A new acoustic emission damage localization method using synchrosqueezed wavelet transforms picker and time-order method

Wang, Jingkai; Huo, Linsheng; Song, Gangbing

doi:10.1177/1475921720977041

Cited by 14 publications

(8 citation statements)

References 75 publications

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“…After bonding the AE sensor on the frame structure under external vibration caused by the eccentric motor, the author can capture AE waves of five different categories of multi-bolt looseness. Notably, based on the author’s experience and some recommended values in existing investigations, 37 the parameters of the AE system are selected as follows: sampling rate is 1 MHz; the threshold is 40 dB, which can effectively eliminate the influence of ambient noise and reflection effect; the gain of pre-amplifier is 40 dB; the frequency range of the analog filter is from 1 kHz to 1 MHz. Finally, in the next section, the captured AE waves under different categories and data pre-processing will be discussed in more detail.…”

Section: Methodsmentioning

confidence: 99%

Multi-Bolt looseness detection using a new acoustic emission strategy

Wang

2022

Structural Health Monitoring

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In mechanical and aerospace engineering, different components are usually integrated together via bolted connections. Compared to the rivet joint and welding joint, the bolted connection is preferred in some cases due to its easy-to-operation and low-cost. However, the bolt self-loosening caused by vibration or other issues (e.g., improper installation and chemical corrosion) may induce severe accidents. Therefore, in this paper, the author proposes a new strategy based on the acoustic emission (AE) technique to detect bolt looseness. To the best of the author’s knowledge, this research is the first attempt to identify multi-bolt looseness via the AE-based method. Particularly, the main contribution is that the author proposes a new shapelet-enhanced AE method that employs a newly developed dual-shapelet networks classifier to discriminate AE waves. The dual-shapelet networks classifier consists of sample-specific shapelets, which is sensitive to the difference among various categories, and category-specific shapelets derived from auxiliary binary classifiers. The objective of category-specific shapelets is to address the imbalanced classification task, that is, discriminating minority categories. Then, the sample-specific shapelets and category-specific shapelets are combined to extract features from AE signals under different multi-bolt looseness cases, and the final classification is achieved by feeding the extracted features into a softmax layer. Finally, the author conducts an experiment to verify the effectiveness of the proposed method. Moreover, by comparing the proposed method’s performance with two baselines, the advantages of the shapelet-enhanced AE method can be demonstrated. Overall, this research demonstrates that the AE technique is valid to characterize friction and collision between asperities on the bolted interface, thus providing a new direction for multi-bolt looseness detection, and the proposed shapelet-enhanced AE method has substantial potential in the field of structural health monitoring (SHM).

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Section: Methodsmentioning

confidence: 99%

Multi-Bolt looseness detection using a new acoustic emission strategy

Wang

2022

Structural Health Monitoring

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“…As a classic method in dealing with nonlinear stationary signals, wavelet transform (WT) extract inherent signal characteristics in both the time and frequency domains through time-frequency analysis [33]. WT generally includes discrete wavelet transform and CWT, and the latter represents global characteristics of signals more intuitively in all scale and shift operations than the former one.…”

Section: Cwtmentioning

confidence: 99%

An efficient robotic-assisted bolt-ball joint looseness monitoring approach using CBAM-enhanced lightweight ResNet

Li,

Yuan,

et al. 2023

Smart Mater. Struct.

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Bolt-ball joints are widely used in space structures, and their looseness may lead to major safety accidents. The current bolt monitoring methods based on deep learning usually have high computational complexity, and it is difficult to guarantee its computational efficiency under practical scenario. To mitigate this problem, here in this paper, an efficient robotic-assisted bolt-ball joint looseness monitoring approach using convolutional block attention module (CBAM)-enhanced Lightweight ResNet is proposed. Firstly, the robotic-assisted tapping method is applied to bolt-ball joints to generate audio signals, which are constructed into time-frequency maps by continuous wavelet transform (CWT). Secondly, the original ResNet is improved as a lightweight network, which successfully reduces model complexity, and employs time-frequency maps as input. Then, CBAM is introduced to capture global information and focus on the critical feature. Thus, the efficiency of feature extraction is significantly improved. Finally, by the overall optimized structure, a CBAM-enhanced Lightweight ResNet model is established to monitor the bolt-ball joints looseness state accurately. Experimental results demonstrate the high efficiency while maintaining very lightweight structure of the proposed approach, verifying the effectiveness and superiority of the robot-assisted approach using CBAM-enhanced lightweight ResNet over other methods.

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“…The results of TFD are widely used in the monitoring of mechanical system faults or abnormality detection. [21][22][23] By identifying the characteristics of TFD, the running state of the machine can be evaluated. 24,25 The numerical control machine tool is one of the indispensable pieces of equipment in modern machining.…”

Section: Introductionmentioning

confidence: 99%

Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear

Guo,

Tu,

Abbas

et al. 2023

Structural Health Monitoring

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Mechanical system condition monitoring is an important procedure in modern industry, which not only reduces maintenance costs but also ensures safe equipment operation. At present, the monitoring method based on signal processing is one of the most common and effective fault diagnosis methods. In this work, the time-frequency distribution (TFD) obtained by generalized horizontal synchrosqueezing transform is used to extract the impulse feature of the non-stationary vibration signal of the tool. By using the TFD result, the two-dimensional (2D) Fourier transform can further detect the periodic pulses. Next, the energy proportion factor of periodic frequency point is proposed to evaluate the different tool wear degrees. Numerical simulations and experimental data analysis demonstrate the effectiveness of the proposed method as well as the potential for condition monitoring.

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A new acoustic emission damage localization method using synchrosqueezed wavelet transforms picker and time-order method

Cited by 14 publications

References 75 publications

Multi-Bolt looseness detection using a new acoustic emission strategy

Multi-Bolt looseness detection using a new acoustic emission strategy

An efficient robotic-assisted bolt-ball joint looseness monitoring approach using CBAM-enhanced lightweight ResNet

Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear

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