A novel diagnosis methodology of gear oil for wind turbine combining stepwise multivariate regression and clustered federated learning framework

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To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) to wind gear oil diagnosis. Initially, a stepwise multivariate regression (SMR) model is introduced and optimized after data process, which integrates a multiscale feature and AIC diagnosis feature. Subsequently, to tackle data heterogeneity among different indicators, canonical correlation series of representations are extracted from the SMR models, and a combining model of CFLF method and SMR is proposed to assess the performance of gear oil. Actual data analysis of wind turbine gear oil showcase the superior performance of the proposed model over single SMR model with higher prediction accuracy of 35.73%. This study provides a new technique for evaluating gear oil in the wind energy sector. Physical sciences/Energy science and technology/Renewable energy Physical sciences/Mathematics and computing/Computer science Stepwise multivariate regression Gear oil evaluation Clustered federated learning framework Wind turbines Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights •The gear oil of wind turbines can be diagnosed and estimated by knowing only the acid value, viscosity, PQ value, Mn, Si, Ca and Zn. •We reached the appropriate model in the eleventh step using stepwise multivariate regression (SMR) analysis with RMSE of 0.0708. •We proposed combining model of SMR and clustered federated learning framework (CFLF) for wind gear oil diagnosis, which has the power to increase 35.73% in accuracy over single SMR model. 1. Introduction Wind energy is a very desirable green choice among many new energy sources because it is renewable and clean energy source with recyclability and high efficiency [ 1 – 3 ]. The main gear lubrication systems play key role on normal operation of wind turbines. However, the large-scale wind power poses a critical impact on the main gear lubrication systems, which renders gear oil becoming key monitor spot in wind turbine system. Addressing this challenge requires reliable analysis method for comprehensive analysis of gear oil and afterward evaluation model, facilitating optimal maintenance time selection for wind turbines [ 4 ]. As a result, main gear oil analysis has become a focal point, particularly with the application of deep learning methods. Searching with the keywords of gear oil / wind turbines, there are 23558 documents from 2014-01 to 2024-11 [ 5 ], and the annual average number of documents issued is 2142. As shown in illustration inside Fig. 1 , 2023 reaches the peak of 3069 annual publications, and 2019 has the fastest growth rate of 12.64%, suggesting that the research in this field has been developed rapidly and is in a fast rising stage. 23558 papers were retrieved, and the top 30 journals in terms of the number of publications are shown in Fig. 1 , in which the journal with the most publications is Energies (795 articles); Journal of Physics: Conference Series ranks second, with 769 articles; Renewable Energy ranks third, with 511 articles. Stepwise multivariate regression (SMR) analysis belongs to one of linear regression methods, which combines multivariate and stepwise modeling technology. Limitations of linear models lies in poor fitting of nonlinear relationships, sensitivity to outliers and insufficient handling of multicollinearity problems [ 6 – 9 ]. Deep learning, as a branch of machine learning, is widely applied in industry due to its remarkable feature learning capabilities and capacity for handling high-dimensional data [ 10 – 13 ]. To address data inhomogeneity characteristic and overcome data sparse, Federated Learning (FL) has been introduced as a collaborative distributed machine learning approach in fault diagnosis [ 14 – 18 ]. Hybrid deep learning model and convolutional neural network (CNN) can also be utilized for spatial feature extraction to capture the temporal dependencies [ 19 , 20 ]. To tackle the challenges in wind turbine fault diagnosis using federated learning, this paper compares a SMR model and clustered federated learning framework (CFLF) for gear oil diagnosis of wind turbines. On this basis, a combining model of SMR and CFLF was proposed, which leverages the multi-scale residual to extract spatial features for gear oil diagnosis, experimental datasets is subjected to these modeling approaches for comparing diagnosis performance of wind gear oil. 2. Data description and process 2.1. Data description Table 1 presents datasets of gear oil for a wind farm in south China. In addition to the general categories of data there are four main categories of data, i.e. chemical data, pollution data, metallic leaching elements and additive precipitating elements. Viscosity (40℃), acid value, moisture and PQ value are four critical variables for gear wear diagnosis, metallic leaching