Advanced Signal Decomposition Methods for Vibration Diagnosis of Rotating Machines: A Case Study at Variable Speed

Du, Nguyen Trong; Dien, Nguyen Phong

doi:10.1007/978-981-16-3239-6_30

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…A faulty gearbox component might potentially halt the functioning of the entire production line, resulting in significant financial losses [5][6][7][8][9][10]. The mechanism's vibrations reflect the technical condition of the gearbox, and any faults caused by the operation can be detected early and accurately using signal analysis tools [11]. Due to the above reasons, the gear clusters and shaft bearings in the gearbox are the most extensively studied objects in technical diagnostics using vibration analysis, with many publications in international scientific journals.…”

Section: Introductionmentioning

confidence: 99%

Improvements in the Wavelet Transform and Its Variations: Concepts and Applications in Diagnosing Gearbox in Non-Stationary Conditions

Nguyen,

Nguyen

2024

Applied Sciences

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Wavelet transform is a powerful time-frequency-based analysis method often used in gear fault diagnostics. The development of wavelet transform is closely linked to the improvement of resolution. When the high-frequency resolution allows for easy observation of different frequency components, it is a symptom of damage to an individual part of the machine. This study effectively applied the Wavelet analysis technique to diagnose faulty gearboxes operated in non-stationary conditions. This is a complex problem that usual diagnostic approaches need help to solve due to its non-linear character. This work conducted a simulation and real-world testing to show that the newest wavelet analysis techniques work well, showing that they can accurately find gear faults in gearboxes in non-stationary conditions. A thorough overview of the cutting-edge applications of wavelet transform in diagnosing faults in industrial gearbox systems was also given. This work also explained in detail the mathematical ideas behind the continuous wavelet transform, discrete wavelet transforms, and wavelet packet transform.

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Section: Introductionmentioning

confidence: 99%

Improvements in the Wavelet Transform and Its Variations: Concepts and Applications in Diagnosing Gearbox in Non-Stationary Conditions

Nguyen,

Nguyen

2024

Applied Sciences

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“…It is typically employed as a filtering approach in the time domain. It is a highly effective and accurate signal processing technique to isolate periodic signals from a complex signal applied to rotating machinery [9][10][11][12][13][14]. McFadden [15] presented a method for identifying cracks in the geared system by employing TSA in conjunction with phase demodulation.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, many scientists in the vibration signal analysis field are concentrating on various techniques to estimate the instantaneous frequency of shafts [18][19][20][21]. In order to determine the rotational frequency of the shaft, it is necessary to analyze the signal in the time-frequency domain using windowing transforms such as Short-time Fourier Transforms (STFTs) [14,22] and wavelet transforms [23][24][25]. By examining the time-frequency representation (TFR) to identify gear meshing frequency (GMF) components, after that, dividing these components by the number of teeth will give the shaft rotational frequency.…”

Section: Introductionmentioning

confidence: 99%

Tacholess Time Synchronous Averaging for Gear Fault Diagnosis in Wind Turbine Gearboxes Using a Single Accelerometer

Nguyen,

Nguyen

et al. 2024

Machines

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Wind power is increasingly seen as a global, sustainable, and eco-friendly energy option. However, one significant obstacle to further wind energy investment is the high failure rate of wind turbines. The gearbox plays a pivotal role in turbine performance. In recent years, there has been a surge in the focus on gearbox fault diagnosis, reflecting its criticality and prevalence in the industry. Time synchronous averaging (TSA) is a primary technique to identify faults in wind turbine gearboxes using mechanical vibration signals. Generally, implementing TSA requires a device that is capable of recording the phase information of a rotary shaft. Nevertheless, there are situations in which the installation of such a device poses difficulties. For instance, gearboxes that are in use cannot be halted to allow for the installation of a device, and sealed gearboxes provide challenges while being inserted into the device. This research presents an innovative technical way to improve the TSA method without requiring a phase signal. The proposed method has the advantage of extracting the shaft rotation angle signal from the measured acceleration signal, even in non-stationary conditions where the rotational speed varies over time. The effectiveness of the proposed method is validated through measured datasets from wind turbine gearboxes with actual faults and a dataset from a gear system with variable rotational speeds.

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Advanced Signal Decomposition Methods for Vibration Diagnosis of Rotating Machines: A Case Study at Variable Speed

Cited by 2 publications

References 10 publications

Improvements in the Wavelet Transform and Its Variations: Concepts and Applications in Diagnosing Gearbox in Non-Stationary Conditions

Improvements in the Wavelet Transform and Its Variations: Concepts and Applications in Diagnosing Gearbox in Non-Stationary Conditions

Tacholess Time Synchronous Averaging for Gear Fault Diagnosis in Wind Turbine Gearboxes Using a Single Accelerometer

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