Detection Fault Symptoms of Rolling Bearing Based on Enhancing Collected Transient Vibration Signals

Du, Nguyen Trong; Dien, Nguyen Phong; Cuong, Nguyen Huu

doi:10.1007/978-981-19-1968-8_30

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…2 Principle of the proposed method 2.1 Clarifying varying rotation speed effect 2.1.1 Noise filter Acquired signals often contain background noise from environments. TQWT has effectively denoised bearing vibration signals (Du et al, 2022). Figure 1 shows the TQWT flowchart.…”

Section: Introductionmentioning

confidence: 99%

Automatic rolling bearings fault classification: a case study at varying speed conditions

Du,

Trung,

Cuong

et al. 2024

Front. Mech. Eng.

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Rolling bearings always operate under variable speed conditions, which poses a challenge for researchers in identifying and classifying bearing faults. In contrast to the stationary speed condition, the Fault Characteristic Frequency (FCF) under variable speed conditions exhibits a variable value that depends on the instantaneous shaft rotational speed (ISRS). The representation of the FCFs in the frequency domain reveals overlapping patterns among them. To solve the mentioned problem, a novel tool is proposed and established by mixing the two methods: The Fourier-based SynchroSqueezing transform (FSST) and Principal Component Analysis (PCA). By illustrating the envelope signal in time-frequency distribution using FSST, the FCF is highlighted in each ISRS value. Finally, this time-frequency distribution is used as input of PCA to classify rolling bearings. This method successfully diagnosed both inner race fault and outer race fault of rolling bearings.

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Section: Introductionmentioning

confidence: 99%

Automatic rolling bearings fault classification: a case study at varying speed conditions

Du,

Trung,

Cuong

et al. 2024

Front. Mech. Eng.

Self Cite

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“…Pre-processing: the measured signals are denoised using Tunable Q-factor Wavelet Transform (TQWT)[26], yielding pure signals. These pure signals are then split into training, validation, and testing sets.…”

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confidence: 99%

A distinguished deep learning method for gear fault classification using time–frequency representation

Nguyen,

Pham

et al. 2024

Discov Appl Sci

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Fault diagnosis of gearboxes has attracted increasing interest in recent decades due to their ubiquity and importance in the industry. Modern research trends focus on developing a diagnosis system that works automatically with the application of artificial intelligence. These previous studies have used the Deep Learning (DL) network without adequately addressing noise of the input data, requiring more data to achieve effective training. Thus, this work proposes a novel Transfer Learning method using the time–frequency representation of gear vibration signals, which enables more accurate classification in complex working conditions and reduces necessary input data to train. Using fine-tuning techniques proposed in this paper requires only a limited data set while ensuring acceptable classification results. An experiment test rig within different gear faults and load conditions was set up to evaluate the algorithm’s effectiveness.

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Detection Fault Symptoms of Rolling Bearing Based on Enhancing Collected Transient Vibration Signals

Cited by 2 publications

References 6 publications

Automatic rolling bearings fault classification: a case study at varying speed conditions

Automatic rolling bearings fault classification: a case study at varying speed conditions

A distinguished deep learning method for gear fault classification using time–frequency representation

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