Fault diagnosis in spur gears based on genetic algorithm and random forest

By M. Cerrada, G. Zurita, Diego Cabrera, Réne-Vinicio Sánchez, M. Artés, Chuan Li. 2016

350 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/73cf50dea7958ef8d8b55657d2c559a7659d72ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Changhong Yan
5. Chen Lu
6. Shibin Wang
7. Hongyan Tao
8. Yong Guan

In Mechanical Engineering

1. Chen Lu
2. Shibin Wang
3. Hongyan Tao
4. Yong Guan
5. Ran Zhang
6. Bo Zhou
7. Chuang Sun
8. Lifeng Wu

In Mechanics of Materials

1. Xuefeng Chen
2. Ruqiang Yan
3. Chuang Sun
4. Hongyan Tao
5. Yong Guan
6. Ran Zhang
7. Lifeng Wu

Citations in the corpus

• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2019, positive): “IFD based on traditional machine learning methods [1], including random forest [2] and support vector machine [3], has been widely applied in research and industry scenarios.”
• Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing (2018, methodology): “proposed fault diagnosis in spur gears based on genetic algorithm and Random Forest [49].”
• Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions (2017, methodology): “[8] proposed a fault diagnosis model based on genetic algorithm and random forest.”
• Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification (2017, background): “can provide good support for fault feature extraction, and with the development of machine learning the classifiers such as support vector machine (SVM) [7,8], random forest (RF) [9] and probabilistic neural network (PNN) [10] have well development for accurate fault pattern identification.”
• Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, methodology): “With regard to the method for fault classification, some artificial intelligence methods are adopted [15], such as artificial neural network (ANN), support vector machine (SVM) [13], [16], [17], k-nearest neighbor [18], fuzzy inference system [11], and random forest [19].”
• Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data (2019, neutral): “[4] proposed a”