Integrate IBM Predictive Maintenance and Quality (PMQ) with ILS deviceWISE to onboard high-value asset data
Identify problem hot spots and their solutions
Asset-intensive industries, such as oil and gas, mining, and energy and utilities, use complex equipment, such as compressors, haul trucks, and turbines, in their day-to-day operation. Any unplanned downtime or major unforeseen failure of this equipment has a direct impact on production downtime, which affects the financial performance of the organization.
Potential component and equipment failure, plus machine health of in-service equipment needs to be monitored by identifying early signs of possible downtime. The goal is to maximize the uptime of the component/equipment.
The IBM Predictive Maintenance and Quality (PMQ) solution helps you monitor, analyze, and report on information that is gathered from high-value assets and recommend maintenance activities for them. With this integrated solution, you can:
- Predict the failure of a monitored asset in order to fix it and avoid costly downtime.
- Search stored maintenance logs to determine the best repair procedures and cycles.
- Identify the root causes of asset failure to take corrective actions.
The integration bus layer within PMQ helps to transform external events (received from monitored high-value assets) into the format that is required by the PMQ analytics solution's data model. One way to receive external low-level events, such as the discharge pressure of a compressor or the inlet temperature of compressor, is to use the ILS deviceWISE Machine to Machine (M2M) Application Platform. (For more information about ILS deviceWISE, see Related topics.) This platform helps:
- Connect assets to applications.
- Collect and process data from a variant of assets, in various locations.
- Integrate that data into existing enterprise IT systems or to a custom dashboard to drive better business decisions.
- Remotely access and manage assets.
This article explains how to onboard data from an OLE for Process Control (OPC) source, which stands for Object Linking and Embedding (OLE) for Process Control, and the step-by-step configuration to onboard data into PMQ to perform analytics on the asset data.
A typical oil and gas industry involves various rotating equipment, such as turbines, pumps, compressors, generators, and motors. Each gas turbine has an upstream rotating compressor, which generates pipeline data, such as compressor unit suction and discharge pressures, gas temperatures, unit flow, ambient temperature, and more. The data that is received from low-level source systems acts as a data store to perform analytics, like the health monitoring of turbines. The data is captured in the form of events from field-level systems in a real-time mode that uses a protocol and format that is called OLE for Process Control (OPC). OPC event sources are integrated by using an external adapter, ILS deviceWISE, which pushes the data into the Integration Bus layer within PMQ by using the WebSphere® MQ (IBM MQ) transport. After device events are received in a queue, the event-processing component transforms them into the PMQ event format that is required by the solution.
Tags are often used in the process industry and are normally assigned to a piece of information. A tag consists of a name by describing a single point of information, so a process system can consist of hundreds and even thousands of tags.
For demonstration of integration capabilities, this article uses MatrikonOPC Simulator, which provides equipment data connectivity by using these tags, which are an item database for a device. Simulated tags are OPC-timestamped with range and OPC quality. Based on the quality set, a tag can either be "good" or "bad."
In this scenario, each compressor carries about nine tags that indicate different measurements, as shown in Table 1.
Table 1. Compressor tags, with their units of measurement
|Name||Definition||English unit||Metric unit|
|CTIM||Compressor inlet temperature||Fahrenheit (°F)||Celsius (°C)|
|CTD||Compressor discharge temperature||Fahrenheit (°F)||Celsius (°C)|
|CPR||Compressor pressure ratio||None||None|
|AFPCS||Compressor inlet pressure transducers 96CS||in H2O||mm H2O|
|CPD||Compressor discharge press max select||psi||bar|
|AFQ||Compressor inlet air mass flow||lb/s||kg/s|
|CompEff_Mean||Mean compressor efficiency||%||%|
|AFPAP||Barometric pressure transducers 96AP||in||mm|
Figure 1 shows the integration of ILS deviceWISE with IBM Predictive Maintenance and Quality solution, a packaged, preconfigured cross-industry business analytics solution.
Figure 1. Architecture of the solution when you integrate ILS deviceWISE with PMQ
Technical summary of the solution workflow
- Integration with historians: Equipment tags and online analyzers are configured in DCS, SCADA, and historian systems. ILS deviceWISE is configured to read the values of all the online and offline tags in a real-time mode. Thus, ILS deviceWISE integrates with level 2 systems and historians to fetch sensor, alarm, monitoring, and diagnostic data that is injected from equipments. This integration results in continuous raw event data capture and a near real-time analysis of the captured data.
- Real-time data integration: Triggers are created in ILS deviceWISE workbench to put the tag data in the integration bus layer within PMQ by using a message queue. This data is received on the queue in the form of XML.
- Onboard operational data store: Event data that is received from historian systems is converted to the format expected by PMQ, which is eventually populated in the analytical data store. This onboarded data is aggregated data and includes key performance indicator (KPI) and profile information.
- Predictive model: IBM SPSS® executes pre-built analytical models, resulting in scores. KPIs are analyzed by the system on a continuous basis. In response to the scores and the current KPI values, SPSS generates recommendations by using the pre-configured business rules.
- Enterprise Asset Management (EAM) systems: The received recommendation can be used to initiate or modify a work order in EAM (Maximo) systems for maintenance of the compressor. This event also provides an automatic email alert in all such instances.
High-level configuration to integrate IBM PMQ with ILS deviceWISE
- Configure MatrikonOPC to simulate tag values for a compressor
- Configure ILS deviceWISE
- Configure IBM PMQ
- Test the solution
Configure MatrikonOPC to simulate tag values for a compressor
Create an alias group in the MatrikonOPC Simulation Server, with the required alias for each parameter, as shown in Figure 2. (For more information about MatrikonOPC, see Related topics.)