elements and additive precipitating elements are two types of supporting diagnostic indicators. Additionally, dependent variables, Results, can be set as variable ‘Y’, which reflects the quality of the gear oil. 2.2. Data preprocessing There are notable variations in gear oil variables during wind gear oil degradation. After outliers handling and aggregating all gear oil data of wind turbines, Fig. 2 demonstrates considerable heterogeneity in the distribution of these variables across different wind turbines. Data heterogeneity is commonly found in comprehensive fault analysis tasks involving multiple turbines. Failing to account for this heterogeneity when aggregating indicator models can lead to decreased diagnostic performance and slower convergence of the global model. In order to remove differences in numerical values and the influence of units among different variables, we need to apply a normalization function to scale each variable in the input dataset. The normalized variable is transformed following function expression: 1 where min(X) and max(X) denote the minimum and maximum value of vector X. The normalization process maps the data uniformly onto the [0,1] interval for all variable, as shown in Fig. 3 . 3. Methodology 3.1. SMR modeling Since the dependent variables (Y) was continuous in the study, analysis was performed with a SMR model. In this multivariate model, stepwise approach was used for higher accuracy [21–23]. The R software (version 4.4.1) was used to build SMR model [24]. Chi-square analysis was used for the evaluation of the relationship between the variables and the dependent variable in the estimated model. Finally, the model coefficients were reported, and the estimated model was expressed as a function [25–29]. The reference value was taken as P ≦ 0.05 to test the statistical significance. Table 2 indicates 11 stages of SMR modeling process through continuous optimization of AIC value. In the first step of the modeling process, the dependent variable Y is linearly regressed on all the independent variables, and the AIC value of the model is obtained as -292.37, and from the feedback of the model analysis, it can be seen that after doing optimization on the independent variable Ni, the AIC of the model obtained from the modeling again can be further reduced to -294.37, and so on, and the continuous optimization is carried out to get the best model in the 11th step, and at this step, the AIC value is -309.76, and will no longer be reduced, indicating that the model at this step is the model with the best goodness-of-fit. Additionally, the Chisquare value of model is 23.44502 with p far less than 0.0001. The optimal model function is shown as following function expression: 2 where the PQ and Si indicators have the highest level of significance for the model, with probability P-values of 2.25e−05 and 3.14e−05 respectively. Table 2 SMR modeling process of gear oil datasets Figure 4 displays diagnosis scheme of the above SMR model, which includes four subplots i.e. residuals ~ fitted values plot, standardized residuals ~ quantiles plot, root standardized residuals ~ fitted values plot and standardized residuals ~ leverage plot. The residual ~ fitted plot show that the dependent variable is linearly related to the independent variable, the residual values are not systematically related to the predicted (fitted) values; the Q-Q residuals plot shows that the assumption of normality is basically satisfied and the points on the plot fall on a straight line at a 45⁰ angle; in the scale-location plot, the points around the horizontal line should be randomly distributed, and this plot seems to satisfy that assumption, which indicates that the model is a good fit. The residuals ~ leverage plot provides information about the individual observations, and from the graphs it is possible to identify outliers, high leverage points and strong influence points, the more typical anomalies being records 8,12 and 69. 3.2. CFLF modeling Gear oil performance diagnosis is a nonlinear, multivariate comprehensive influence of the mathematical modeling process, so it is necessary to introduce nonlinear-based clustering federated learning algorithms for comprehensive analysis and evaluation of gear oil performance. Following the development of local multiscale residual networks for each wind turbine, we proposed the representational canonical correlation clustering method to group these local indicators into distinct clusters [30–36]. Initially, the use of R language NbClust package to determine the optimal number of clusters, NbClust package defines dozens of evaluation indicators, the number of clusters from 2 traversed to 9, through these indicators to see how many clusters in the number of optimal, with the increase in the number of clusters, the number of each category is becoming less and less, closer and closer to each other, so the value of the Dindex Values is certainly decreasing with the increase of