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Advancements in industrial sensing technologies have created new opportunities and value for data capture and analysis. But how do you start and scale an IoT implementation?
Sensor technology has long been a staple in modern manufacturing—providing data inputs for the measurement and control of processes and equipment. However, in the last decade or so, several other technologies have emerged to make industrial sensing, and the data gathered, even more valuable.
First, the Internet of Things (IoT) enabled sensors to communicate with other sensors and systems, making it possible to aggregate and centralize operational data at an unprecedented scale. Second, advancements in artificial intelligence (AI) and machine learning (ML) have made it possible to analyze that data in near real-time to generate action insights about factory operations.
The combination of these technologies to gather, aggregate, and analyze real-time data has opened up a new paradigm of connected, smart factories. IoT enables machines talking to each other in order to automate processes, drive efficiencies, and improve visibility into manufacturing operations. With data centralized in an enterprise asset management (EAM) solution, AI algorithms can detect trends and anomalies to drive predictive maintenance—ultimately avoiding equipment failures and preventing downtime.
However, putting the pieces together to get from vision to reality hasn’t always been easy.
Collectively, this technology shift in industrial sensing is known as Industry 4.0. But many industrial sites today are facing an uphill battle to reach Industry 4.0—old technology, patchwork implementations, and a lack of standardization all slow adoption at legacy facilities.
There are three main challenges. First, you need to get sensors in the right place to collect relevant data. Next, you need to efficiently transfer that data to EAM systems for analysis. And finally, you need to ensure your analysis reaches maintenance teams and others in the organization who can put it to use.
How you solve these will depend on—among other factors—the scale of your IoT deployment, the amount of data flow, and the frequency of data collection.
One of the first steps to adopting Industry 4.0 is to break the implementation into manageable segments. Rather than upgrading every sensor and machine in a facility at once, experts recommend starting small with critical but manageable problems. This helps avoid information overload for maintenance teams, enables you to identify which data gaps you want to fill next, and provides real evidence for the value of your IoT program.
One way to start small, without extensive implementations or costly retrofitting, is to use dynamic sensing for your data collection. Rather than sensorizing every machine or inspection point, you automate inspections with mobile robots, bringing the sensors where they’re needed on the right schedule. A mobile robot can be outfitted with the appropriate sensors and dispatched on site—a few sets of sensors on a single robot can cover an entire facility and the data can be transferred in any area that has comms coverage, rather than at the data source.
This approach enables you to validate your IoT-enabled predictive maintenance program without major communications or sensor implementations. You can start with a single inspection type—for example, thermal inspections to detect overheating motors—and scale from there, adding more data types and inspection points while freeing up maintenance teams to focus on fixing any issues detected.
To learn more about how to get started with Industrial IoT and the role of dynamic sensing, download our latest whitepaper!
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