A method of in-situ monitoring multiple parameters and blade condition of turbomachinery by using a single acoustic pressure sensor

Li, Ning; Chen, Yugang; Zhai, Jingyu; Han, Qingkai; Hou, Zengxuan

doi:10.1016/j.ymssp.2022.109051

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Earlier methods of condition monitoring relied on data sources developed with the aid of sensors. The sensors utilized are accelerometers [8], pressure transducers [9], flow meters [10], and displacement sensors [11] to name a few. The sensors, however, are expensive, prone to failure, difficult to incorporate into existing system design, and lead to overfitting of condition monitoring model [12].…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Study the Effect of Complexity on an External Gear Pump Model to Generate Data Source for AI-Based Condition Monitoring Application

Azeez,

Mazzei,

Minav

et al. 2023

Actuators

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The external gear pump, like any other hydraulic component, is vulnerable to failure, which may lead to downtime as well as the failure of other components linked to it, thereby causing production loss. Therefore, establishing a condition monitoring system is crucial in identifying failure at an early stage. Traditional condition monitoring approaches rely on experimental data that are collected by means of sensors. However, the sensors utilized in the experiments may have calibration issues, which lead to inaccurate measurements. The availability of experimental data is also limited as it is difficult and expensive to create and detect a fault in a component. Hence, it is essential to develop a simulation model that mimics the performance of the actual system. The data generated from the model can be utilized to create the data source required for automated condition monitoring. A new methodology based on a detailed geometric model for simulating the External Gear Pump is described and compared to two models analyzed in the authors’ previous work, namely Schlosser’s loss model and simple geometric model. In this paper, the three models are compared with experimental data and the method utilized for fault injection. Schlosser’s loss model, as well as the detailed geometric model, are found to be suitable in terms of validation; however, the latter is a better candidate in terms of fault injection. Hence, the detailed geometric model can be implemented as a tool to generate the data source for condition monitoring applications.

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Section: Introductionmentioning

confidence: 99%

A New Approach to Study the Effect of Complexity on an External Gear Pump Model to Generate Data Source for AI-Based Condition Monitoring Application

Azeez,

Mazzei,

Minav

et al. 2023

Actuators

View full text Add to dashboard Cite

show abstract

“…Due to its compactness and the complexity of the relative movements between different elements, it is more difficult to install multiple sensors to monitor the working condition of the micro-turbine. As an alternative, using the acoustic pressure signal generated by rotating components for health monitoring [7,8] is a good method. However, the working environment of the turbine system is complex, resulting in the monitored acoustic signal having to contain the signals of multiple components of the equipment and the strong interference of the Machines 2022, 10, 444 2 of 22 surrounding environment in the actual operation, which poses a serious challenge to the detection, feature extraction, and recognition of the acoustic signal generated by the turbine [9].…”

Section: Introductionmentioning

confidence: 99%

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

Chen

Zhai

et al. 2022

Machines

Self Cite

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The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.

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Frequency-domain analysis of two controllable attenuators for control processes and perturbations in a constant temperature chilled-water system

Guo,

Hong,

Chen

et al. 2024

Applied Thermal Engineering

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A method of in-situ monitoring multiple parameters and blade condition of turbomachinery by using a single acoustic pressure sensor

Cited by 5 publications

References 30 publications

A New Approach to Study the Effect of Complexity on an External Gear Pump Model to Generate Data Source for AI-Based Condition Monitoring Application

A New Approach to Study the Effect of Complexity on an External Gear Pump Model to Generate Data Source for AI-Based Condition Monitoring Application

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

Frequency-domain analysis of two controllable attenuators for control processes and perturbations in a constant temperature chilled-water system

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