Build a Motor Defect Detector Solution
This solution covers the basic implementation of fast Fourier transforms (FFT), logistic regression, K-Means clustering, and Gaussian Mixture Model (GMM). It also shows how FFT is helpful in feature engineering of the vibrational data of a machine.
Detects irregularities in machine vibration
Local processing using the sklearn library
Sends data to the cloud for aggregation and viewing
There are several methods that do not require training of a neural network to be able to detect failures, starting with the most basic fast Fourier transforms (FFT) to the most complex Gaussian Mixture Model (GMM). These have the advantage of being able to be reused with minor modifications on different data streams. They also do not require a lot of known previously classified data (unlike neural nets). In fact, some of these methods can be used to classify data in order to train deep neural networks (DNN).