Predictive maintenance involves predicting when a component will fail or require service, before that failure happens. It goes well beyond traditional approaches, such as reactive maintenance (replace components after they fail) or scheduled maintenance (replace components on a pre-determined schedule). Instead a predictive maintenance solution uses historical data to predict when a failure is likely to occur, so you can take action and avoid costly downtime.
IBM has launched an industry leading Predictive Maintenance and Quality solution, built on IBM's extensive product portfolio and extensive real world predictive maintenance experience. But what products are involved? And how does it work?
Here are the six steps that make up the IBM Predictive Maintenance and Quality solution.
1. Loading master data
Before you can start making predictions you'll need to load some master data into the analytic data store (the DB2 database central to the Predictive Maintenance and Quality solution). Master data includes a list of all the devices to be monitored by the solution.
Let's use a scenario to illustrate this. Consider a manufacturing firm that's having difficulty maintaining the power transformer that feeds electricity to their production lines. If they wanted to predictively maintain the power transformer they would need to provide master data about the transformer device itself.
You can edit master data using IBM Master Data Management, or simply use a spreadsheet application to edit the data directly. Either way, you'll export .csv files which are loaded into the analytic data store.
2. Loading and storing events
The second thing the analytic data store needs is to receive events from monitored devices. In the power transformer scenario, the device being monitored (the transformer) might provide events with several different observations such as temperature and current load measurements.
These events are received either in real time or batch. The Predictive Maintenance and Quality solution requires these events to be stored in a particular format. To achieve that, an IBM WebSphere Message Broker message flow transforms the message format sent by each device into a standard event format.
With the events delivered, they need to be stored in the analytic data store. The event processing flow records them into the DB2 database.
3. Performing aggregation
Events are aggregated into key performance indicators (KPIs) and profiles using measurement types and profile variables. A measurement type defines how to interpret a particular device reading (so a reading of "107" is understood to be a temperature reading and not something else). Profile variables designate a specific profile calculation that should be performed on the incoming data (for example to calculate the average temperature of the transformer and its current load).
Scoring is where the magic starts to happen. Predictive models are created in IBM SPSS Modeler. These predictive models use historical data to determine the probability of certain future outcomes. For example, a model could be created based on historical data regarding transformer temperature, current load, and occurrences of failure. The score that is returned can be thought of as an estimate of the likelihood that the transformer will fail within a designated period of time, based on the most recent readings.
5. Decision management
With scores calculated, it's time to start making decisions. With SPSS Decision Management, rules can be authored, tested, optimized, and deployed. For example the recommended action that results from the predictive score may be to perform a detailed on-site inspection to look for early signs of trouble. When the predictive score shows a particularly high probability of failure, the action may be to transfer the load to another device and shut down the transformer for a component-level inspection and possible repair.
6. Dashboards and delivering recommendations
The communication of recommended actions (such as to perform an on-site inspection) can be accomplished by the creation of work orders in IBM Maximo. The accumulated KPIs and current profile values (such as the average temperature of the transformer) can be viewed in IBM Cognos Business Intelligence reports.
To find out more about the IBM Predictive Maintenance and Quality solution and how it works, see the IBM Redpaper IBM Predictive Maintenance and Quality Technical Overview.
Martin Keen is an IBM Redbooks Project Leader. He works with technical experts to create books, guides, blogs, and videos. Follow Martin on Twitter at @MartinRTP.
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