Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

Xu, Chao; Huang, Chenchen; Zhu, Weidong

doi:10.1177/1550147719875656

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…In recent years, due to technological improvement of cameras and related algorithms, the approach based on image processing to diagnose bolts has accumulated some favor [58][59][60]. This method is usually combined with deep learning technology [61][62][63], support vector machines [64], and nonlinear decomposition such as empirical mode decomposition [65]. Selecting the reference points to detect changes in the nuts' position has a substantial effect on the reliability of these methods.…”

Section: Diagnostics Of Bolted Joints Loosening In Structuresmentioning

confidence: 99%

Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model

Krot,

Shiri,

Dąbek

et al. 2023

Materials

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The condition-based maintenance of vibrating screens requires new methods of their elements’ diagnostics due to severe disturbances in measured signals from vibrators and falling pieces of material. The bolted joints of the sieving deck, when failed, require a lot of time and workforce for repair. In this research, the authors proposed the model-based diagnostic method based on modal analysis of the 2-DOF system, which accounts for the interaction of the screen body and the upper deck under conditions of bolted joint degradation. It is shown that the second natural mode with an out-of-phase motion of the upper deck against the main screen housing may coincide with the excitation frequency or its higher harmonics, which appear when vibrators’ bearings are in bad condition. This interaction speeds up bolt loosening and joint opening by the dynamical loading of higher amplitude. The proposed approach can be used to detune the system from resonance and anti-resonance to reduce maintenance costs and energy consumption. To prevent abrupt failures, such parameters as second natural mode frequency, damping factor, and phase space plot (PSP) distortion measures are proposed as bolt health indicators, and these are verified on the laboratory vibrating screen. Also, the robustness is tested by the impulsive non-Gaussian noise addition to the measurement data. A special diagram was proposed for the bolted joints’ strength capacity assessment and maintenance actions planning (tightening, replacement), depending on clearance in the joints.

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Section: Diagnostics Of Bolted Joints Loosening In Structuresmentioning

confidence: 99%

Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model

Krot,

Shiri,

Dąbek

et al. 2023

Materials

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“…28,29 Furthermore, time-frequency analysis is investigated to explore the relationship between the changes of damage indices and that of bolt preloads. [30][31][32] Lately, driven by machine learning algorithms, effective classifiers could be trained by sensitive features, 33 or directly tuned by raw vibration signals using deep learning architectures. [34][35][36] Then, autonomous identification of looseness states could be realized with attractive performance.…”

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 recent years, visual image processing methods have gained a popularity in bolts loosening diagnostics [ 40 , 41 , 42 ] using deep learning techniques [ 43 , 44 , 45 ], Hough transforms [ 46 , 47 ], support vector machine (SVM) [ 48 ] density-based spatial clustering of applications with noise (DBSCAN) [ 49 ], empirical mode decomposition–based nonlinear system identification [ 50 ] and convolutional neural networks (CNNs) [ 51 ]. A brief review of bolted joint monitoring is given in [ 52 ].…”

Section: Introductionmentioning

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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Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

Cited by 7 publications

References 37 publications

Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model

Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model

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

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

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