Adaptive Stochastic Resonance for Bolt Looseness Identification Under Strong Noise Background

Gong, Tao; Yang, Jianhua; Sanjuán, Miguel A. F.; Liu, Houguang

doi:10.1115/1.4053799

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…In addition, the evaluation index of the determination coefficient of the prediction effect reaches 0.9, which is significantly higher than other conventional models, such as multiple linear regression and multiple adaptive regression splines. Gong [21] proposed a method based on subharmonic resonance and adaptive stochastic resonance (ASR) to identify whether the bolt is loose. Through numerical simulation and experimental verification of a typical single bolt connection model, it is proved that this method can effectively identify bolt loosening under a strong noise background.…”

Section: Introductionmentioning

confidence: 99%

Research on the Influence of Disc–Drum Connection Bolt Preloading Rotor Assembly Modal Characteristics and Diagnosis Technology

Wang,

Xue,

Zhang

et al. 2024

Machines

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The drum rotor of an aero-engine is connected by one or multiple mounting edges through bolts, and their dynamics are significantly influenced by the preload state of the bolts. Long working hours in challenging environments can result in the deterioration of bolt pre-tightening during assembly or service, which impacts the rotor’s dynamic stability and overall performance. Currently, there are no available methods for detecting the dynamic characteristics of the drum connection components. This paper analyzes the impact of the natural characteristics of the drum composite structure of a high-pressure aero-engine turbine based on the refined finite element method when the preloading state changes. Two conditions of deviation and uneven stiffness distribution were applied to the connected components of the drum. The analysis focused on the impact of the pre-tightening state on its natural frequency. After analyzing the feasibility of identifying the pre-tightening state, two methods are proposed. These methods focus on changes in natural frequency and mode shape, specifically the sensitive natural frequency change method and the mode step change method. The methods proposed in this paper can serve as a reference for evaluating the quality of assembling complex disc–drum structures with multiple bolt connections.

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Section: Introductionmentioning

confidence: 99%

Research on the Influence of Disc–Drum Connection Bolt Preloading Rotor Assembly Modal Characteristics and Diagnosis Technology

Wang,

Xue,

Zhang

et al. 2024

Machines

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“…The study of nonlinear stochastic resonance system shows that an appropriate amount of noise can amplify weak signals [24,25]. In recent years, the application research of stochastic resonance in fault diagnosis [26], image processing [27,28] and other fields has expanded the engineering value of nonlinear stochastic dynamics. In conclusion, the novelty of this work are as follows, in order to improve the accuracy of weak defect feature extraction under noise background, we propose a monostable stochastic resonance image denoising method to preprocess the noise reduction of weld X-ray flaw detection image.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

Zheng¹,

Luo²,

Shen³

et al. 2023

Energy Engineering

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To solve the problems of low precision of weak feature extraction, heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage (UHV) equipment key parts, an automatic feature extraction algorithm is proposed. Firstly, the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method. Then, binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation. Finally, the automatic identification of weld defect area is realized based on the sequential traversal of binary tree. Several characteristic analysis dimensions are established for weld defects of UHV key parts, including defect area, perimeter, slenderness ratio, duty cycle, etc. The experiment using the weld detection image of the actual production site shows that the proposed method can effectively extract the weak feature information of weld defects and further provide reference for decision-making.

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“…There are strict conditions for the excitation signal, noise, and nonlinear system model to generate SR phenomenon. In order to generate resonance actively, adaptive SR has been proposed and widely studied [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Stochastic resonance impact signal detection method based on a novel single potential well model

Li,

Bai

et al. 2023

Meas. Sci. Technol.

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Our research introduces a novel stochastic resonance (SR) model featuring a single potential well and develops a dedicated detection system designed to address the challenging problem of detecting impact signals within a highly noisy background. We begin by examining the limitations of conventional metrics, such as the cross-correlation coefficient and kurtosis index, in identifying nonperiodic impact signals, and subsequently introduce an improved metric. By harnessing parameter-adjusted SR, this innovative potential well model and metric is integrated to formulate an adaptive detection method for nonperiodic impact signals. This method automatically adjusts system parameters in response to the input signal. Subsequently, numerical simulations of the system is conducted so as to perform a comparative analysis with experimental results obtained from both asymmetric single potential well and periodic potential systems. Our findings conclusively demonstrate the enhanced effectiveness of our proposed method in detecting impact signals within a high-noise environment. Furthermore, the method provides more accurate estimates of both the intensity and precise location of the input impact signal from the output results.

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Adaptive Stochastic Resonance for Bolt Looseness Identification Under Strong Noise Background

Cited by 8 publications

References 36 publications

Research on the Influence of Disc–Drum Connection Bolt Preloading Rotor Assembly Modal Characteristics and Diagnosis Technology

Research on the Influence of Disc–Drum Connection Bolt Preloading Rotor Assembly Modal Characteristics and Diagnosis Technology

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

Stochastic resonance impact signal detection method based on a novel single potential well model

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