the cluster number. When the slope changes slowly, it can be assumed that further increase the number of clusters does not enhance the effect, at this time, this “elbow point” is the optimal number of clusters, from 1 class to 7 classes declined very quickly, and after that, it declined slowly, so the optimal number of clusters is chosen to be 7, as shown in Fig. 5a and Fig. 5b; Fig. 5c demonstrates that the number of supporting indicators is maximized when the number of clusters is 7. The results of the above clustering can be visualized through the fviz_cluster function in the FactoMineR package, as shown in Fig. 6. Dim1, Dim2 in the graph represent what percent of the trend in the gear oil data set can be explained by Principal Component 1 and Principal Component 2; A dot represents a row in the data frame, and the distance between the dots represents the similarity. Figure 6 shows the relative concentration of data points in clusters with cluster numbers 4, 5 and 6. The optimized cluster analysis shows that records 8, 12 are outliers, indicating that the gear oil is in a warning state and needs attention. By employing spectral clustering, the graph is partitioned to effectively divide the indicators into separate clusters. Subsequently, the clustered federated learning model carry out local training tasks, which performs cluster-internal model aggregation. After determining the number of clusters for optimal clustering, it is necessary to create a data frame of the clustering results. On this basis, neural network modeling is performed for each cluster of data groups to obtain the model combination as shown in Fig. 7. In R language, using the neuralnet package for neural network regression prediction is a relatively direct process. This package provides a simple method for building and training neural network models, suitable for regression problems. The neuralnet() function in the neuralnet package was used to create a neural network model, and the number of neurons for hidden layer is set to 5, with a threshold of 0.01. The seven network diagrams in Fig. 7 correspond to the neural network model diagrams of the seven clustered grouped data records, and the correspondences are illustrated in red color in the middle right side of the figure. From the comparison of model deviation data in the lower right corner of the figure, it can be seen that the deviation ranges from 0.000001 to 0.006, which indicates that the accuracy of this CFLF modeling method is high. 3.3. Evaluation for combining model of SMR & CFLF The SMR model has advantages in predicting or explaining the quantitative effects between variables, but it has shortcomings such as overfitting and weak non-linear modeling capabilities; CFLF belongs to unsupervised learning, which has the advantage of not requiring preset labels or target variables, and only relying on similarity measures of feature variables (such as Euclidean distance). The disadvantage of CFLF is that it outputs class labels or cluster assignments of samples. The combining model of SMR and CFLF can be proposed as SMR-CFLF composite model, which is based on the highly canonical correlation series obtained from SMR, afterwards CFLF modeling is carried out on this basis. Gear oil performance dataset can be extracted through SMR modeling process, and canonical series include Vis40, PQ, Mn, Si, Ca and Zn. Extracting 15% of the data volume for the prediction of the above SMR model, the obtained Y-values were analyzed for deviation from the actual Y-values, and the root mean squared error (RMSE) was 0.0708, which demonstrates higher accuracy of this model. The same validation data set was used for the prediction of the CFLF model and the root mean square error RMSE value obtained was 0.0729. However, the SMR-CFLF composite model can obtain an RMSE value of 0.0455 for the same validation data set predictive analysis. Table 3 presents corresponding RMSE values among SMR, CFLF and combining SMR - CFLF model. Table 3 Comparison of root mean square errors for different models. Model SMR CFLF SMR - CFLF RMSE 0.0708 0.0729 0.0455 Improvement of NEFM over MLR / −2.97% 35.73% As can be seen from Table 3, during the analysis of the wind turbine gear oil indicators data set, the CFLF model alone did not have a better RMSE value than the SMR model, but instead the deviation was − 2.97%; whereas, the RMSE value of the combined SMR-CFLF model achieved a relatively significant improvement with a deviation ratio of 35.73%. The RMSE results of SMR-CFLF model are better than SMR model by 35.73%. 4. Conclusions This study introduces a SMR modeling for gear oil diagnosis of wind turbines with eleven steps optimization. By grouping oil indicators with similar class into clusters, CFLF is introduced for addressing data heterogeneity issues and generating clustered models to assess the performance of gear oil with the deviation ranges from 0.000001 to 0.006. A combining model of SMR and CFLF is proposed to assess the performance of gear oil. Actual data analysis of wind turbine gear oil showcase the superior performance of the proposed model over single SMR model with higher prediction accuracy of 35.73%. The study provides significant promise in integrating SMR and CFLF into feature interaction learning for optimal diagnostic of wind gear oil. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request and can be contacted at corresponding author (Yuchun Li, [email protected] ). References Tian Z, Wang J. Variable frequency wind speed trend prediction system based on combined neural network and improved multi-objective optimization algorithm. Energy 2022;254:124249. https://doi.org/10.1016/j.energy.2022.124249. Liu H, Chen C. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 29 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Submission checks completed at journal 19 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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(b) Second diferences Dindex values with clusters; (c) Number of indicators supported with clusters.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5681585/v1/96465fc8827c7ad8b18174fb.png"},{"id":81221584,"identity":"b0c37f3d-bcaa-4371-a2b3-af3fb8e07c84","added_by":"auto","created_at":"2025-04-23 15:14:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24835,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of clustering results by fviz_cluster() function\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5681585/v1/8d73e3c381ff9bdcc7579840.png"},{"id":81220457,"identity":"0fd19fb3-bf90-4be4-8741-90d215bed23b","added_by":"auto","created_at":"2025-04-23 15:06:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":266778,"visible":true,"origin":"","legend":"\u003cp\u003eModels of neural network learning after Clustering\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5681585/v1/c71e407315a2df8af4192b60.png"},{"id":86180163,"identity":"9c0cd41d-6323-48bd-be51-0fdcb84d76ce","added_by":"auto","created_at":"2025-07-07 16:21:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1031320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5681585/v1/89f6e235-c030-4432-9721-6dca2b237e81.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel diagnosis methodology of gear oil for wind turbine combining stepwise multivariate regression and clustered federated learning framework","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull;The gear oil of wind turbines can be diagnosed and estimated by knowing only the acid value, viscosity, PQ value, Mn, Si, Ca and Zn.\u003c/p\u003e\u003cp\u003e\u0026bull;We reached the appropriate model in the eleventh step using stepwise multivariate regression (SMR) analysis with RMSE of 0.0708.\u003c/p\u003e\u003cp\u003e\u0026bull;We proposed combining model of SMR and clustered federated learning framework (CFLF) for wind gear oil diagnosis, which has the power to increase 35.73% in accuracy over single SMR model.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eWind energy is a very desirable green choice among many new energy sources because it is renewable and clean energy source with recyclability and high efficiency [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The main gear lubrication systems play key role on normal operation of wind turbines. However, the large-scale wind power poses a critical impact on the main gear lubrication systems, which renders gear oil becoming key monitor spot in wind turbine system. Addressing this challenge requires reliable analysis method for comprehensive analysis of gear oil and afterward evaluation model, facilitating optimal maintenance time selection for wind turbines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a result, main gear oil analysis has become a focal point, particularly with the application of deep learning methods. Searching with the keywords of gear oil / wind turbines, there are 23558 documents from 2014-01 to 2024-11 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and the annual average number of documents issued is 2142. As shown in illustration inside Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 2023 reaches the peak of 3069 annual publications, and 2019 has the fastest growth rate of 12.64%, suggesting that the research in this field has been developed rapidly and is in a fast rising stage. 