- Open the Matrikon OPC Server for Simulation.
- Right-click on Alias Configuration and select Insert Alias Group from the pop-up menu.
- Provide a suitable name for the alias group.
- In the Contents of alias group frame for the newly created group, right-click and select Insert New Alias from the pop-up menu.
- Provide a suitable name for the alias/parameter.
Figure 2. Alias group and its contents in Matrikon OPC
Configure ILS deviceWISE
- Create a device of type DA CLIENT in ILS deviceWISE and specify the
required OPC server URL. (The OPC server URL field differs per your
setup. It is not a generic URL to be used by all.)
Figure 3. Device of type DA Client, pointing to the correct OPC server
After the device is connected, it displays the items n the Variable tab as in Figure 4.
Figure 4. DA Client with a list of available alias groups and their aliases that are configured in Matrikon OPC
- Create a WebSphere MQ transport and provide the required queue
manager, queue, channel, and host name.
Figure 5. WebSphere MQ transport that is created in deviceWISE
- Create a transport map for each tag by selecting the previously
created IBM MQ transport. Repeat this step for each tag.
Figure 6. Transport map
Figure 7 provides a screen capture of the XML message that is generated.
Figure 7. XML message that is generated after the map is successfully created
- Create a project within the node, as shown in Figure 8.
- In the NEW NODE tree in deviceWISE, expand NEW NODE.
- Right-click on the projects item, and select New from the menu.
- Provide a new project name in the wizard.
A new project then appears in the Projects tab, as shown in Figure 8.
Figure 8. A new project that is created under Node section in deviceWISE
- Create a trigger for each tag within the project by using the Canvas
Figure 9. Canvas editor, depicting a new trigger for a tag
After the trigger definition is validated and saved, it appears within the project, along with all the other triggers, as shown in Figure 10.
Figure 10. Project with a list of all available triggers in deviceWISE workbench
Configure IBM PMQ
Master data is the type of resource that you want to manage, such as people, parts, assets, pieces of equipment, and processes. Master data is normally loaded by using one of the supplied connectors or the Flat File API. The connectors and the Flat File API use IBM Integration Bus flows to transform the data into the required form and to update the data in the IBM Predictive Maintenance and Quality database.
Explore master data and other concepts in the PMQ solution guide (see Related topics).
The following master data files must be loaded in PMQ data store to populate the master tables:
The resource master data sheet contains a list of all compressors and their attributes, as shown in Figure 11.
Figure 11. Sample master data sheet for a resource in IBM PMQ
Test the solution
Start the trigger that is created in ILS deviceWISE.
- Right-click on the trigger present in a project under 'Node' in deviceWISE.
- Select Start to start the trigger.
Figure 12. Starting trigger in ILS deviceWISE workbench
Starting the trigger sends a message into the queue that was defined in the transport section. The message is of the format defined in the transport map definition. Data that is pushed from ILS is received in XML in a WebSphere message queue.
You can browse for the XML message received from ILS.
Figure 13. List of messages that are received in WebSphere MQ
You can double-click any one of the XML messages to see a detailed view of the data present in the XML, as shown in Figure 14.
Figure 14. Detailed view of XML message
The event data that is received from historian systems is converted to the format expected by PMQ, which is eventually populated in the analytical data store. This onboarded data is aggregated data and includes KPI and profile information.
You can see a screen capture of the event observation table in PMQ data store, as shown in Figure 15.
Figure 15. Data onboarded in the PMQ data store
The IBM Predictive Maintenance and Quality solution helps you monitor, analyze, and report on information that is gathered from devices and other assets and recommend maintenance activities. PMQ uses ILS deviceWISE to integrate seamlessly with level 2 systems and historians to perform predictive and business analytics on the operational data. This analysis provides the hot spot identification of a problem and the corresponding resolution to avoid a forced outage.
- "Real-time data analytics using IBM Predictive Maintenance and Quality" (developerWorks, May 2014): Understand how to use IBM PMQ to onboard production data in real time and perform analytics on the data to predict production in near future.
- IBM Predictive Maintenance and Quality Information Center: Learn more about the solution in the IBM Predictive Maintenance and Quality Information Center.
- IBM Predictive Maintenance and Quality solution: Explore the IBM Predictive Maintenance and Quality solution, which helps you maximize asset productivity and operational performance.
- "Predictive Maintenance and Quality 1.0 Solution Guide" (IBM, 2013): Gain an understanding of how the IBM Predictive Maintenance and Quality solution works. Know what tasks are involved when you plan to implement IBM Predictive Maintenance and Quality. (The solution asset that is used in this article is based on PMQ 1.0.)
- "IBM Predictive Maintenance and Quality 2.0 Solution Guide" (IBM Redbooks, May 2014): Learn how Predictive Maintenance and Quality enables companies to identify when manufacturing assets need maintenance, not just according to the manufacturer's scheduled repair guide but also based on how the asset is used every day. This information helps to keep critical production lines running while also saving money because repairs are always, and only, performed when truly necessary.
- "Predict the future to keep your production line running" (IBM): View a demo on how IBM Predictive Maintenance and Quality helps spot problems before they happen so you can plan for, rather than react to asset failure.
- ILS deviceWISE: Learn more about ILS deviceWISE and how to seamlessly connect your assets with your enterprise systems and databases.
- MatrikonOPC: Learn more about MatrikonOPC and MatrikonOPC Simulation Server.