Exploring PI Asset Framework Data in Spotfire

Introduction

Spotfire Vision to be Vertical Focused

Enabling vertical use cases in Energy, HTM and Manufacturing is one of Spotfire's main vision pillars. Historian systems can be found abundantly in these verticals and in other verticals as well. AVEVA PI Systems have a large footprint. Spotfire PI Custom data sources can connect to PI Asset Framework and PI Data Archives, pull data and update existing tables in Spotfire through a parameterized approach that enables the user to change the query parameters interactively. By the end of this article you will know how to access AF through Spotfire and what are the available options to pull and process the data.

Why PI Asset Framework

The PI Asset Framework provides a flexible approach to model physical or logical objects, and enables the review of assets and their associated data in the most appropriate way that fits the use case and allows the identification the components of a process, establish relationships between them, and organize them according to your business needs. For instance, suppose you have a pump that produces various data streams such as power consumption, vibration, fluid volume, impeller speed, oil temperature, and pressure, each based on different parameters. By combining various data streams, such as high energy consumption and low water flow, you can pinpoint a worn-out impeller as the cause of the problem. With the right data streams, PI Asset Framework can even provide its own analytics. PI Asset Framework will enable the end user to quickly pull a group of tags according to a predefined set of categories. Below an example of how AF helps us to breakdown an equipment (pump) to simple logical parts. Imagine querying PI Data Archive without leveraging  AF, SMEs will need to know the exact name of tags instead which can be a very time and effort consuming activity. Using PI Asset Framework can help creating modularized and reusable solutions that can be used in analytical pipelines without worrying about changing Tag Names, for instance you can apply a specific analysis pipeline to one element, as we will see in the How AF can help us to build OSI PI queries on the fly section.

PI Asset Framework industry use cases

Energy and Utilities: AF is used to manage and monitor power generation, transmission, and distribution assets, such as turbines, generators, transformers, and switchgear. It provides real-time visibility into asset performance and enables predictive maintenance by looking at several components at once, reducing downtime and improving overall asset efficiency.

Oil and Gas: AF is used to manage and monitor offshore and onshore assets such as drilling rigs, pipelines, refineries, and storage facilities. It provides visibility into equipment health, facilitates condition-based maintenance, and helps operators optimize asset performance, reducing costs and improving safety.

Manufacturing: AF is used to manage and monitor manufacturing assets, such as assembly lines, conveyors, robots, and packaging machines. It helps optimize production processes, reduce downtime, and improve product quality by providing real-time visibility into equipment performance and enabling predictive maintenance.

Chemicals: AF is used to manage and monitor chemical processing assets, such as reactors, distillation columns, and heat exchangers. It provides visibility into asset performance, helps optimize production processes, and facilitates condition-based maintenance, reducing downtime and improving safety.

Healthcare: AF is used to manage and monitor hospital assets, such as medical equipment, patient monitoring systems, and HVAC systems. It helps improve patient safety, optimize energy consumption, and reduce maintenance costs by providing real-time visibility into asset performance.

How AF can help us to build OSI PI queries on the fly

One way AF can help build OSI PI queries on the fly is by providing a standardized hierarchy of asset elements that can be used to organize data from different data sources. This hierarchy can be used to create dynamic queries that can be modified in real-time to accommodate changes in the underlying asset structure or data sources. In the example below, the AF was pulled to Spotfire using PI Asset Framework Details View custom datasource, and presented as an interactive visualization mod in Spotfire where users can pick and choose what they want to input to the PI Archive Data Function in a Element wise style instead of single PI Tags.

Making sense of PI Asset Framework data and PI Explorer

Let’s take a real life example of PI Asset Framework  hierarchy, assume we have a plant that has several facility sites (Facility#1, Facility#2..) each location has several pumps, each pump has a shaft, each shaft has two bearings, and we want to monitor the temperature of the front bearing. PI Asset Framework can describe physical entities or logical processes, and they can even be part of a process or describe a whole logical process.

Using Spotfire PI Connector to Access AF Data

In Spotfire, there are two ways to query AF Data:

Using GUI

• Start by clicking (+) in Spotfire ---> Other ---> OSISoft PI Asset Framework
• Enter your server details and connect.

• Move to the second tab and select the elements and attributes you want to involve in your query

Finally choose the Data Retrieval Params. 

The output table will look like this sample below:

Using Custom Data Function

• Using Spotfire OSIPI Custom Data Source provides more flexibility to pull PI Data interactively and in a more parametrized way. To access this data source, go to Tools ---> Create Asset Framework Details View
• Fill up the parameters as below, as seen below most of them were parametrized.

How does AF query output look like in Spotfire

As described above the data output from Asset Framework has around 31 columns. Some of these important columns that are needed in creating queries:

PI Tag: Specific PI Tag that is assigned to an element or property.

Path: Where is a specific PI Tag is stored

Element Template: Refers to the library template that this specific PI Tag was assigned to.

Element Parent Name: If the element is a sub from a higher element, parent element will be mentioned here.

Attribute Name: What is being measured in this tag.

Putting it all together, query PI Interactively

We saw that using PI Asset Framework to pull tags from PI Asset Framework Server without the need to know the tag name. Using the meta model or the logical hierarchy to pull the list of tags that we are interested in. Using Spotfire interactivity we can query PI Data Archive by using hierarchal visualizations of PI Asset Framework to send queries back to PI Data Archive. Below is an example of using Spotfire List Mod to visualize the PI Asset Framework and using that will form a query to the PI Data Archive, this data will be flattened and converted into a wide format to be used downstream in different time series analysis using Spotfire DSML Package.

Still interested to learn about OSIPI Applications in Spotfire? Checkout these resources:

- Accessing PI Data in Spotfire (Article)

- Accessing PI Data in Spotfire (Dr. Spotfire Session)