthy and degradation stages of the run-to-failure vibration data. Next, the data from degradation stages are selected as training sets to facilitate the RUL prediction of the model. Finally, we combine instance normalization and instance denormalization of the bearing data into a unified GRU-based RUL prediction network for the purpose of leveraging the distribution bias in instance normalization and improving the generalization performance of the model. We use two public datasets to verify the proposed method. The experimental results demonstrate that, in the IEEE PHM Challenge 2012 dataset experiments, the prediction accuracy of our model with the average RMSE value is 1.44, which is 11% superior to that of the suboptimal comparison model (Transformer model). It proves that our model trained on one-bearing data achieves state-of-the-art performance in terms of prediction accuracy on multiple bearings.

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“…Generally, an external trigger signal, provided by a tachometer, is used for synchronous sampling. This external reference signal should be stable so that effective synchronization can be obtained [14]. However, in some cases, installing a tachometer is difficult or impossible.…”

Section: Introductionmentioning

confidence: 99%

Time Synchronous Averaging Based on Cross-power Spectrum

Jiang

2023

Chin. J. Mech. Eng.

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Periodic components are of great significance for fault diagnosis and health monitoring of rotating machinery. Time synchronous averaging is an effective and convenient technique for extracting those components. However, the performance of time synchronous averaging is seriously limited when the separate segments are poorly synchronized. This paper proposes a new averaging method capable of extracting periodic components without external reference and an accurate period to solve this problem. With this approach, phase detection and compensation eliminate all segments' phase differences, which enables the segments to be well synchronized. The effectiveness of the proposed method is validated by numerical and experimental signals.

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Impact of pulse time uncertainty on synchronous average: Statistical analysis and relevance to rotating machinery diagnosis

Cited by 12 publications

References 42 publications

An unsupervised intelligent fault diagnosis research for rotating machinery based on NND-SAM method

An unsupervised intelligent fault diagnosis research for rotating machinery based on NND-SAM method

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

Time Synchronous Averaging Based on Cross-power Spectrum

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