Looseness localization for bolted joints using Bayesian operational modal analysis and modal strain energy

Duan, Ke; Guo, Wei-Gong; Jiang, Cheng; Zhou, Yunlai; Zhu, Weidong

doi:10.1177/1687814018808698

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Strain measurements are carried out using fibre Bragg-grating (FBG) sensors [54,55] and optimal tightening sequence is determined in multi-bolt connections [56]. In other studies [57][58][59][60], theoretical and experimental studies have been conducted for an assessment of modal properties and frequency response functions applicability for detection of structural bolted joint degradation [61]. Some methods of radial clearance diagnostics by vibration signals are given in [62][63][64][65][66][67][68][69][70][71][72][73].…”

Section: Diagnostics Of Bearing Supports and Bolts Looseningmentioning

confidence: 99%

Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes

Krot

Korennoi

Zimroz

2020

Sensors

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The problem solved in this research is the diagnosis of the radial clearances in bearing supports and the loosening of fastening bolts due to their plastic elongation (creep) or weak tightening using vibration signals. This is an important issue for the maintenance of the heavy-duty gearboxes of powerful mining machines and rolling mills working in non-stationary regimes. Based on a comprehensive overview of bolted joint diagnostic methods, a solution to this problem based on a developed nonlinear dynamical model of bearing supports is proposed. Diagnostic rules are developed by comparing the changes of natural frequency and its harmonics, the amplitudes and phases of shaft transient oscillations. Then, the vibration signals are measured on real gearboxes while the torque is increasing in the transmission during several series of industrial trials under changing bearings and bolts conditions. In parallel, dynamical torque is measured and its interrelation with vibration is determined. It is concluded that the radial clearances are the most influencing factors among the failure parameters in heavy-duty gearboxes of industrial machines working under impulsive and step-like loading. The developed diagnostics algorithm allows condition monitoring of bearings and fastening bolts, allowing one to undertake timely maintenance actions to prevent failures.

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Section: Diagnostics Of Bearing Supports and Bolts Looseningmentioning

confidence: 99%

Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes

Krot

Korennoi

Zimroz

2020

Sensors

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“…In vibration-based methods, the vibration response signals excited by percussion are usually analyzed, since the structural properties (e.g., mass, damping, and stiffness) are sensitive to the variation of preload. 23 Based on the time-domain and frequency-domain analysis of collected vibration signals, many dynamic characteristics have been discussed experimentally, for example, natural frequencies, 24,25 transmissibility functions, 26 mode shapes, 27 and their ramifications. 28,29 Furthermore, time-frequency analysis is investigated to explore the relationship between the changes of damage indices and that of bolt preloads.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine

Yang

Zhang

Chen

2023

Structural Health Monitoring

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Bolted joints are the most widely utilized connection types in industries, and therein looseness identification of bolted structures is of great significance to guarantee structural reliability. In this article, a comprehensive study of bolt looseness identification under random excitation is presented. To fulfill this task, this research focuses on three prominent difficulties, including nonstationary signal processing, subtle feature extraction, and robust state classification. First, a novel filter bank structure of quasi-analytic dual-tree complex wavelet packet transform is constructed to analyze the measured vibration response signals, for purpose of capturing subtle feature information. Then, multiple features are extracted from subband signals to capture the variations of dynamic characteristics, and sensitive features are selected by Laplacian score to construct the low-dimensional feature set. Subsequently, a novel classifier with better generalization performance, named large margin distribution machine, is optimized with the wavelet kernel function and the whale optimization algorithm, in order to handle the intrinsic uncertainty related to the looseness states of bolted structures. After feeding the low-dimensional feature set, the proposed classifier is trained to identify looseness states of bolted structures. Finally, experiments of a two-bolt lapped beam under random excitation are conducted to verify the effectiveness of the proposed method, and two typical loading conditions (paired-bolt looseness and single-bolt looseness) are considered. Besides, the superiority of the proposed method is demonstrated by comparing with other analogical methods. This research can provide a promising implement in practical applications of bolt looseness identification under random excitation.

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“…In particular, the convolutional neural network is often used in structural health-monitoring tasks due to its ability to share weights, form local connections, and down-sample (Goodfellow et al, 2016). The Bayesian model and modal strain energy were used to determine the bolt looseness (Hu et al, 2018). The simultaneous use of CNNs and the analysis of guided waves helped to identify the location of the damage in a pressure vessel (Hu et al, 2020), in a steel plate structure (Lim and Sohn, 2020), and in aerospace structural material .…”

Section: Introductionmentioning

confidence: 99%

Quantitative Monitoring of Bolt Looseness Using Multichannel Piezoelectric Active Sensing and CBAM-Based Convolutional Neural Network

et al. 2021

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The bolted connection is widely utilized in engineering to practically and rigidly couple structural components. The integrity of the connection is paramount to the safety of the structure and has prompted the development of many monitoring methods, including the piezoelectricity-based active sensing method. However, the active sensing method cannot quantify bolt looseness due to the unclear relationship between bolt looseness and the single monitoring index typically used in the active sensing method. Thus, the authors propose the unique combination of a one-dimensional convolutional neural network (1DCNN) and multichannel active sensing for quantitative monitoring of bolted connections. In an experiment, piezoelectric ceramic transducer (PZT) patches are bonded on steel plates connected by a bolt. Each patch is wired to a multichannel active sensing monitoring system. After obtaining multichannel stress wave signals at different looseness levels, a looseness vector is calculated to generate training and validation datasets. A baseline 1DCNN model and a novel model improved using the convolutional block attention module (CBAM) are used to monitor the bolt looseness. Finally, the authors verify that the multichannel active sensing method combined with the 1DCNN model can accurately perform quantitative monitoring of bolt looseness, and the monitoring accuracy of the baseline 1DCNN model is above 91.07% in three different specimens. Compared with the baseline 1DCNN model, the monitoring accuracy of the CBAMCNN model improved by approximately 5%. Overall, the method proposed in this article offers a new and highly accurate approach for quantitative monitoring of bolted connections.

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Looseness localization for bolted joints using Bayesian operational modal analysis and modal strain energy

Cited by 8 publications

References 20 publications

Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes

Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes

Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine

Quantitative Monitoring of Bolt Looseness Using Multichannel Piezoelectric Active Sensing and CBAM-Based Convolutional Neural Network

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