The concept of Occam’s Razor was introduced into our modern lexicon by Jodie Foster‘s character in the movie Contact. William of Ockham, after whom the concept is named, was a 13th century theologian and philosopher noted that, when given two solutions to a problem, the simpler one is more often the correct one. It was also presented by David Krumholtz‘s character in the TV show Numb3rs which stated, perhaps more correctly, that people tend to add complexity to a solution when trying to solve a problem. This is common with Industrial Internet of Things (IIoT) efforts. Are you adding complexity to your IIoT journey?
One of the fundamental misunderstandings of an IIoT effort is that it is a single complex event, or a number of complex events. Moreover, the effort will require a lot of money, a lot of sensors and tons of processing power. The good news is an IIoT effort should be a journey and start with basic data gathering systems and grow over time. This better news is that you will not need a large capital budget to get started. The best news is that as simpler, less expensive efforts are implemented, they help pay for future efforts.
A great place to start on an IIoT journey is with a simple process historian. This allows you to capture data from your process variables, like PLC tags, and enables you to perform simple trend analyses in real-time. Maintenance troubleshooting is one of the most common applications. If there is a process upset, the process conditions at the time can be readily revealed. This helps you diagnose problem and take corrective action.
Because of the simplicity, process historians are common in many plants. You may already have one. But, its applications are limited to time-series data (the three pieces of data capture by a historian are time, value and quality). If this is the case the next step is to implement a rules-based event engine or summarization tool, sometimes known as an event database. This enables you to capture process events and correlate them to other data.
Downtime is the most common application for an event database. When a downtime event occurs, it is captured by the event database along with a reason code. Not only will you be alerted to an event, you will be able to track most common causes. Adding in quality information to a production event allows you realize Overall Equipment Effectiveness (OEE). Operations can now use this data to better understand the main causes of downtime and develop solutions to resolve them.
Another common application for an event database is product tracking and traceability, also known as genealogy. When a production batch starts all of the raw materials that are used in the batch are recorded as part of the batch record. As the process continues the finished goods are also recorded In the batch record. You can quickly determine the flow of material for any given batch. This is also extremely helpful for creating batch reports. Moreover, because the start and stop times of the batch are known, all of the process data can be queried specific to the batch.
A third application for an event database is for condition-based maintenance. Rather than performing preventative maintenance on calendar basis, maintenance occurs when certain conditions are met. This can be runtime hours, vibration levels or a combination of variables, like motor current and speed. It should be noted that with open systems, an event database can automatically generate work orders in your CMMS.
Once more simple systems are operational and people are using them (and understand them), you can introduce complexity into your journey. The aforementioned systems will generate a significant amount of data that can be used for better process control and improving production. IIoT efforts will help you better understand how one part of your process interacts with another part. The addition of sensors may, in fact, provide more insights. But you should heed the advice of Occam and not add complexity when it is not necessary. A simple solution may be the correct one.
Tagsacoustic advanced diagnostics analytical artificial intelligence asset management business development condition based maintenance control valve differential pressure downtime ers filtration flow genealogy general historian IIoT impulse piping level machine learning manufacturing intelligence measurement MES mixer orifice plate positioner pressure project management real time visualization rotating equipment safety SCADA temperature vibration wireless WirelessHART