IoT Condition Monitoring - Comparing Alternatives

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