4. Asset-Class Specialty Tools. These tools and interfaces for specific roles and work groups often use field data that don’t belong in the historian or that require pre-processing before going to the historian. Prime examples are instrument diagnostic data and vibration waveforms from rotating equipment.
5. Information Distribution. To be of value, actionable information must reach the right person. Data destinations include: automated work orders to a computerized maintenance management system (CMMS) resulting from new analytics in the historian; traditional human-machine interfaces and mobile apps; and remote connections to the field using Wi-Fi.
Developing A General Infrastructure
For decades, the Purdue Model has been the standard method for describing information flow in automation systems. In the Purdue Model, information always flows up, from the process sensors to the I/O system, through various networks, to the control system. From there, it continues to flow up to process historians, manufacturing-execution-system and CMMS software, and then to corporate databases.
Many plants make the mistake of trying to figure out how IIoT aligns with the Purdue Model before understanding the needs of their organization and the value of IIoT options. This causes three key problems. First, some engineers unfamiliar with the Purdue Model may choose not to take part. Second, OT and IT groups, citing assumed security issues, may refuse to participate in or endorse efforts. Third, a lack of key applications to drive ROI could result in low utilization of infrastructure.
The DIS model reduces the technical details down to the simplicity of see-decide-act, easing ROI calculations. The summary ROI of all the proposed applications can justify efforts by getting past the technical barriers and into a state of managed risk. The summation of individual-application DIS models into a general DIS model can then serve as the template for developing the IIoT infrastructure.
Using the examples in Table 1, Figure 4 represents the general DIS model for several applications. It combines DIS models created for each application and, thus, shows general requirements through overlaps in infrastructure usage.
You can implement each application in stages such that the entire infrastructure is scalable, while providing future applications and infrastructure with a build-to blueprint.
Using The Approach
For the chemical plant already cited, the monitoring of 100 PRVs was the key driver, initiated by a request from the health, safety, security and environmental (HSSE) group to comply with new regulations. Following the see-decide-act model, a wireless infrastructure consisting of ten WirelessHART gateways was installed, and data from the 100 wireless acoustic transmitters monitoring the PRVs were routed to a PRV app residing on a server. HSSE personnel now receive alerts and data for corrective action and environmental reporting.
The reliability group — prompted because monthly vibration inspections didn’t suffice to prevent failure of critical rotating equipment — then started an initiative for more-proactive monitoring. This involved adding wireless vibration transmitters and routing data from these instruments to an existing asset management system previously used to store data from manual rounds. Data became available in relevant time, once every few seconds instead of after each round. Some manual inspections still took place, but less frequently.
Process engineers, seeing the value gained by the HSSE and reliability groups, decided to better monitor the health of compressors and large heat exchangers. Data had been collected manually from pressure gauges and with local measurements of heat. Wireless instruments were installed to measure pressure and temperature, replacing manual rounds. The data from these instruments were combined with existing data in the plant’s historian and sent to the facility’s asset-class analytics software (Plantweb Health Advisor applications), which contain pre-made analytics and dashboards. As a result, the plant now can see overall health metrics, which are correlated with production and used to generate work orders that go to the existing CMMS system.
The plant also was facing issues common to many sites — employee attrition and the need for modern tools appealing to a new generation of workers. So, it launched a general initiative for worker efficiency. Complicating matters, its control rooms were far removed from the process units, hampering operators’ ability to remain connected between the control room and the process units.
The plant set up a mobile workforce system so workers could access data via tablets and smart phones. Pump, steam trap, PRV and other monitoring and analytical apps now send data, analyses and reports to the cloud from which workers can retrieve the information via secure browser-based interfaces.
Developing An IIOT Vision
The DIS model provides an effective way to define the requirements for each application. Summing their estimated benefits and implementation costs determines the total ROI. Summarizing the ROIs for individual applications, along with the required data flows, creates an IIoT vision for implementation in phases and justifies the effort of working through the technical details.
Typically, the ROI of one or two applications will justify an entire plant IIoT wireless infrastructure. Even though not all applications will get implemented on day one, the general infrastructure will reflect their individual needs. By following the steps outlined in this article, an organization collectively working together to drive the IIoT implementation can achieve scalable and future-proofed deployment.
DAN CARLSON is a solution architect at Emerson Automation Solutions, Shakopee, Minn. Email him at firstname.lastname@example.org.