Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines

@article{Ahmad2020AutoencoderbasedCM,
  title={Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines},
  author={Sabtain Ahmad and Kevin Styp-Rekowski and Sasho Nedelkoski and Odej Kao},
  journal={2020 IEEE International Conference on Big Data (Big Data)},
  year={2020},
  pages={4093-4102}
}
Rotating machines like engines, pumps, or turbines are ubiquitous in modern day societies. Their mechanical parts such as electrical engines, rotors, or bearings are the major components and any failure in them may result in their total shutdown. Anomaly detection in such critical systems is very important to monitor the system’s health. As the requirement to obtain a dataset from rotating machines where all possible faults are explicitly labeled is difficult to satisfy, we propose a method… 
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References

SHOWING 1-10 OF 24 REFERENCES
Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning
TLDR
A nonlinear projection is applied to achieve the compressed acquisition, which not only reduces the amount of measured data that contained all the information of faults but also realizes the automatic feature extraction in transform domain.
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
TLDR
This work proposes a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct 'normal' time-series behavior, and thereafter uses reconstruction error to detect anomalies.
Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis
TLDR
The method is able to detect incipient faults and diagnose the locations of faults under masking noise, and provides a health index that tracks the degradation of faults without missing intermittent faults.
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
TLDR
A fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body is proposed.
Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals
TLDR
This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals and demonstrates that the proposed feature extraction scheme has an advantage over other similar approaches.
Electric Power System Anomaly Detection Using Neural Networks
TLDR
Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology and unexpected power demand trend.
A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models
TLDR
The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs.
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