A Method for Fault Detection in Wireless Sensor Network Based on Pearson’s Correlation Coefficient and Support Vector Machine Classification

Biswas, Priyajit; Samanta, Tuhina

doi:10.1007/s11277-021-09257-7

Cited by 17 publications

(5 citation statements)

References 16 publications

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“…The Pearson correlation coefficient was calculated to assess the correlation between the influencing factors and the EWCC. This can be calculated using Equation (2) [22]:…”

Section: Pearson Correlation Analysismentioning

confidence: 99%

Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China

Ke,

Zhang,

Sikai

et al. 2024

Water

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Forecasting the electricity-to-water conversion coefficient (EWCC) can help manage and plan irrigation water in arid and semiarid areas. However, the EWCC is influenced by several factors, making it difficult to develop an analytical model for validation or prediction. Therefore, this study selected 206 typical irrigation wells in Baicheng City to conduct EWCC tests in a field investigation to gather information regarding the results and related influencing factors. Subsequently, machine learning models (multiple linear regression model, support vector model, and backpropagation neural network) were trained, validated, and tested, and their precisions were evaluated and compared. The backpropagation neural network model was the most accurate, followed by the support vector and multiple linear regression models. The backpropagation neural network model results were consistent with those of the field survey, and this model was thus used to forecast the EWCC for all the townships in Baicheng City. The forecasting models revealed that most towns had an EWCC from 3 to 7 m3/kW·h, with an EWCC greater than 7 observed in the Tao’er River Fan and Yueliangpao District. The BP models developed in this study proved to be dependable and applicable for forecasting the EWCC in this area.

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“…The Pearson correlation coefficient was calculated to assess the correlation between the influencing factors and the EWCC. This can be calculated using Equation (2) [22]:…”

Section: Pearson Correlation Analysismentioning

confidence: 99%

Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China

Ke,

Zhang,

Sikai

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

“…The Pearson correlation coefficient is a type of correlation analysis that is generally used to determine the strength of correlation between two groups of data (Biswas and Samanta, 2022). If Data A increases and Data B also increases, Data A and Data B show an increasing proportional relationship, which can be expressed by the Pearson correlation coefficient.…”

Section: Length Of Arrangement Spacementioning

confidence: 99%

Configuration Design of a Cruise Ship Lifesaving System Based on a Genetic Algorithm

Zhang

Cai

2023

Journal of Coastal Research

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To improve the configuration design of cruise ship lifesaving systems, this study proposes a system configuration design method based on a genetic algorithm. The configuration design of the lifesaving system of a cruise ship is a combinatorial optimisation problem, with various performance constraints. This study analyses the rules of the configuration design through regression analysis and establishes a mathematical model on this basis. Subsequently, aiming at the discrete problem of the selection and configuration of lifesaving equipment on cruise ships, combined with the characteristics of genetic programming, the lifesaving system required by cruise ships is analysed, classified, and described as a coding structure form of genetic programming. From the perspective of safety, a genetic programming solution strategy for equipment configuration is proposed. The results show that the system configuration design method of cruise ship lifesaving equipment proposed in this study is effective and practical, and a reasonable configuration design scheme can be obtained accurately and efficiently.

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“…Most traditional rotating machinery fault diagnosis methods eliminate the noise factor through various filtering methods, and then manually select specific fault indicators to realize the mining of fault feature information (time domain, timefrequency domain, etc.). Various techniques, such as support vector machine [15][16][17], k-nearest neighbor [5,18,19], and shallow neural network, have been used for tackling the issues.…”

Section: Introductionmentioning

confidence: 99%

An effective method for fault diagnosis of rotating machinery under noisy environment

Xu,

2023

Meas. Sci. Technol.

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Rotating machinery is widely utilized as mechanical equipment in the industrial field. However, due to the complex working conditions, the existing fault diagnosis methods have failed to address good results in practical applications. To improve the fault diagnosis performance of rotating machinery in a noisy environment, a new multi-scale convolution neural network (MSCN) based on a self-calibrating attention module is proposed. First, this thesis constructs a multi-scale convolution layer with a wide convolution kernel to form an efficient sampling structure at the filter level, which can filter out incoherent noise from the signals and extract rich features. Second, a multi-scale self-calibrating attention module (MS-SCAM) is implemented with two identical self-calibrating convolutional networks (SCN) to continuously focus on significant embeddings and adaptively combine information from different spatial dimensions. Third, the multi-dimensional characteristics are integrated by the feature cascade layer, and then the fault modes are identified though the classifier layer under noise. Finally, based on the Case Western Reserve University (CWRU) datasets and Paderborn University (PU) bearing datasets, the experimental results show that our proposed MSCN can significantly enhance the fault identification ability to rotate machinery in a noisy environment.

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A Method for Fault Detection in Wireless Sensor Network Based on Pearson’s Correlation Coefficient and Support Vector Machine Classification

Cited by 17 publications

References 16 publications

Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China

Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China

Configuration Design of a Cruise Ship Lifesaving System Based on a Genetic Algorithm

An effective method for fault diagnosis of rotating machinery under noisy environment

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