Objective assessment of IPM denoising quality of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si161.svg" display="inline" id="d1e1073"><mml:mi>φ</mml:mi></mml:math>-OTDR signal

Chen, Yunfei; Zhu, Peibin; Yin, Yue; Wu, Minfeng; Yu, Kaimin; Feng, Lei; Chen, Wen

doi:10.1016/j.measurement.2023.112775

Cited by 7 publications

(9 citation statements)

References 23 publications

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“…This requires the combination of multi-dimensional parameters inside and outside the monitored battery with technologies such as machine learning and artificial intelligence to achieve a battery level battery management system (BMS) and fault diagnosis. Our recently proposed series of advanced fiber optic signal processing methods [155][156][157][158][159] is capable of eliminating various kinds of noises in practice, such as electrochemical noises, road noises, wind noises, and auxiliary system noises [160,161]. This helps in fiber optic sensing multiparameter decoupling; signal signature identification; and evaluation with battery SOC, SOH, and RUL.…”

Section: Challenges and Outlooksmentioning

confidence: 99%

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

Yu,

Chen,

Deng

et al. 2024

Sensors

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Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 με), pressure (0.14 bar), and refractive index (6 × 10−5 RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/με, −0.3 pm/bar, and −18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development.

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Section: Challenges and Outlooksmentioning

confidence: 99%

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

Yu,

Chen,

Deng

et al. 2024

Sensors

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“…In addition, signal processing methods such as Empirical Mode Decomposition (EMD), Moving Average (MA), Moving Differential (MD), and Frequency Domain Dynamic Averaging (FDDA) can also eliminate the frequency deviation of φ-OTDR signals, improve their signal-to-noise ratio (SNR), and enhance the accuracy of vibration mode recognition [23][24][25]. Although image processing techniques, such as non-local mean (NLM) [26][27][28], Block-Matching and three-dimensional filtering (BM3D) [29,30], and the neural network-based filtering method [31,32], show superior denoising effects compared to two-dimensional WT, two-dimensional WT has higher processing efficiency with good denoising effects [33]. Additionally, the WD method can be combined with other methods to obtain better denoising results, but essentially, the parameters of WD play a decisive role in the denoising results.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, an improved WT called Maximum Overlapping Discrete Wavelet Transform (MODWT) is introduced to process the vibration signals, achieving remarkable sensing lengths, positioning accuracy, and processing times [36]. Notably, recent efforts in our research have enhanced the wavelet threshold and threshold function to improve the SNR of φ-OTDR and electrocardiogram (ECG) signals [33,37,38]. Despite these advancements, limited attention has been given to the optimization of wavelet DL in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Despite these advancements, limited attention has been given to the optimization of wavelet DL in recent years. In previous work, we have attempted to select the DL of WD using a signal-based method [33], namely first cycle lag of the autocorrelation function (FCL-ACF). However, the optimal DL cannot be determined because FCL-ACF increased monotonically with DL.…”

Section: Introductionmentioning

confidence: 99%

“…• Initialize the parameter of WD as "Dmey" wavelet base, soft threshold function, DL = 1. Then, the wavelet coefficient of each DL are obtained using Matlab command "wavedec2", and the threshold of each DL is optimized by FCL-ACF based thresholding method [33].…”

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confidence: 99%

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Wavelet Decomposition Layer Selection for the φ-OTDR Signal

Chen,

Yu,

et al. 2024

Photonics

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The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry (φ-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured φ-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance.

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