23558 papers were retrieved, and the top 30 journals in terms of the number of publications are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, in which the journal with the most publications is Energies (795 articles); Journal of Physics: Conference Series ranks second, with 769 articles; Renewable Energy ranks third, with 511 articles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStepwise multivariate regression (SMR) analysis belongs to one of linear regression methods, which combines multivariate and stepwise modeling technology. Limitations of linear models lies in poor fitting of nonlinear relationships, sensitivity to outliers and insufficient handling of multicollinearity problems [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Deep learning, as a branch of machine learning, is widely applied in industry due to its remarkable feature learning capabilities and capacity for handling high-dimensional data [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To address data inhomogeneity characteristic and overcome data sparse, Federated Learning (FL) has been introduced as a collaborative distributed machine learning approach in fault diagnosis [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Hybrid deep learning model and convolutional neural network (CNN) can also be utilized for spatial feature extraction to capture the temporal dependencies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo tackle the challenges in wind turbine fault diagnosis using federated learning, this paper compares a SMR model and clustered federated learning framework (CFLF) for gear oil diagnosis of wind turbines. On this basis, a combining model of SMR and CFLF was proposed, which leverages the multi-scale residual to extract spatial features for gear oil diagnosis, experimental datasets is subjected to these modeling approaches for comparing diagnosis performance of wind gear oil.\u003c/p\u003e"},{"header":"2. Data description and process","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Data description\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents datasets of gear oil for a wind farm in south China. In addition to the general categories of data there are four main categories of data, i.e. chemical data, pollution data, metallic leaching elements and additive precipitating elements. Viscosity (40℃), acid value, moisture and PQ value are four critical variables for gear wear diagnosis, metallic leaching elements and additive precipitating elements are two types of supporting diagnostic indicators. Additionally, dependent variables, Results, can be set as variable \u0026lsquo;Y\u0026rsquo;, which reflects the quality of the gear oil.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e2.2. Data preprocessing\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003cp\u003eThere are notable variations in gear oil variables during wind gear oil degradation. After outliers handling and aggregating all gear oil data of wind turbines, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates considerable heterogeneity in the distribution of these variables across different wind turbines. Data heterogeneity is commonly found in comprehensive fault analysis tasks involving multiple turbines. Failing to account for this heterogeneity when aggregating indicator models can lead to decreased diagnostic performance and slower convergence of the global model. In order to remove differences in numerical values and the influence of units among different variables, we need to apply a normalization function to scale each variable in the input dataset. The normalized variable is transformed following function expression:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAALwAAAArCAYAAADCKuJQAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAeFSURBVHhe7Z05iBRNFMdrPwQRj0QDj2g90UA80EAMPDZYVyNRVwSPQDEwUNTATN1I8FgExWAN1A1k0UBQ2WBFxFwFE1HxSDxADDwCs/n69+w31rZzVM9M7/Ta7wdFd1dXVder/r+eququ3Y5ShDOMgvBfvDWMQmCCNwqFCd4oFCZ4o1CY4HPK3r17XUdHh4Tz589L3L1798pxXV1d7ufPnxLfKj59+uTmzZsn1wmFumn9koTaUKuMlsMsjZE/fvz4UdqzZ0/p3LlzcUyp9PHjx1Jvb2/p6dOncUx7oW7UsRppbEimywoTfI5BFDt27BDhIJS+vj7ZzwN3796tKXYljQ2UR7lZYoLPMQhk1apVpcePHzcsdsqYO3du6ebNm6WNGzfyzkWEpfEc65NVr4dIK+XTdNSDuBBxprHBd46sMMHnHMSJuBoRgQoTsU6fPl0ERWAfMSNGREuaqP8uW84hzmQ+0mkejhEx+yGE2qDOQflZETRoffbsmZsxY0Z5sKHBH2j4gxENDIAYCBmNwYDuw4cPbs6cOW7KlClxbDjkuXPnjovE5q5du+aWL1/uZs6c6SLhutu3b7tZs2ZJ2d+/f+fB5wYHB+VcpXyki8Qv5VKnadOmualTp8pxLdLYQHmUS/qsCBI8Br9//14aACIvdJE3umPHjskxbNmyxUWeKY3S3d0t+2/evJFGNdKDUPr7+93Jkyfdixcvqj44/JmQZOBcGhBcZ2dnfNQ8oTaMJcHTknjn2bNnRdBfv36NY/+AcWfOnHGXLl1yw8PD4iRGY9CWQ0NDbv/+/dKOiPDz58/x2dFcv35dns6VAufaRRobxpJU8/ALFixwy5Ytc2/fvnVPnjyJY38bR/fmxIkTbufOnXGs0Qh0H2nL3t5e+XXkQbN69Wr38OHDOEX70W5Q1CePY0bTqA2UR7mUnxnRkyAVjNTJ5k9JnTp1KtOBRhHwB5gEnQHR9ibooDEN3CfyJgejlMU9Y8sx6TQtcV1dXeV89+/fL6cjvw5wk7M0zdpAfXI3S4MRfsVN7MUEHSDuVooTh0s6UatJ/WnBypUrZST/7ds3t23bNjm2/nrxYJJi06ZN7tChQ3FMczDAXrp0qZSbJakFT59s7dq1MnDdunVr5hXMAvqXlWY1qgV/+tX4A7N027dvb7p9yI/Y/Vm/rGhoxRPeeOPGDZmmZL623vyqYeSF1E94vHD+/PkyPfnu3buqI3XDyCXSkw/k6NGjMqjwR+NZDzLyDm1gIb8hSfATnif7+vXrpc9OF4YBC7x8+VK2RSVqQws5DkmCBO+LXdmwYYN0a3iryoun8YQNWgtM5AVVefXqVen48eMV59mZg2cunhcTNg8/tvAiRz/VVfSlEUHP6TsTgs6ZV8rbalQbabq7teoVYhufHofYVVHwNMyFCxdEzPTbK4FRfMqpF8Q5jOzhpiKAJNwz4v2bzj1q5+qiUKrZpITaFmLXX4Ink3qO70FcVKHQZJpK6dTT9a0siwlwIr9SvLZWx1HnUu8lH6/DOT8wMFAuC+cirV5Py/3XB9DYV0sY3Pw8rS4KoZ5NSqht9eyq2aVpBVTuwIEDIkqCbyD7KmAq71dW071+/VrEjcFa1tWrVyUPjsA+TkPaet6dJdQNW/wVQtRJ4znW+mGrptF0fhzOq9+vaJdRz9e6mVyLNslidVEa+7Qees+S+ZLtUMsmJdS2enZlLniMiQa9paGhITlWYfqNohCvjcEWofsVp6zu7m4RO/nWrFkjDZmm4bJAr69ipW4E9rnZ2ErdSEPa5L6mAdpHz6mjA1vaS9NVw89fi0rtX4009rV75VQ9u1K/eErLrVu33JcvX1xPT4/M5vz69UtmePi8ePHixeXvcDjHjM+iRYvK+8wM+W9xKYs/7cBnynxqOmnSJJk5in4F5LNSvutpB9QxdGVRdLOkzg8ePJB8xE+YMKH8rfjly5dly2IM7Nf2CVllRLtlsboojX2Rptq6cqqeXZkKnhUurHQ5cuSIVBJhAoJl/p7vJ5RHjx7Jm1tESzoaZd26dfHZ32VF3ut27dpVdggtF/HjPDT8eILpzhUrVoxaUIM9utAmDbRJM6uLirJyKlPB89TCcAQOCHPJkiXlp9nz58/FGBYM7Nu3z128eFFESzoWDPiezC/C7Nmz5TxPSW6EPtEphxdheVhRE4KuEYbop3eUuLmZV65ckZVjhw8fDrq5tGGzq4uKsnIqU8EnhYsw+amZPHmyO3jwoJs4caI8DdgfGRkpv9giHV0bH/8XwRc/gsDrOe87SJ6hXTZv3vzX14HYEvVT3enTp2XlGF+l7t69W266dhlwdp9cry6qQzWblEZsq2tX5MFGC2BARXMyOKu3sohj0nG8cOFC2SewTxwDOz8NZUXOIFvOQXRjy9fQPMBgX+O4ZrUBIeWnmaVJY5+mJW6sV07Vs8sEP47gxiOEUJHWAlEmhdYOWmkT1LMr81kao3XQ5WvFKqOxWl0UQqtsghC77F/ejEP4o1eMWZJjgBD0Q7hG8mZJMzZBqF0meKNQWJfGKBQmeKNQmOCNQmGCNwqFCd4oFCZ4o0A49z8D2EWoFT+B8AAAAABJRU5ErkJggg==\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003emin(X)\u003c/em\u003e and \u003cem\u003emax(X)\u003c/em\u003e denote the minimum and maximum value of vector X. The normalization process maps the data uniformly onto the [0,1] interval for all variable, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.1. SMR modeling\u003c/h2\u003e\n \u003cp\u003eSince the dependent variables (Y) was continuous in the study, analysis was performed with a SMR model. In this multivariate model, stepwise approach was used for higher accuracy [21\u0026ndash;23]. The R software (version 4.4.1) was used to build SMR model [24]. Chi-square analysis was used for the evaluation of the relationship between the variables and the dependent variable in the estimated model. Finally, the model coefficients were reported, and the estimated model was expressed as a function [25\u0026ndash;29]. The reference value was taken as P\u0026thinsp;≦\u0026thinsp;0.05 to test the statistical significance. Table\u0026nbsp;2 indicates 11 stages of SMR modeling process through continuous optimization of AIC value. In the first step of the modeling process, the dependent variable Y is linearly regressed on all the independent variables, and the AIC value of the model is obtained as -292.37, and from the feedback of the model analysis, it can be seen that after doing optimization on the independent variable Ni, the AIC of the model obtained from the modeling again can be further reduced to -294.37, and so on, and the continuous optimization is carried out to get the best model in the 11th step, and at this step, the AIC value is -309.76, and will no longer be reduced, indicating that the model at this step is the model with the best goodness-of-fit. Additionally, the Chisquare value of model is 23.44502 with p far less than 0.0001. The optimal model function is shown as following function expression:\u003c/p\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"text-align: start; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); font-size: medium; font-family: \u0026quot;\u0026quot;;\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere the PQ and Si indicators have the highest level of significance for the model, with probability P-values of 2.25e\u0026minus;05 and 3.14e\u0026minus;05 respectively.\u003c/p\u003e\n \u003cp\u003eTable 2\u0026nbsp;\u003c/p\u003eSMR modeling process of gear oil datasets\u003cdiv\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003cp\u003eFigure 4 displays diagnosis scheme of the above SMR model, which includes four subplots i.e. residuals\u0026thinsp;~\u0026thinsp;fitted values plot, standardized residuals\u0026thinsp;~\u0026thinsp;quantiles plot, root standardized residuals\u0026thinsp;~\u0026thinsp;fitted values plot and standardized residuals\u0026thinsp;~\u0026thinsp;leverage plot. The residual\u0026thinsp;~\u0026thinsp;fitted plot show that the dependent variable is linearly related to the independent variable, the residual values are not systematically related to the predicted (fitted) values; the Q-Q residuals plot shows that the assumption of normality is basically satisfied and the points on the plot fall on a straight line at a 45⁰ angle; in the scale-location plot, the points around the horizontal line should be randomly distributed, and this plot seems to satisfy that assumption, which indicates that the model is a good fit. The residuals\u0026thinsp;~\u0026thinsp;leverage plot provides information about the individual observations, and from the graphs it is possible to identify outliers, high leverage points and strong influence points, the more typical anomalies being records 8,12 and 69.