A new on-line ultrasonic thickness monitoring system for high temperature pipes

Song, Jiahe; Guo, Donghui; Jia, Jiuhong; Tu, S.T.

doi:10.1016/j.ijpvp.2022.104691

Cited by 6 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where t is the population algebra, P (1) is the initial population. Each parameter in ( 6) is defined in (1), and obtained by taking a random value in the respective feasible domain.…”

Section: The Genetic Algorithm and The Levenberg-marquardt Algorithm ...mentioning

confidence: 99%

“…Step 1: Set the population size M, population algebra gen = 1, and randomly generate the initial population P (1) = {α(1), β(1), τ(1), f c (1), ϕ(1)}.…”

Section: The Genetic Algorithm and The Levenberg-marquardt Algorithm ...mentioning

confidence: 99%

“…Online monitoring of high-temperature pressure vessels and piping using ultrasonic testing is crucial for ensuring their safe operation. The online monitoring technology using waveguide transducer provides a new approach to solve the problem of long-term online monitoring of equipment in high-temperature environments [1]. However, the signal received from test pieces working in noisy environments often contains interference noise, resulting in a low signal-to-noise ratio (SNR) of the useful ultrasonic signal and limiting the computational accuracy of time-of-flight (TOF) [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An integration method of a hybrid genetic algorithm and the Levenberg–Marquardt algorithm for ultrasonic testing

Li,

Jia,

Yang

et al. 2024

Metrology and Measurement Systems

View full text Add to dashboard Cite

The accurate measurement of time-of-flight (TOF) is essential in ultrasonic testing. Further, noise interference is the key factor affecting the measurement accuracy. Therefore, to develop a reliable computational method of TOF for test pieces working in noisy environments, an integration method of a hybrid genetic algorithm and the Levenberg–Marquardt algorithm (GA–LM) for ultrasonic thickness measurement is proposed in the present research. A Gaussian model is first established for an echo signal. Further, the model-based parameter estimation is converted into a nonlinear optimization problem by applying the least square method. As the parameter estimation methods are easily affected by the initial value, an integrating innovation of the GA–LM algorithm is proposed. The initial values of the model parameters are selected by GA to obtain an approximate global optimal solution. Subsequently, this approximate solution is used as the initial value for the LM algorithm to perform iterations. The accurate global optimal solution of the Gaussian model is obtained through these iterations. Finally, the measuring accuracy and robustness of the GA–LM algorithm for TOF computation are verified by both numerical simulation and experiment data

show abstract

“…where t is the population algebra, P (1) is the initial population. Each parameter in ( 6) is defined in (1), and obtained by taking a random value in the respective feasible domain.…”

Section: The Genetic Algorithm and The Levenberg-marquardt Algorithm ...mentioning

confidence: 99%

“…Step 1: Set the population size M, population algebra gen = 1, and randomly generate the initial population P (1) = {α(1), β(1), τ(1), f c (1), ϕ(1)}.…”

Section: The Genetic Algorithm and The Levenberg-marquardt Algorithm ...mentioning

confidence: 99%