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Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

机译：基于时域和频域特征的机器学习模型对旋转机器故障诊断的比较

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Natalia Espinoza Sepulveda; Jyoti Sinha;
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年度 2018
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1. Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines [J] . Natalia Espinoza Sepulveda, Jyoti Sinha MATEC Web of Conferences . 2018,第1期

机译：基于时域和频域特征的旋转机器故障诊断机器学习模型比较
2. Fault diagnosis of rotating machinery with ensemble kernel extreme learning machine based on fused multi-domain features [J] . ISA Transactions . 2020,第期

机译：基于熔融多域特征的集合内核极端学习机旋转机械故障诊断
3. Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines [J] . Natalia Espinoza Sepulveda, Jyoti Sinha Machines . 2020,第4期

机译：基于振动的机器学习模型中的参数优化，用于旋转机器准确可靠的故障诊断
4. Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines [C] . Natalia Espinoza Sepulveda, Jyoti Sinha International Conference on Vibration Engineering and Technology of Machinery . 2018

机译：基于时域和频域特征的机器学习模型对旋转机器故障诊断的比较
5. Fault detection and diagnosis in machining processes and rotating machinery using fuzzy approach and hidden Markov model [D] . Boutros, Tony 2006

机译：基于模糊方法和隐马尔可夫模型的机械加工和旋转机械故障检测与诊断
6. Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features [O] . Yuefan Xu, Sen Zhang, Zhengtao Cao, 2021

机译：具有混合时域和小波时频特征的心跳分类的极端学习机
7. Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks [O] . Kenisuomo C. Luwei, Akilu Yunusa-Kaltungo, Yusuf A. Sha’aban 2018

机译：使用频域数据融合和人工神经网络对旋转机器故障进行分类的集成故障检测框架

Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

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