The Challenges of the IIoT in Monitoring

How IIoT technologies can be leveraged to improve reliability through condition monitoring? Emerson replies to this question, by describing the essential steps in setting up a good monitoring programme

Collecting data to enable experts to perform condition monitoring is not a new concept within the process industry. Automation vendors have provided specific services, such as vibration monitoring on rotating equipment. In Vladimir Nitu’s opinion, European digital and connected services manager at Emerson, what’s different with the IIoT is the introduction of a new suite of sensing technology, cost-effective approaches for connectivity, and better ways of connecting data with people. In addition, the industry has recognised that machine learning, artificial intelligence and cloud technology can be applied within condition monitoring concepts to enable predictive maintenance practices. Automation technologies are a great lever for connecting what is happening in operations with business performance KPIs (Key Performance Indicator). Automation systems are the nerve system for all the data, and enable experts to see what’s happening inside the facility. Real-time data is generated by new sensing technologies, then secure connectivity sends the data to analytical tools that can be embedded in edge computing devices or run in the cloud. Then mobile tools can provide access to that data anywhere inside the plant.

Emerson offers domain expertise especially for  very specific equipment classes.
Emerson offers domain expertise especially for very specific equipment classes.

All critical equipment can be monitored online

Equipment condition monitoring is an important use case for the IIoT. A condition-based maintenance programme can be stablished, with early detection of potential failures for a wider variety of critical assets to enable predictive maintenance. It is not just about monitoring vibration data on rotating equipment anymore. Leveraging a wide variety of sensor technologies, the condition of all critical equipment can be monitored online. This allows better planning of parts and resource needs for when a planned outage takes place.

A three key step process

The first step in setting up a good condition monitoring programme is to identify critical equipment and applications. If required, a more formal criticality analysis can be performed, usually as part of a standardised reliability programme, leveraging industry best practices for risk management. Another approach might be to review maintenance records to see which equipment has historically created the biggest negative impact to operations. When establishing a business case, the key drivers will be lowering maintenance costs, improving plant performance, increasing process availability and improving maintenance efficiency. The next step is to monitor the equipment while it is operating. This is where the IIoT provides real value. Traditionally, equipment has been inspected when it is offline and out of service. Sometimes parts are replaced whether they need to be or not. With many facilities extending the period between planned outages to five years or more, equipment will now run for many years with there being little understanding of its condition. Some preventative maintenance programmes have been established to periodically inspect equipment. This often involves skilled technicians visiting the equipment and collecting data manually on an infrequent basis, generally only once a year. Besides the challenge of trying to detect issues through a manual inspection at a single point in time, there is also the safety risk of sending people into hazardous areas while the process is in operation.The third and final step is perhaps the most important because it is where the real value can be achieved. It is not enough to simply monitor the condition of critical equipment. There needs to be the ability to act on the monitoring results. There must be a focus on planned maintenance, and success depends on the proper integration of predictive maintenance into work processes.

Maintenance 4.0: Analitycs and data management.
Maintenance 4.0: Analitycs and data management.

Some of the main concepts to consider

There are some key concepts to consider. The recommendations made by the domain experts need to be integrated into the maintenance planning systems and the work needs to be prioritised properly. By gaining the ability to detect potential failures before they happen, the necessary replacements parts can be ordered well in advance of the maintenance work. Condition monitoring results will help focus efforts on critical equipment that really requires repair. Finally, much more will be known about the condition of equipment going into a maintenance activity. A better plan will ensure maintenance work is finished on time. With IIoT, data is collected while the equipment is online, during normal operation. Experts can be helped by analytics software to identify potential failures in the early stages of development, and they can plan for future maintenance events.

With IIoT, data is collected while the equipment is online, during operation.
With IIoT, data is collected while the equipment is online, during operation.

An outcome-based service

As a result of IIoT technology, a new business model has been created where external service providers can offer an outcome-based service. This is not unique to the process industry, but is gaining in popularity in other sectors as well. When attempting to achieve a desired outcome, it can make sense to turn to external service providers, such as Emerson, that offer domain expertise especially for very specific equipment classes. Connected Services leverages IIoT technology to securely connect on-premise data systems and sensing networks to cloud-hosted systems that enable experts to analyse the data. Companies can then focus on better planning of maintenance activities, preparing for shutdowns, turnarounds and outages, and respond to the more urgent daily priorities. Extending a shutdown due to poor planning can be just as bad as unplanned downtime. The goal is to plan maintenance using actual equipment data, ordering parts early, and execute work based on the condition monitoring results achieved in between outages.