How IoT Can Improve Your Manufacturing Maintenance Systems

(Estimated Reading Time: 2 Minutes)
In order to accurately forecast equipment issues, your manufacturing maintenance systems need access to as much data as possible. Today, most manufacturers rely on Internet-of-Things sensors to provide that information. 
The Internet of Things (IoT)  a global network of physical objects that automatically collect, relay, and respond to data from the real world  goes way beyond the data pooling techniques that most of us are acquainted with; techniques such as computer modeling, data aggregation, applied set theory, meta-analysis, etcetera.
 Inherently forward-facing and action-oriented, IoT is to the dataverse what Neo is to the Matrix – capable of manipulating the underlying order to forge outcomes, rather than simply reacting to them. While that's admittedly a grandiose analogy, it's also a fitting one. Applied IoT technologies provide the infrastructure needed to support predictive maintenance, real-time production analysis and other processes that introduce entirely new pieces to the Chess board. 

There Are More Variables to Analyze 

"The cost of IoT sensors dropped from $1.30 in 2004 to about $0.50 in 2016."
Excessive vibration or abnormal power consumption can indicate that a machine is about to fail, but if you don't have the means to register these conditions in real time, you won't know if something's wrong. Sensors make it practical for manufacturers to gather data about specific conditions on a continuous basis. 
IoT devices aren't all that expensive, either. According to The Atlas, the average cost of IoT sensors dropped from nearly 60% in the past decade. 

You Can Implement Predictive Maintenance

Predictive maintenance involves analyzing IoT data to determine:
point.pngWhen a failure is likely to occur.
point.pngThe potential extent of the failure.
point.pngWhich components will be associated with the failure.
All of these details help technicians prevent failures from occurring. In addition, it allows facilities to reduce the costs associated with upkeep according to a preventative maintenance schedule. Instead of indiscriminantly greasing a machine on a weekly basis, for instance, machinists can grease equipment only when necessary. 
How can the IoT improve manufacturing maintenance systems?
Predictive maintenance allows technicians to act on anomalous behavior.
According to one study from Oniqua Enterprise Analytics, between 30% and 40% of preventative maintenance expenses are spent servicing assets with low failure impacts. That means manufacturers that use preventative tactics waste a significant portion of their budgets maintaining machines that don't need constant attention.
In contrast, predictive maintenance  enabled by an indusrtrial IoT backbone  allows manufacturers to focus on machines that actually need attention. 

Manufacturing Maintenance Systems Can Send Alerts 

This benefit actually ties back into predictive maintenance. Let's say you have a technician on duty at one of your production plants. He or she has an industrial tablet connected to a platform that manages devices across your facility. Your connected devices provide data on energy consumption, vibration, noise, heat and humidity. 
Your senior technicians have configured the platform to send notifications every time one of the sensors registers an anomaly associated with an impending failure. So, when an energy sensor registers higher-than-normal power consumption, your on-duty technician receives an alert as well as a short explanation as to what the problem is. 

Putting IoT Enabled Manufacturing Maintenance into Perspective

What we're talking about here is a truly smart manufacturing maintenance system. It not only logs all previous equipment issues but also initiates actions based on the data it receives and analyzes. As such, it is a true paradigm of prescriptive maintenance.
IoT also provides the perfect foil for manufacturers to put advanced machine learning technology to work. If, for example, a system can learn that malfunction predication varies across sites in a pattern that correlates with local climate and humidity reports, the manufacturer will have a new dimension by which to examine the data.
In this example, these newly discovered operational factors can now be tracked and managed. This will push the manufacturers data modeling to greater accuracy and  in turn  predictive strength. Such a realization would no doubt also bear impact on machine maintenance regimens – reducing operating costs and extending asset lifespans. 
Outside temperature is of course just one potential factor among a myriad, but it does a nice job representing the point. There are things affecting the efficiency and productivity of your operation that you're likely not taking into account and that you wouldn't know how to operationally accomodate even if you wanted to. It's precisely in those situations, straddling the edge of your ability and knowledge, when the promise of industrially applied IoT and machine learning technology is the most grand.

Where will such technology take you? I can't say. But I'll tell you this: it's taking the industry on a one-way trip to the future.

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