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Monitor operational data and create dashboards and alerts

Summary

In this code pattern, we will monitor simulated oil-well-drilling equipment data in Maximo Asset Monitor by integrating an OSIsoft PI System data historian with it using IBM App Connect Enterprise.

Description

In instrumented industrial and manufacturing processes, IoT devices such as temperature, pressure, or flow sensors and actuators are a key source of intelligence and automation. In many scenarios, programmable logic controller (PLC) and remote terminal unit (RTU) systems typically have direct control over these IoT devices and are able to monitor and control their state.

Supervisory control and data acquisition (SCADA) is a device monitoring and controlling framework that is comprised of instrumented equipment and process, PLC systems, higher-level supervisory control computers, and often a data historian. The data historian is typically a database that captures site and equipment data along with instrumented time series sensor data. The data elements or attributes that are captured are tags or points that correspond to sensors that are often associated with asset site and location.

The PI System developed by OSIsoft is one such data historian that can capture, store, and manage, real-time time-series sensor data and plant information data from PLC and SCADA systems. A typical PI System configuration consists of:

  • Systems that are running the PI Interface to collect data
  • The PI Data Archive server that is used for efficient storage and retrieval of data
  • The PI Asset Framework architecture that provides a human consumable mapping of data points to assets or an asset hierarchy
  • Analytic and visualization tools.

Site operators and maintenance professionals need visibility into equipment tag point data and into the operations and health of their sites and equipment. They need customizable dashboards that can alert them when anomalies are detected in their operations.

IBM Maximo Asset Monitor is a solution that is powered by AI and provides remote asset monitoring capabilities through monitoring dashboards. With Maximo Asset Monitor you can connect devices, collect metrics and display them on dashboards, transform / cleanse data and detect anomalies. The advanced analytics and AI powered anomaly detection capabilities can be leveraged to detect issues in operational point data that was captured the historian. The tight integration with Maximo Asset Management drives creation of work orders for instrument equipment records maintained in Maximo.

For the purpose of this code pattern we have used a simple OSIsoft PI System configuration and used an asset-based PI example kit to generate simulated data. Data from the PI data historian is connected to Maximo Asset Monitor through a configuration flow in IBM App Connect Enterprise that maps data from the PI data historian to tables in Maximo Asset Monitor.

When you have completed this code pattern, you will understand how to:

  • Monitor operational data in a data historian in Maximo Asset Monitor and create dashboards and alerts.
  • Query an OSIsoft PI data historian to fetch operational time series point data.
  • Integrate operational data from the PI data historian with Maximo Asset Monitor using IBM App Connect Enterprise.

Flow

flow

  1. The OSI Soft Pi System collects operational, time-series sensor data as points via a PI Interface that gets persisted in a PI Archive.
  2. A scheduled cron Node.js script fetches new point data from the archive or data historian using PI Web APIs, filters the data, and formats the data.
  3. The Node.js script sends Points JSON to IBM App Connect via an HTTP POST.
  4. IBM App Connect uses message flow in a bar file to map Points time series data into the entity type and dimensions tables in Maximo Asset Monitor (IoT Platform) to update points data and points metadata.
  5. The operational points data from multiple sites can be viewed in Maximo Asset Monitor summary dashboards.

Instructions

Find the detailed steps for this pattern in the readme file.