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.2. CFLF modeling\u003c/h2\u003e\n \u003cp\u003eGear oil performance diagnosis is a nonlinear, multivariate comprehensive influence of the mathematical modeling process, so it is necessary to introduce nonlinear-based clustering federated learning algorithms for comprehensive analysis and evaluation of gear oil performance. Following the development of local multiscale residual networks for each wind turbine, we proposed the representational canonical correlation clustering method to group these local indicators into distinct clusters [30\u0026ndash;36]. Initially, the use of R language NbClust package to determine the optimal number of clusters, NbClust package defines dozens of evaluation indicators, the number of clusters from 2 traversed to 9, through these indicators to see how many clusters in the number of optimal, with the increase in the number of clusters, the number of each category is becoming less and less, closer and closer to each other, so the value of the Dindex Values is certainly decreasing with the increase of the cluster number. When the slope changes slowly, it can be assumed that further increase the number of clusters does not enhance the effect, at this time, this \u0026ldquo;elbow point\u0026rdquo; is the optimal number of clusters, from 1 class to 7 classes declined very quickly, and after that, it declined slowly, so the optimal number of clusters is chosen to be 7, as shown in Fig. 5a and Fig. 5b; Fig. 5c demonstrates that the number of supporting indicators is maximized when the number of clusters is 7. The results of the above clustering can be visualized through the fviz_cluster function in the FactoMineR package, as shown in Fig. 6. Dim1, Dim2 in the graph represent what percent of the trend in the gear oil data set can be explained by Principal Component 1 and Principal Component 2; A dot represents a row in the data frame, and the distance between the dots represents the similarity. Figure 6 shows the relative concentration of data points in clusters with cluster numbers 4, 5 and 6. The optimized cluster analysis shows that records 8, 12 are outliers, indicating that the gear oil is in a warning state and needs attention.\u003c/p\u003e\n \u003cp\u003eBy employing spectral clustering, the graph is partitioned to effectively divide the indicators into separate clusters. Subsequently, the clustered federated learning model carry out local training tasks, which performs cluster-internal model aggregation. After determining the number of clusters for optimal clustering, it is necessary to create a data frame of the clustering results. On this basis, neural network modeling is performed for each cluster of data groups to obtain the model combination as shown in Fig. 7. In R language, using the neuralnet package for neural network regression prediction is a relatively direct process. This package provides a simple method for building and training neural network models, suitable for regression problems. The neuralnet() function in the neuralnet package was used to create a neural network model, and the number of neurons for hidden layer is set to 5, with a threshold of 0.01. The seven network diagrams in Fig. 7 correspond to the neural network model diagrams of the seven clustered grouped data records, and the correspondences are illustrated in red color in the middle right side of the figure. From the comparison of model deviation data in the lower right corner of the figure, it can be seen that the deviation ranges from 0.000001 to 0.006, which indicates that the accuracy of this CFLF modeling method is high.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.3. Evaluation for combining model of SMR \u0026amp; CFLF\u003c/h2\u003e\n \u003cp\u003eThe SMR model has advantages in predicting or explaining the quantitative effects between variables, but it has shortcomings such as overfitting and weak non-linear modeling capabilities; CFLF belongs to unsupervised learning, which has the advantage of not requiring preset labels or target variables, and only relying on similarity measures of feature variables (such as Euclidean distance). The disadvantage of CFLF is that it outputs class labels or cluster assignments of samples. The combining model of SMR and CFLF can be proposed as SMR-CFLF composite model, which is based on the highly canonical correlation series obtained from SMR, afterwards CFLF modeling is carried out on this basis. Gear oil performance dataset can be extracted through SMR modeling process, and canonical series include Vis40, PQ, Mn, Si, Ca and Zn.\u003c/p\u003e\n \u003cp\u003eExtracting 15% of the data volume for the prediction of the above SMR model, the obtained Y-values were analyzed for deviation from the actual Y-values, and the root mean squared error (RMSE) was 0.0708, which demonstrates higher accuracy of this model. The same validation data set was used for the prediction of the CFLF model and the root mean square error RMSE value obtained was 0.0729. However, the SMR-CFLF composite model can obtain an RMSE value of 0.0455 for the same validation data set predictive analysis. Table\u0026nbsp;3 presents corresponding RMSE values among SMR, CFLF and combining SMR - CFLF model.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparison of root mean square errors for different models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFLF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSMR - CFLF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImprovement of NEFM over MLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;2.