Integration with Other Systems

Asset data can be integrated into CMMS and other enterprise applications in a number of ways. There are two basic scenarios: push or pull. Push sends data to people and systems using any number of delivery channels, for example, by sending emails, texts, or invoking a web service. The pull model is enabled by EIDI data access technologies where enterprise applications can acquire data from the EIDI using any number of technologies, including the most popular open standards methods such as open database connectivity (ODBC) provided via the EIDI SQL Explorer. (See Chapter 10, which focuses on the EIDI integrator as a tool that can be used to simplify


Example of EIDI real-time notifications and work orders.

the integration with business systems and advanced data analytics tools that are available in the market.) Pierce (2018) provides several scenarios for CMMS integration.

Figure 6.14 shows how the EIDI interacts with a maintenance application. The generated triggers are compared with the maintenance plan logic to create a work order for repair of the equipment. This work order triggers a process change management system to assure that all process safety guidelines are properly followed. As Proclndustries connect to their data using the digital plant template, the operating and maintenance teams are brought together. They all share the same information with the capability of defining the proper triggers to generate alerts for further analysis by the maintenance and engineering teams. As such, they will be able to make an informed recommendation as to when to execute a work order.

Paul summarized: "The team will be able to use data mining tools to do a more detailed investigation. So whether you call it 'big data' or simply connecting people, I believe that bringing data from silos connects people and encourages them to use their knowledge for process improvements and innovation."

Implications of Improving Asset Availability

Monica shares that from a production perspective, avoiding costly repairs and downtimes will improve quality management and align with the production schedule. She adds that each production team has to provide the best internal product on time for the next operation to have the least possible disturbance. In the past, Proclndustries handled large inventories of products that needed reprocessing. Monica points out that these events will be minimized as well.

Reducing waste product (also known as off product) inventories increases potential plant throughput and overall plant production. By abstracting the process unit, time constants, and inventories for all processing areas, users can visualize all related information using a Gantt chart to instantly check daily schedule performance.

Peter added, "Big data is a relatively new technology for us. Now we can capture and integrate real-time data and events in our business."

"Health, safety and environmental issues are always going to be the first priority in the process industries," says Raj Singh, the process safety manager. "Critical situations always arise when we have downtimes or unexpected situations. Using CBM will help us avoid safety and environmental issues. Although we can monitor minor losses, which are very hard to solve, we still have a long way to go. Now that we have the measurements of all the losses clearly defined, just having these metrics will change our corporate culture."

Trouble time was a hidden loss that was not possible to detect without having a way to trigger events and be able to automatically aggregate production consumables data. Learning from the collected data and operational events will allow for better planning and improved production campaigns. Monica is very proud of what they have achieved in such a short time: "Capturing these hidden losses as key metrics opens a new way to improve our processes. This is really the integration of operations and business."

In Chapter 2, the strategies for continuous improvement enabled by a data infrastructure system were presented. In this specific case, the operational and equipment data is processed by real-time analytics to generate events and the alerts for finding the root cause. Once the root cause is known, the maintenance workflow can begin to take the best action to keep the plant running, or to schedule repairs aligned with the plant's production schedule.

Shared analysis is readily available, offering a view of all information related to an exception raised by the CBM system. Web-based portal technologies are best suited for establishing collaborative environments because they allow for easy integration of real-time, relational, and unstructured (documents) information. Portal technology eliminates the need for client software licenses or training when providing views of information for decision-making.

The benefits of a well-implemented CBM program extend outside of protecting corporate investment in asset portfolios. When real-time asset data provide visibility into asset condition, maintenance schedules and costs can be planned and spent correctly. (Will that compressor make it until the next outage?) Common failures or issues that occur across units or within fleets can be identified. (Why is maintenance more expensive on a specific vendor's equipment compared to other vendors?) Just by creating visibility into asset condition indicators, data can help prevent catastrophic failures. It typically only takes a few big saves to pay for a complete CBM implementation.

From what Peter and Monica have explained, Paul knows that real-time, online health monitoring for pumps and other rotating equipment can be constructed using off-the-shelf EIDI components. The key business strategy is to eliminate unscheduled downtimes to zero. He summarizes the next steps for the EIDIs:

  • 1. The interfaces collect the required data from the pump motors, bearings, flowmeters, and so forth. These typically come from PLC, DCS, or supervisory control and data acquisition (SCADA) interfaces and IIoT devices.
  • 2. The server routes data to all analysis routines and visualization clients in real time. All aggregated data, both sensor and calculated data, are kept in the EIDI historical data archive.
  • 3. The asset object model organizes pump data according to asset attribute, elements, and topology to create a standardized method of assessing pump health.
  • 4. The asset analytic is used to calculate real-time condition indicator values.
  • 5. The statistical quality control (SQC) system compares the computed values against statistical norms.
  • 6. Contextualized extracted EIDI data enables offline tools, such as advanced predictive analytics and AI. These tools do not require real time execution.
  • 7. The EIDI visualization tools (OSIsoft PI Vision, PI DataLink, and other client tools) display real-time and historical information in visual and graphical formats, enabling role-based dashboards, and deep content displays.
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