A fault diagnosis method of rotating machinery based on improved multiscale attention entropy and random forests

Chen, Fei; Zhang, Liyao; Liu, Wenshen; Zhang, Tingting; Zhao, Zhigao; Wang, Weiyu; Chen, Diyi; Wang, Bin

doi:10.1007/s11071-023-09126-x

Cited by 14 publications

(3 citation statements)

References 56 publications

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“…It can be observed that the d nostic accuracy of the model for Category 1 (normal) is the highest, and the diagno accuracy for Category 3 (bearing ball failure) and Category 4 (bearing outer ring failu is lower. In addition, the diagnosis performance of the constructed DoubleEnsemble-LightGBM model was compared with that of the original LightGBM model and three other ensemble learning models with excellent performance in the field of fault diagnosis: the RF model used in [29], the AdaBoost model used in [30], and the XGBoost model used in [31]. The average value of the overall fault diagnosis accuracy of 10 experiments was taken as the evaluation index, and the experimental comparison results are shown in Table 9.…”

Section: Analysis Of Experimental Results Of a Widely Used Datasetmentioning

confidence: 99%

A Fault Diagnosis Method for Key Components of the CNC Machine Feed System Based on the DoubleEnsemble–LightGBM Model

Li,

Wang,

et al. 2024

Machines

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To solve the problem of fault diagnosis for the key components of the CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and an ensemble learning model is proposed in this study. First, various monitoring signals including vibration signals, noise signals, and current signals are collected. Then, the monitoring signals are preprocessed and the time domain, frequency domain, and time–frequency domain feature indices are extracted to construct a multi-dimensional mixed-domain feature set. Finally, the feature set is entered into the constructed DoubleEnsemble–LightGBM model to realize the fault diagnosis of the key components of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the widely used dataset and the feed system test bench dataset, and the average overall accuracy is 91.07% and 98.06%, respectively. Compared with XGBoost and other advanced ensemble learning models, this method demonstrates better accuracy. Therefore, the proposed method provides technical support for the stable operation and intelligence of CNC machines.

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Section: Analysis Of Experimental Results Of a Widely Used Datasetmentioning

confidence: 99%

A Fault Diagnosis Method for Key Components of the CNC Machine Feed System Based on the DoubleEnsemble–LightGBM Model

Li,

Wang,

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…Specifically, LSTM-LAE [30] supervised convolutional autoencoder (SCAE) [31] were trained using the same training dataset D as our proposed method. However, CMNE-TSNE-RF [32] and two-level SVM [33] employ their own feature selection methods, and thus, they were trained using dataset A. In addition to these four comparative methods, adjustments were made to compare with the state-of-theart digital twinning technology.…”

Section: Experiments 3-validating the Performance Of The Ssaementioning

confidence: 99%

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Liu,

Qi,

et al. 2024

Meas. Sci. Technol.

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The energy loss and substantial costs associated with faults in photovoltaic (PV) systems impose significant limitations on their efficiency and reliability. Addressing current issues in PV fault diagnosis such as the lack of typical fault data, imbalanced data distribution, and poor diagnostic performance, this paper proposes an intelligent fault diagnosis method for PV systems, Deep Digital Twins with Information Gain Stacking Sparse Autoencoders(DDT-IGSSAE).Initially, the method designs a novel DDT modeling framework tailored to actual PV system specifications. This framework utilizes a mechanism simulation model to generate typical data under various states. Simultaneously, a deep data model is constructed to learn the distribution characteristics of the mechanism model and complete data diversification, achieving the fusion and complementation of data from both models. Subsequently, a diagnostic network using IGSSAE is introduced. This network utilizes Information Gain Ratio (IGR) to assess feature classification contributions, enabling automatic feature selection. Based on the input features, a Stacked Sparse Autoencoder (SSAE) fault classification network is designed, incorporating multi-level feature compression to enhance the model's stability and diagnostic accuracy. Finally, a case study is conducted using a 250 kW grid-connected PV system, thoroughly validating the method's effectiveness with a diagnostic accuracy of 98.4%.

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“…Chen et al [10] proposed a fault diagnosis method for rotating machinery based on improved multiscale attention entropy and random forests. The method used a nonlinear dynamics technique called multiscale attention entropy to measure the signal complexity at multiple time scales and a composite multiscale attention entropy to overcome the problem of insufficient coarse-graining.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Various Feature Selection Methods for Churn Prediction Using Various Machine Learning Algorithms

Kusnawi,

Ipmawati,

Asadulloh

et al. 2024

JOIV : Int. J. Inform. Visualization

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This study aims to examine the effect of customer experience on customer retention at DQLab Telco, using machine learning techniques to predict customer churn. The study uses a dataset of 6590 customers of DQLab Telco, which contains various features related to their service usage and satisfaction. The data includes various features such as gender, tenure, phone service, internet service, monthly charges, and total charges. These features represent the demographic and service usage information of the customers. The study applies several feature selection methods, such as ANOVA, Recursive Feature Elimination, Feature Importance, and Pearson Correlation, to select the most relevant features for churn prediction. The study also compares three machine learning algorithms, namely Logistic Regression, Random Forest, and Gradient Boosting, to build and evaluate the prediction models. This study finds that Logistic Regression without feature selection achieves the highest accuracy of 79.47%, while Random Forest with Feature Importance and Gradient Boosting with Recursive Feature Elimination achieve accuracy of 77.60% and 79.86%, respectively. The study also identifies the features influencing customer churn most, such as monthly charges, tenure, partner, senior citizen, internet service, paperless billing, and TV streaming. The study provides valuable insights for DQLab Telco in developing customer churn reduction strategies based on predictive models and influential features. The study also suggests that feature selection and machine learning algorithms play a vital role in improving the accuracy of churn prediction and should be customized according to the data context.

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A fault diagnosis method of rotating machinery based on improved multiscale attention entropy and random forests

Cited by 14 publications

References 56 publications

A Fault Diagnosis Method for Key Components of the CNC Machine Feed System Based on the DoubleEnsemble–LightGBM Model

A Fault Diagnosis Method for Key Components of the CNC Machine Feed System Based on the DoubleEnsemble–LightGBM Model

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Leveraging Various Feature Selection Methods for Churn Prediction Using Various Machine Learning Algorithms

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