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs can be seen from Table\u0026nbsp;3, during the analysis of the wind turbine gear oil indicators data set, the CFLF model alone did not have a better RMSE value than the SMR model, but instead the deviation was \u0026minus;\u0026thinsp;2.97%; whereas, the RMSE value of the combined SMR-CFLF model achieved a relatively significant improvement with a deviation ratio of 35.73%. The RMSE results of SMR-CFLF model are better than SMR model by 35.73%.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study introduces a SMR modeling for gear oil diagnosis of wind turbines with eleven steps optimization. By grouping oil indicators with similar class into clusters, CFLF is introduced for addressing data heterogeneity issues and generating clustered models to assess the performance of gear oil with the deviation ranges from 0.000001 to 0.006. A combining model of SMR and CFLF is proposed to assess the performance of gear oil. Actual data analysis of wind turbine gear oil showcase the superior performance of the proposed model over single SMR model with higher prediction accuracy of 35.73%. The study provides significant promise in integrating SMR and CFLF into feature interaction learning for optimal diagnostic of wind gear oil.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request and can be contacted at corresponding author (Yuchun Li, [email protected] ).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTian Z, Wang J. Variable frequency wind speed trend prediction system based on combined neural network and improved multi-objective optimization algorithm. 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Clustering driven incremental learning surrogate model-assisted evolution for structural condition assessment, Mechanical Systems and Signal Processing, 2025, 224, 112146, https://doi.org/10.1016/j.ymssp.2024.112146.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stepwise multivariate regression, Gear oil evaluation, Clustered federated learning framework, Wind turbines","lastPublishedDoi":"10.21203/rs.3.rs-5681585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5681585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eData-driven approaches demonstrate significant potential in accurately diagnosing faults in wind turbines. To enhance diagnostic performance, we introduce a clustered federated learning framework (CFLF) to wind gear oil diagnosis. Initially, a stepwise multivariate regression (SMR) model is introduced and optimized after data process, which integrates a multiscale feature and AIC diagnosis feature. Subsequently, to tackle data heterogeneity among different indicators, canonical correlation series of representations are extracted from the SMR models, and a combining model of CFLF method and SMR is proposed to assess the performance of gear oil. Actual data analysis of wind turbine gear oil showcase the superior performance of the proposed model over single SMR model with higher prediction accuracy of 35.73%. This study provides a new technique for evaluating gear oil in the wind energy sector.\u003c/p\u003e","manuscriptTitle":"A novel diagnosis methodology of gear oil for wind turbine combining stepwise multivariate regression and clustered federated learning framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 15:06:18","doi":"10.21203/rs.3.rs-5681585/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T06:15:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T05:58:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T16:15:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69508418015102304389380384993528558766","date":"2025-04-22T16:08:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53949724241923957900886770156495197564","date":"2025-04-22T08:56:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-22T08:52:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-19T06:21:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-09T13:59:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7962b539-200b-495d-a81e-f882a6d4ff04","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47525005,"name":"Physical sciences/Energy science and technology/Renewable energy"},{"id":47525006,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2025-07-07T16:15:25+00:00","versionOfRecord":{"articleIdentity":"rs-5681585","link":"https://doi.org/10.1038/s41598-025-06826-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-02 15:57:56","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-04-23 15:06:18","video":"","vorDoi":"10.1038/s41598-025-06826-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-06826-9","workflowStages":[]},"version":"v1","identity":"rs-5681585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5681585","identity":"rs-5681585","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}