Research on the Cooperative Detection of Stochastic Resonance and Chaos for Weak SNR Signals in Measurement While Drilling

Yang, Yi; Li, Fēi; Zhang, Nan; Huo, Aiqing

doi:10.3390/s21093011

Cited by 10 publications

(8 citation statements)

References 28 publications

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“…In the detection study for rock interfaces, it is commonly assumed that the uniaxial compressive strength (UCS) of the rock and the drilling parameters remain stable during non-interface stages, while the UCS exhibits changes at the interface [14]. Consequently, the drilling state of any interface position during the drilling process can be expressed as Equation (1).…”

Section: Modelmentioning

confidence: 99%

Automated Stratum Interface Detection Using the Optimized Drilling Specific Energy through Self-Adaptive Logistic Function

Liu,

Cheng,

Sun

et al. 2023

Sensors

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The precise detection of stratum interfaces holds significant importance in geological discontinuity recognition and roadway support optimization. In this study, the model for locating rock interfaces through change point detection was proposed, and a drilling test on composite strength mortar specimens was conducted. With the logistic function and the particle swarm optimization algorithm, the drilling specific energy was modulated to detect the stratum interface. The results indicate that the drilling specific energy after the modulation of the logistic function showed a good anti-interference quality under stable drilling and sensitivity under interface drilling, and its average recognition error was 2.83 mm, which was lower than the error of 6.56 mm before modulation. The particle swarm optimization algorithm facilitated the adaptive matching of drive parameters to drilling data features, yielding a substantial 50.88% decrease in the recognition error rate. This study contributes to enhancing the perception accuracy of stratum interfaces and eliminating the potential danger of roof collapse.

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Section: Modelmentioning

confidence: 99%

Automated Stratum Interface Detection Using the Optimized Drilling Specific Energy through Self-Adaptive Logistic Function

Liu,

Cheng,

Sun

et al. 2023

Sensors

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“…where f (x) is the objective function or system energy, T is temperature, and k is the Boltzmann constant. More detail about the SA optimization procedure can be found in the literature [47][48][49]. Algorithm 1 demonstrates the SA optimization process that was implemented to reconstruct the Duffing differential equation from the measured dataset.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Modeling of Magnetoelectric Microresonator Using Numerical Method and Simulated Annealing Algorithm

Sadeghi,

Bazrafkan,

Rutner

et al. 2023

Micromachines

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A comprehensive understanding of the linear/nonlinear dynamic behavior of wireless microresonators is essential for micro-electromechanical systems (MEMS) design optimization. This study investigates the dynamic behaviour of a magnetoelectric (ME) microresonator, using a finite element method (FEM) and machine learning algorithm. First, the linear/nonlinear behaviour of a fabricated thin-film ME microactuator is assessed in both the time domain and frequency spectrum. Next, a data driven system identification (DDSI) procedure and simulated annealing (SA) method are implemented to reconstruct differential equations from measured datasets. The Duffing equation is employed to replicate the dynamic behavior of the ME microactuator. The Duffing coefficients such as mass, stiffness, damping, force amplitude, and excitation frequency are considered as input parameters. Meanwhile, the microactuator displacement is taken as the output parameter, which is measured experimentally via a laser Doppler vibrometer (LDV) device. To determine the optimal range and step size for input parameters, the sensitivity analysis is conducted using Latin hypercube sampling (LHS). The peak index matching (PIM) and correlation coefficient (CC) are considered assessment criteria for the objective function. The data-driven developed models are subsequently employed to reconstruct/predict mode shapes and the vibration amplitude over the time domain. The effect of driving signal nonlinearity and total harmonic distortion (THD) is explored experimentally under resonance and sub-resonance conditions. The vibration measurements reveal that as excitation levels increase, hysteresis variations become more noticeable, which may result in a higher prediction error in the Duffing array model. The verification test indicates that the first bending mode reconstructs reasonably with a prediction accuracy of about 92 percent. This proof-of-concept study demonstrates that the simulated annealing approach is a promising tool for modeling the dynamic behavior of MEMS systems, making it a strong candidate for real-world applications.

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“…By solving Equation (12), the variation range of term θ-β is from −60.7 • to 60.7 • . The simulation results also prove that when β = 0 and the other conditions remain unchanged, the variation range of θ is from −60 • to 60 • .…”

Section: All-phase Frequency Detection Based On Array Duffing Chaos S...mentioning

confidence: 99%

“…Under such conditions, it can retain useful signals and filter out irrelevant noise components during the filtering process [10]. However, in the actual collected MWD signals, the frequency distribution of noise signals is very complex due to the variety of interference sources, and there is bound to be a part close to the frequency of the characteristic signal [11,12]. Therefore, while suppressing noise, the characteristic signal will inevitably be suppressed or damaged, and even lead to invalid MWD.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Effect-Based Array Duffing Systems with Improved Nonlinear Restoring Force for Weak Signal Detection in Dynamic MWD

Yang,

Ding,

Gao

et al. 2023

Sensors

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In the process of dynamic Measurement While Drilling (MWD), the strong vibration and rapid rotation of the Bottom Hole Assembly (BHA) lead to multi-frequency and high-amplitude noise interference in the attitude measurement signal. The weak original signal and extremely low signal-to-noise ratio (SNR) are always the technical difficulties of dynamic MWD. To solve this problem, this paper uses the chaotic effect of the Duffing system, which takes the expression (−x3 + x5) as a nonlinear restoring force to detect the weak characteristic signal of downhole dynamic MWD. First of all, in order to meet the limit condition of the chaotic phase transition of the system output, the frequency value of the characteristic signal is reconstructed and transformed based on the variable scale theory. Then, in order to solve the influence of the initial phase of the characteristic signal on the detection accuracy, a detection model based on the array Duffing system is presented, and a frequency-detection scheme with all-phase coverage is given. Finally, another array Duffing system is designed for the parameter estimation of the characteristic signal. The critical value of chaotic phase transition is determined by adjusting the amplitude of the driving signal of the array Duffing system, and then the amplitude and phase parameters of the characteristic signal are synchronously estimated. The experimental results show that the proposed method can effectively extract the weak characteristic signal within the strong noise, and the SNR of the characteristic signal can be as low as −21 dB. Through the attitude calculation for the extracted characteristic signal, it can be seen that the proposed method can improve the accuracy of the inclination of the drilling tool significantly, which proves the feasibility and effectiveness of the method proposed in this paper.

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Research on the Cooperative Detection of Stochastic Resonance and Chaos for Weak SNR Signals in Measurement While Drilling

Cited by 10 publications

References 28 publications

Automated Stratum Interface Detection Using the Optimized Drilling Specific Energy through Self-Adaptive Logistic Function

Automated Stratum Interface Detection Using the Optimized Drilling Specific Energy through Self-Adaptive Logistic Function

Modeling of Magnetoelectric Microresonator Using Numerical Method and Simulated Annealing Algorithm

Chaotic Effect-Based Array Duffing Systems with Improved Nonlinear Restoring Force for Weak Signal Detection in Dynamic MWD

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