An ensemble of convolution-based methods for fault detection using
  vibration signals
Xian Yeow Lee Aman Kumar Lasitha Vidyaratne Aniruddha Rajendra Rao Ahmed Farahat Chetan Gupta
This paper focuses on solving a fault detection problem using multivariate
time series of vibration signals collected from planetary gearboxes in a test
rig. Various traditional machine learning and deep learning methods have been
proposed for multivariate time-series classification, including distance-based,
functional data-oriented, feature-driven, and convolution kernel-based methods.
Recent studies have shown using convolution kernel-based methods like ROCKET,
and 1D convolutional neural networks with ResNet and FCN, have robust
performance for multivariate time-series data classification. We propose an
ensemble of three convolution kernel-based methods and show its efficacy on
this fault detection problem by outperforming other approaches and achieving an
accuracy of more than 98.8\%.