Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

By Turker Ince, Serkan Kiranyaz, Levent Tolga Eren, Murat Askar, Moncef Gabbouj. IEEE transactions on industrial electronics (1982. Print), 2016

1,154 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a0cddfbea0e6bc230ef297cf6358705dc560fdb7

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

In Artificial Intelligence

1. Long Wen
2. Liang Gao
3. Xinyu Li
4. Jun Yan
5. Mohammed Hussein
6. Daniel J. Inman
7. Jianqiao Sun
8. Muhammad Hussain

In Control and Systems Engineering

1. Xiang Li
2. Qian Ding
3. Liang Gao
4. Xinyu Li
5. Long Wen
6. Ruqiang Yan
7. Joseph Homer Saleh
8. Zhaoyi Xu

Citations in the corpus

• Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning (2017, methodology): “ches for feature extraction tasks such as image classification (Krizhevsky et al, 2012), natural language processing (Kim, 2016), recommending systems (Van den Oord, et al, 2013) and fault detection (Ince et al, 2016). CNN learns how to extract and recognize characteristics of the target task by com”
• A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion (2020, background): “CNN is a feed-forward neural network which primarily simulates the operation of the human brain visual system [19].”
• An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach (2018, methodology): “Recently, 1D-CNN has been successfully used for text understanding, music generation, and other time series data (Cui et al., 2016; Ince et al., 2016; LeCun et al., 1998; X. Zhang et al., 2015).”
• A CNN-RNN Framework for Crop Yield Prediction (2019, methodology): “Similar convolutional models have been widely used in various application domains and found to be effective in improving prediction accuracy (Ince et al., 2016; Borovykh et al., 2017; Kiranyaz et al., 2019).”
• A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals (2017, methodology): “Therefore, some studies proposed that the classifier should have the ability to classify the data from the raw signal directly, without feature extraction or manual selection [24,27].”
• Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, methodology): “Real-time Motor machine failure identification and early fault diagnosis [124]”
• Multi-Layer domain adaptation method for rolling bearing fault diagnosis (2019, background): “Deep learning methods have gained great interests and achieved significant results in machinery fault diagnosis researches [21–27].”
• Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, background): “developed on raw current signals for motor bearing fault detection [18], where two major blocks (i.”
• Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks (2018, methodology): “[18] use 1D CNNs for anomaly detection in a study aimed at detecting faulty motor bearings based on univariant motor current data.”
• 1-D Convolutional Neural Networks for Signal Processing Applications (2019, methodology): “They have achieved the state-of-the-art performance levels in several signal processing applications such as early arrhythmia detection in electrocardiogram (ECG) beats [15]-[17], on-site structural health monitoring [14], [18]-[22], motor fault detection [23] and real-time monitoring of high-power ”