IoT has proven itself to provide unique value in commercial or industrial applications, even in the presence of the sensors and control systems that are already installed. The reason; IoT has found a niche for itself in being able to easily supply trend data for virtually any kind of sensor, whether already installed or newly installed for the benefit of IoT. This trend data is the basis of more advanced capabilities like predictive maintenance. Looking at these control alternatives, you typically have control systems (PLC’s) or commercial controllers (distributed controllers, building management systems, etc) also installed in the same facility, now also starting to offer IoT like capabilities.
But – what exactly are the technical and cost differences? This is an important consideration when trying to decide on a long term IoT strategy. The table below breaks it down:
| Area | IoT Condition Monitoring | Industrial Controllers | Commercial Controllers |
| Application Focus | Trending sensor data | Machine Control | Process Control |
| Application Value | Predictive Maintenance | (Re) Programmability | Automated process mgmt |
| Underlying architecture | Internet based | Proprietary | Proprietary |
| Comms Backbone | Cellular / WiFi | Proprietary Ethernet | Proprietary + Ethernet |
| Installation | Off Grid | Needs Plant Network | Needs Plant Network |
| Personal User I/F | Smart Phone Apps | None | None |
| Supervisory User I/F | Cloud (Web) | Optional | Proprietary |
| Cost to add (1) sensor | Free | Average $800 | Average > $1500 |
| Extensibility | None – Fit for Purpose | User extensibility | Extensible w/ factory help |
| Target User | Maintenance Teams | Facility Engineers | Plant Engineers / Mgrs |
The red highlight is, in our opinion, the main differentiator of the three solutions. It is pretty clear that the three were designed for different things. But as control companies begin to offer IoT capabilities, users will need to decide which approach to take, as the benefits of IoT have become compelling.
Industrial controllers are priced by how much I/O and memory (rated in kilobytes or megabytes) it supports, whereas this is not even a factor in the IoT world. In commercial controllers, systems are most often turnkey installations from one of the big commercial control companies, and pricing is usually turnkey based on the size of the contract. Other than IoT, the primary objective is one thing and one thing only: control. Either machine control or process control, with the new concepts of IoT being an afterthought, not designed in from the beginning
With IoT, the application comes out of the box, more like a SaaS (software as a service) application, preprogrammed and ready to use on day one. This is great for maintenance teams. It is designed to be ‘off grid’ – no plant network integration required. So once sensor data is available, it can be trended, then fed into machine learning and other advanced algorithms, only then can it provide the one thing that alternatives are trying to provide: predictive maintenance. Can control systems store sensor data, trend it, apply machine learning, and visualize it on a web screen? Yes, absolutely. It’s just a lot of work, a lot of programming, a ton of integration, and finally you may end up with something not very usable by the target user- maintenance teams. Today, what we mostly see are independent systems, and that makes the IoT system a check for the control system. Most plants today are working through integration scenarios using CMMS systems, so they get the best of both.
The bottom line – in IoT based condition monitoring applications – Simplicity counts. Easier to use and easier to understand means adoption, which leads to a better ability to predict equipment issues, and finally reduce downtime. Want to learn more? Contact us at sales@preddiotech.com
