Why the Industrial IoT Needs Open Source to Innovate
The industrial world has a long history of modernizing their process controls in order to keep production running efficiently and safely while minimizing downtime. Yet, many are locked in established data historian solutions that are costly and lack the methods needed to provide innovation and interoperability. In contrast, open-source software —which is built on the foundation of community — inherently provides diverse design perspectives not available from a single software vendor. It provides freedom from vendor lock-in, which means it will always provide you with the ability to integrate with other solutions. And finally, open-source software provides customization, allowing you to adapt the code to fit your ever-changing system requirements (which is not easy with proprietary systems). In this article, we will examine what the existing solutions lack and review a few open-source projects that should be considered for future success for operators.
Industrial organizations around the world, large or small, have been working with a number of solutions to digitally transform their manufacturing processes. Most organizations use a system of software and hardware components called Supervisory Control and Data Acquisition (SCADA) to help control machinery and systems in a factory in real time. In particular, these systems control processes locally or remotely by gathering event data from sensors, valves, pumps, motors, and recordings. In addition, the relevant data is presented to the operator to make decisions about the machinery to keep it running optimally. Many industries rely on SCADA systems — including energy producers, manufacturing, and food and beverage — to collect event data such as:
All process and event data include a value and a timestamp and are stored in a data historian to show trends per machine or across a collection of machines. A data historian is a time-series database, and as such, needs to allow for fast ingest and query of data in near real-time and provide compression of the data to minimize storage.
There are many commercial data historian solutions in the market, and several have been on the market for some time, yet all these solutions come with a number of challenges — primarily cost, vendor lock-in, and scalability.
Cost — These solutions are not cheap, charge an annual license and support fee, and are costly to set up and maintain. Moreover, custom development on top of these off-the-shelf products is common, which may require outside consulting resources. And since these solutions are proprietary systems, the work is time-consuming and expensive.
Vendor Lock-in — These solutions are often Windows-based and do not offer a simple, open API for other software to interface with. This means you are limited to integrate and buy all components from only one vendor, locking you into a proprietary solution.
Scalability — Collecting event data from your equipment is just the beginning. True digital transformation requires more data sources and more analysis of the combined data to gain a better understanding of your systems. Doing so with the existing solutions will require the vendors to create (and charge for) new interfaces for data import. The good news is that this data is easy to export to spreadsheets. The bad news is that spreadsheets only give you a static view. What is required instead are modern dashboarding engines that have obsoleted the idea of exporting large time series datasets. Ultimately, with all of this data coming in, you can no longer rely on manual techniques for analysis.
To contend with these challenges, the industrial sector should consider new ways for optimizing operations, including trying open-source solutions.
