GE Predix's machine learning (anomaly detection) capabilities Background:
I am investigating time-series anomaly detection for industrial machine data, and have stumbled upon GE Predix. It seems like a promising tool, however, I am not familiar with their approaches for anomaly detection. 

Question:
Does anyone have experience with GE Predix, in the anomaly detection space? How would one rate its anomaly detection capability? What technical approaches (algorithms) does it use? Any other literature about GE Predix that is openly available that I have missed (besides References below)? Are there any available benchmarks (e.g., versus Splunk + Prelert)? Who are its competitors?

References:
1) https://www.predix.com/sites/default/files/predixplatformbrief-march_2016.pdf
2) https://www.predix.com/sites/default/files/GE-Software-Modernizing-Machine-to-Machine-Interactions.pdf
(full disclosure, I do work for GE)

GE itself has been using their GE Predix Anomaly detection algorithms to establish a relationship between anomalies &amp; machine failure for their factories, in an effort to establish a preventive maintenance framework. The algorithms have been heavily tested to catch any point anomalies from a machine data.  Go ahead and test it out for your datasets: The algorithms are available for registered developers (in catalog) to use in their applications.

Technical approaches (algorithms): Univariate/Multi-variate anomaly detection algorithms.  It detect Shift anomalies , Unexpected trends in physical system behaviors, Anomalous patterns &amp; detect changes in relationship among different variables. It also detects data with very high dimensions (>100). Mann-Kendall, Mann-Whitney, Kernel density estimate etc are some of the key algorithms used.