{"title":"Research on pipeline flange leakage detection method based on random forest and Pearson correlation coefficient","authors":"Lihua Huang , Bin Hu , Shuting Wan , Bai Lu","doi":"10.1016/j.apacoust.2025.110918","DOIUrl":null,"url":null,"abstract":"<div><div>Pipeline flange leakage is influenced by multiple factors, including discharge area, temperature, pressure, flow velocity, and installation position. Conventional leakage prediction models suffer from computational inefficiency and reduced generalizability due to high-dimensional feature spaces and redundant parameters. To overcome these limitations, this study introduces an interpretable hybrid framework integrating Random Forest (RF)-driven feature selection with Pearson correlation analysis for high-precision leakage diagnostics. Vibration signals were systematically acquired from paired upper and lower flanges at leakage-critical regions, followed by extraction of multicomponent features including total vibration energy, spectral kurtosis, centroid frequency, Shannon entropy, and bidirectional cross-correlation metrics. Through rigorous RF-based recursive feature elimination, four dominant sensitivity indicators were identified: inter-flange cross-correlation (CC), upper-flange centroid frequency (CF), total vibration energy (TVE) of both flanges, and downstream entropy (DE). Subsequent Pearson correlation mapping revealed minimal collinearity (|<em>r</em>| < 0.4) between CC, CF, and lower-flange TVE, establishing these orthogonal features as prediction model inputs. A Multilayer Perceptron (MLP) network is trained to establish the leakage prediction model. Experimental results demonstrate high accuracy of the proposed method, achieving a mean squared error (<em>MSE</em>) of approximately 6.8 and a coefficient of determination (<em>R<sup>2</sup></em>) of 0.97. The feature dimensionality reduction not only improves prediction accuracy but also reduces iteration cycles.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"240 ","pages":"Article 110918"},"PeriodicalIF":3.4000,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Acoustics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003682X25003901","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Pipeline flange leakage is influenced by multiple factors, including discharge area, temperature, pressure, flow velocity, and installation position. Conventional leakage prediction models suffer from computational inefficiency and reduced generalizability due to high-dimensional feature spaces and redundant parameters. To overcome these limitations, this study introduces an interpretable hybrid framework integrating Random Forest (RF)-driven feature selection with Pearson correlation analysis for high-precision leakage diagnostics. Vibration signals were systematically acquired from paired upper and lower flanges at leakage-critical regions, followed by extraction of multicomponent features including total vibration energy, spectral kurtosis, centroid frequency, Shannon entropy, and bidirectional cross-correlation metrics. Through rigorous RF-based recursive feature elimination, four dominant sensitivity indicators were identified: inter-flange cross-correlation (CC), upper-flange centroid frequency (CF), total vibration energy (TVE) of both flanges, and downstream entropy (DE). Subsequent Pearson correlation mapping revealed minimal collinearity (|r| < 0.4) between CC, CF, and lower-flange TVE, establishing these orthogonal features as prediction model inputs. A Multilayer Perceptron (MLP) network is trained to establish the leakage prediction model. Experimental results demonstrate high accuracy of the proposed method, achieving a mean squared error (MSE) of approximately 6.8 and a coefficient of determination (R2) of 0.97. The feature dimensionality reduction not only improves prediction accuracy but also reduces iteration cycles.
期刊介绍:
Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense.
Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems.
Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.