Decent fault classification of VFD fed induction motor using random forest algorithm

Panigrahy, Parth Sarathi; Santra, Deepjyoti; Chattopadhyay, Paramita

doi:10.1017/s0890060420000311

Cited by 6 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are a total of nine original vibration signals, of which six can be seen in Figure 3. Following sample processing, the original gearbox vibration signals at nine states can be labeled as F1~F9, and after obtaining the samples, the RF [6][7][8][9][10] model training is carried out, and the preliminary fault type recognition results are shown in Fig. 4.…”

Section: Gearbox Vibration Data Analysismentioning

confidence: 99%

Gearbox State Identification Method Based on Vibration Signal Variational Modal Decomposition and Random Forest

Liu,

Zhang,

Ding

2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

A recognition method based on variational modal decomposition and random forest is proposed for fault diagnosis of gearbox vibration signals. Firstly, a vibration signal enhancement method based on VMD and kurtosis - permutation entropy reconstruction criterion is proposed for the problem of vibration signal noise interference, and a fault feature set construction method is proposed to help extract key fault information effectively; furthermore, by carrying out the simulation experiments such as missing tooth, gear root crack, spalling, chipping tip, and so on, we obtain the fault vibration signals and the feature set, and construct a random forest-based fault type recognition model based on random forest. The results show that the recognition accuracy of the proposed method reaches more than 90% in the test set, which can provide an effective way for the recognition of gearbox faults based on vibration monitoring.

show abstract

Section: Gearbox Vibration Data Analysismentioning

confidence: 99%

Gearbox State Identification Method Based on Vibration Signal Variational Modal Decomposition and Random Forest

Liu,

Zhang,

Ding

2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…Zhu et al [22] proposed a corner extraction algorithm, which realized corner detection. Panigrahy et al [23] obtained a model that can accurately match complex scenes through a large amount of data. By introducing DenseNet, the number of parameters is reduced and the training complexity of target detection network is optimized.…”

Section: Related Workmentioning

confidence: 99%

Financial Data Center Configuration Management System Based on Random Forest Algorithm and Few-Shot Learning

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

To form a unified configuration and information management platform, FCCMS (financial center configuration management system) will integrate and sort information based on various configuration data and relationships as well as integrate processes and permissions. However, the most serious issue that data centers are currently facing is how to effectively manage these infrastructures. For various infrastructures, the data center currently uses a decentralized operation and maintenance management model. When an infrastructure fails due to inexperienced configuration management, this mode is not conducive to quickly locating and resolving the problem. A detection method of RFCO (random forest algorithm based on clustering optimization) is proposed, and an appropriate tree is selected from RF to integrate, so as to achieve the best effect. In this paper, the target matching algorithm based on FSL (few-shot learning) is deeply studied, and the target detection model is applied to the target matching and positioning task by using the ML method. The performance of the algorithm is tested by experiments on relevant datasets to verify the effectiveness of the algorithm in various scenarios.

show abstract

“…Compared to the above methods, the random forest (RF) model is capable of handling data sets that contain redundant features and have a shorter training time. In addition, RF can quickly predict sample results, has high practicality and good real-time performance, and is very suitable for fault diagnosis and classification of complex systems [ 32 , 33 ]. Furthermore, RF is convenient for implementation in IoT cloud platforms.…”

Section: Introductionmentioning

confidence: 99%

Remote Fault Diagnosis for the Powertrain System of Fuel Cell Vehicles Based on Random Forest Optimized with a Genetic Algorithm

Quan,

Zhang,

Feng

2024

Sensors

View full text Add to dashboard Cite

To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eight typical malfunctions of its powertrain system was incorporated. Firstly, the information on the powertrain system was obtained through a 5G-based monitoring terminal, and the Alibaba Cloud IoT platform was utilized for data storage and remote monitoring. Secondly, a fault diagnosis model based on the RF algorithm was constructed for fault classification; its parameters were optimized with a genetic algorithm (GA), and it was applied on the Alibaba Cloud PAI platform. Finally, the performance of the proposed RF fault diagnosis model was evaluated by comparing it with three other classification models: random search conditioning, grid search conditioning, and Bayesian optimization. Results show that the model accuracy, F1 score, and kappa value of the optimized RF fault classification model are higher than the other three. The model achieves an F1 value of 97.77% in identifying multiple typical faults of the powertrain system, as validated by vehicle malfunction data. The method demonstrates the feasibility of remote monitoring and fault diagnosis for the powertrain system of fuel cell vehicles.

show abstract

Decent fault classification of VFD fed induction motor using random forest algorithm

Cited by 6 publications

References 29 publications

Gearbox State Identification Method Based on Vibration Signal Variational Modal Decomposition and Random Forest

Gearbox State Identification Method Based on Vibration Signal Variational Modal Decomposition and Random Forest

Financial Data Center Configuration Management System Based on Random Forest Algorithm and Few-Shot Learning

Remote Fault Diagnosis for the Powertrain System of Fuel Cell Vehicles Based on Random Forest Optimized with a Genetic Algorithm

Contact Info

Product

Resources

About