The ThingSpeak team has integrated the Predictive Maintenance Toolbox for MATLAB into the IoT Analytics features of ThingSpeak. The Predictive Maintenance Toolbox provides tools for labeling data, designing condition indicators, and estimating the remaining useful life (RUL) of a machine. You can analyze and label machine data imported from local files, cloud storage, and distributed file systems. You can also label simulated failure data generated from Simulink models.

Here is a quick list of features of the Predictive Maintenance Toolbox for MATLAB:

• Survival, similarity, and time-series models for remaining useful life (RUL) estimation
• Time, frequency, and time-frequency domain feature extraction methods for designing condition indicators
• Organizing sensor data imported from local files, Amazon S3™, Windows Azure^® Blob Storage, and Hadoop^®Distributed File System
• Organizing simulated machine data from Simulink^® models
• Examples of developing predictive maintenance algorithms for motors, gearboxes, batteries, and other machines

The Predictive Maintenance Toolbox is available on ThingSpeak to users that have a license to the toolbox. Just sign into ThingSpeak using your MathWorks Account and you will have access to the features of the Predictive Maintenance Toolbox with the MATLAB Analytics app. If you have any questions about the Predictive Maintenance Toolbox, contact Aditya Baru at MathWorks.