Build a Motor Defect Detector
Real-time monitoring of manufacturing equipment to prevent faults and anomalies is vital for any industrial process and a precursor to predictive maintenance. Use machine learning models to help detect and predict these faults in a motor ball bearing data set.
|Target Operating System||Ubuntu* 16.04 LTS|
|Time to Complete||30 minutes|
Several mathematical techniques in machine learning, starting with basic fast Fourier transform (FFT) to the more complex Gaussian mixture model, do not require training a neural network for anomaly detection. These techniques can be reused with minor modifications on different data streams and don't require a lot of known previously classified data (unlike neural networks). In fact, some of these methods can classify data in order to train deep neural networks.
Gain insight into the following solutions:
- Logistic regression
- K-Means clustering
- Industrial market IoT
Use the skills learned in this reference implementation to develop similar IoT solutions.
Learn to build and run an application with these capabilities: