The following is from the lab notes for the hands-on lab "Fit for Purpose - Layers of Analytics using the PI System: AF, MATLAB, and Machine Learning" at PI World 2018, San Francisco, CA
Lab VM is available via OSIsoft Learning
Lab Manual is attached - scroll to the end of this page.
For an updated version of this Lab using open source tools such as R/Python see Operationalizing analytics - simple and advanced (data-science) models with the PI System
Part 1 Introduction
Part 2 Alky feed dryer – process analytics - descriptive and diagnostic (Exercise 1)
Part 3 Alky feed dryer – process analytics - diagnostic/predictive/prescriptive (Exercise 1 continued)
Part 4 Motor/Pump – maintenance analytics – usage based, condition based and predictive (Exercise 2)
Layers of analytics can be viewed through many lenses. Frequently, it refers to the levels of complexity and the kinds of computations required to transform “raw data” to “actionable information/insight.” It is often categorized into:
- descriptive analytics - what happened
- diagnostic analytics - why did it happen
- predictive analytics - what can/will happen
- prescriptive analytics - what should I do, i.e. prescribing a course of action based on an understanding of historical data (what happened and why) and future events (what might happen)
The purpose of the analytics i.e. whether it is for descriptive or diagnostic or predictive or prescriptive will influence the “raw data” calculations and transforms. The following graph shows “value vs. difficulty” as you traverse the layers.
Layers of analytics can also be viewed through a “scope of a business initiative” lens – for example, in asset maintenance and reliability, the layers are:
- UbM – Usage-based Maintenance - AF
- CbM – Condition-based Maintenance - AF
- PdM – Predictive Maintenance - AF plus third party libraries
Layers of analytics can also be categorized by where the analytics is done, such as:
- Edge analytics
- Server based analytics
- Cloud-based analytics
Analytics at the edge include those done immediately with the collected data. It lessens network load by reducing the amount of data forwarded to a server - for example, Fast-Fourier Transform (FFT) on vibration time wave-forms to extract frequency spectrums. Or, when an action is to be immediately taken based on the collected data without waiting for a round-trip to a remote analytics server.
In the hands-on portion of this Lab:
- Exercise 1 uses an oil refinery process unit operation (Alky feed dryer) to walk-through the layers i.e. descriptive, diagnostic, predictive and prescriptive
- Exercise 2 uses a maintenance/reliability scenario (pump/motor assembly) to illustrate the layers i.e. UbM, CbM, and PdM
Items not included in the detailed hands-on portion will be covered as discussion topics during the Lab.