A literature review of fault diagnosis based on ensemble learning

Mian, Zhibao; Deng, Xiaofei; Dong, Xiaohui; Tian, Yuzhu; Cao, Tianya; Chen, Kairan; Jaber, Tareq Al

doi:10.1016/j.engappai.2023.107357

Cited by 25 publications

(3 citation statements)

References 127 publications

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“…Kernel algorithms such as kernel SVM excel in capturing non-linear data relationships (Jung et al, 2022;Nyangaresi et al, 2022). • Ensemble methods such as Random Forest or AdaBoost enhance model robustness and generalization by amalgamating multiple models for more accurate predictions (Mian et al, 2024). • Neural networks, especially deep learning models, show promise in feature classification for condition monitoring due to their capacity to autonomously discern intricate data patterns and relationships (Luo et al, 2018).…”

Section: Potential Reasons Behind the Selection Of ML Algorithms For ...mentioning

confidence: 99%

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Tambake,

Deshmukh,

Pardeshi

et al. 2024

Front. Mater.

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Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45 rpm), feed (ranging from 0.6 to 1 mm/rev), and depth of cut (6.8 mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise.

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Section: Potential Reasons Behind the Selection Of ML Algorithms For ...mentioning

confidence: 99%

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Tambake,

Deshmukh,

Pardeshi

et al. 2024

Front. Mater.

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“…A lot of scientific papers are concerned with this matter [13,14]. Modern review articles [15,16] that cover the existing methods of AC drive diagnostics reveal several common trends in the development of diagnostics systems. These include the search for new means of measuring parameters [17,18], the search for algorithms for determining specific types of defects in electrical equipment [19,20], system approaches in solving diagnostic problems [21,22], and the use of artificial intelligence in diagnostic problems [23,24].…”

Section: Introductionmentioning

confidence: 99%

A Diagnostic Curve for Online Fault Detection in AC Drives

Koteleva,

Korolev

2024

Energies

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The AC drive is an important component and the most common element of any manufacturing process. A particularly serious task is the proper assessment of the AC drive’s technical condition, as its failure can cause problems for entire units and complexes of industrial enterprises. At present, there are several approaches either to determine electric drives’ condition or to find certain defects. Frequently, these methods require the installation of additional equipment that exceeds the price of the electric drive by several times. In this work, a simple approach is proposed. It includes the use of a diagnostic curve to assess the condition. This diagnostic curve is produced from the measurement results of the current sensors on the drive. Based on the Park vector modification, this is a simple and affordable way to obtain real-time information. The obtained curve can be used for the following purposes: directly for condition assessment by visual monitoring, as a sign for diagnostic systems built on artificial intelligence methods, for dynamic tuning of the drive control system. The article gives the algorithm for obtaining the diagnostic curve, showing its efficiency for model and field experiments. In model experiments, the faults in the rotor and stator of the drive were simulated; in field experiments, the state was analyzed by changing the load on the motor.

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“…As artificial intelligence technology advances, machine learning applications in transformer fault diagnosis have gained momentum. Support Vector Machine 11 – 13 , Convolutional Neural Network(CNN) 14 , 15 , Self-Organizing Mapping Neural Network(SOM) 16 , Gate Recurrent Unit(GRU) 17 , 18 , Cloud Model(CM) 19 , Adaptive Boosting(AdaBoost) 20 , Gradient Boosting Decision Tree(GBDT) 21 and other models have demonstrated remarkable success in classification identification. Yet, The fault diagnosis models mentioned above were all constructed based on the assumption of having a relatively large dataset.…”

Section: Introductionmentioning

confidence: 99%

Transformer fault diagnosis method based on SMOTE and NGO-GBDT

Wang,

Chi,

Ding

et al. 2024

Sci Rep

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In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a transformer fault diagnosis method based on SMOTE and NGO-GBDT. Firstly, the Synthetic Minority Over-sampling Technique (SMOTE) was used to expand the minority samples. Secondly, the non-coding ratio method was used to construct multi-dimensional feature parameters, and the Light Gradient Boosting Machine (LightGBM) feature optimization strategy was introduced to screen the optimal feature subset. Finally, Northern Goshawk Optimization (NGO) algorithm was used to optimize the parameters of Gradient Boosting Decision Tree (GBDT), and then the transformer fault diagnosis was realized. The results show that the proposed method can reduce the misjudgment of minority samples. Compared with other integrated models, the proposed method has high fault identification accuracy, low misjudgment rate and stable performance.

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A literature review of fault diagnosis based on ensemble learning

Cited by 25 publications

References 127 publications

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Machine learning for monitoring hobbing tool health in CNC hobbing machine

A Diagnostic Curve for Online Fault Detection in AC Drives

Transformer fault diagnosis method based on SMOTE and NGO-GBDT

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