Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

Durbhaka, Gopi Krishna; Barani, S.; Mittal, Mamta; Saba, Tanzila; Rehman, Amjad; Goyal, Lalit Mohan

doi:10.32604/cmc.2020.013131

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…It is seen in Fig. 5 that the majority of the studies are considered bearing faults [ [71] , [72] , [73] , [74] , [75] ] followed by rotor faults [ [76] , [77] , [78] , [79] , [80] ], gear faults [ [81] , [82] , [83] , [84] , [85] ], shaft faults [ 21 , 46 , 71 , 72 , 86 ], stator [ 7 , 25 , 87 ], and other component faults [ 28 , [88] , [89] , [90] ]. The pie chart shown in Fig.…”

Section: Fault Diagnosis and Prognosis In Rotating Machinerymentioning

confidence: 99%

“…Although classification-based FP may give an idea about the performance of the proposed approach, it may misdirect one as the developed model receives an input that indicates a severity value close to the mean value of two severity classes introduced to the model. Regarding FD procedure, various studies generally focused on overall model accuracy [ 8 , 21 , 24 , 27 , 40 , 42 , 43 , 97 , 129 ], and a few considered confusion matrix, precision, and recall [ 85 , 101 , 107 ]. The gear faults considered in related studies are diverse since numerous faulty gear cases are examined.…”

Section: Fault Diagnosis and Prognosis In Rotating Machinerymentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for fault analysis in rotating machinery: A comprehensive review

Daş

Birant

2023

Heliyon

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Section: Fault Diagnosis and Prognosis In Rotating Machinerymentioning

confidence: 99%

Section: Fault Diagnosis and Prognosis In Rotating Machinerymentioning

confidence: 99%

Machine learning for fault analysis in rotating machinery: A comprehensive review

Daş

Birant

2023

Heliyon

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“…The data for wind turbine fault signals mainly comes from sensor monitoring, data acquisition systems, maintenance platforms and monitoring systems, and external data acquisition [25,26]. By installing sensors on various components and subsystems, such as vibration sensors, temperature sensors, and pressure sensors, the operating status of the wind turbine can be continuously monitored in real time.…”

Section: Data Sources and Acquisitionmentioning

confidence: 99%

“…These sensors collect and save the data to a database or storage device through the data acquisition system, providing a foundation for subsequent analysis and processing. Additionally, wind turbines are equipped with maintenance platforms and monitoring systems, which are connected to the sensors and data acquisition systems to obtain real-time data from various components and subsystems of the turbine [26]. This study collected data from an actual wind farm, where vibration sensors were installed on the wind turbine to collect continuous vibration time-domain signals.…”

Section: Data Sources and Acquisitionmentioning

confidence: 99%

Multidimensional clustering based fault diagnosis method for wind turbine transmission chain

Zhang,

Ruan,

Huang

et al. 2023

Meas. Sci. Technol.

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With the rapid development of wind power generation, ensuring the reliability and fault diagnosis of wind turbine transmission chains has become a focal point. Traditional clustering methods that address the fault diagnosis problem in wind turbine transmission chains suffer from low fault discrimination and accuracy, as they rely on a single indicator. This paper proposes a two-dimensional clustering model that utilizes multiple indicators to address the issue of distinguishing similar faults within the wind turbine transmission chain from various perspectives and dimensions. The proposed approach involves collecting vibration signals from the transmission chain of wind turbines using sensors and using the root mean square and kurtosis of the fault signal as clustering features. A multidimensional polar coordinate clustering model is established, with the signal kurtosis representing the polar angle and the root mean square representing the polar radius. The clustering of fault signals within wind turbine transmission chains is achieved by optimizing the clustering boundaries by establishing a clustering boundary optimization equation. The results of the developed clustering model are evaluated using external and internal evaluation methods. Experimental results demonstrate that this method exhibits high accuracy and low computational complexity in diagnosing faults within wind turbine transmission chains. In comparison with other clustering methods, the proposed method outperforms them according to the experimental results. Hence, this study presents a novel approach for health monitoring and fault diagnosis of the transmission chain of wind turbines, which has significant implications for improving the reliability of wind turbine operation and reducing maintenance costs.

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“…Gearboxes and rotating machinery are crucial across many industries for their adaptability to torque and speed requirements [1]. In wind turbine systems, gearboxes require regular maintenance to ensure operational safety and reliability.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques

Shukla,

Holderbaum,

Theodoridis

et al. 2024

Machines

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Efficient gearbox fault diagnosis is crucial for the cost-effective maintenance and reliable operation of rotating machinery. Despite extensive research, effective fault diagnosis remains challenging due to the multitude of features available for classification. Traditional feature selection methods often fail to achieve optimal performance in fault classification tasks. This study introduces diverse ranking methods for selecting the relevant features and utilizes data segmentation techniques such as sliding, windowing, and bootstrapping to strengthen predictive model performance and scalability. A comparative analysis of these methods was conducted to identify the potential causes and future solutions. An evaluation of the impact of enhanced feature engineering and data segmentation on predictive maintenance in gearboxes revealed promising outcomes, with decision trees, SVM, and KNN models outperforming others. Additionally, within a fully connected network, windowing emerged as a more robust and efficient segmentation method compared to bootstrapping. Further research is necessary to assess the performance of these techniques across diverse datasets and applications, offering comprehensive insights for future studies in fault diagnosis and predictive maintenance.

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Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

Cited by 20 publications

References 17 publications

Machine learning for fault analysis in rotating machinery: A comprehensive review

Machine learning for fault analysis in rotating machinery: A comprehensive review

Multidimensional clustering based fault diagnosis method for wind turbine transmission chain

Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques

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