Remote connectivity has provided the ability to do almost anything from a safe distance or sometimes even without the need for a human to participate at all. Equipment diagnostics have benefited greatly.
Whether it’s the advantage of receiving advice from someone halfway across the world who can provide diagnostic recommendations or it’s the ability to conduct predictive analytics in the cloud, machine connectivity has opened new doors to better operations.
We asked 17 industry experts for their takes on the impact that it’s had on equipment and manufacturing.
How does remote connectivity improve the effectiveness of sensing and diagnostics?
Remote connectivity in some cases allows us to use more than one technology on a specific data point and helps to ensure the quality of the data taken due to the point being physically located in the exact same position every time.
For suppliers, alternatives to traditional hierarchies are spawning new business models based on connected assets. Instead of reacting to a customer call when something has gone wrong, suppliers can establish dynamic relationships with their customers by offering better maintenance service, ensuring optimal machine performance and, in some cases, guaranteeing machine output. Remote connectivity enables new services such as monitoring for excessive energy consumption, identifying usage patterns that potentially impact machine health and service life, detecting operating conditions outside of design range or recommending new settings or parameters to fine-tune individual machine performance.
Also read: Can I monitor and program using the same remote-access hardware or software?
Over time, industrial companies will shift their machine buying and asset performance management protocols to take advantage of the new capabilities made possible by digitized machines and enhanced service offerings. Instead of making capital expenditures to buy machines and then contracting to support them, in certain cases they may choose to enter into service-level agreements guaranteeing the outputs of machines, similar to the power-by-the-hour jet engine market model.
Now the engineer simply needs to connect to the remote machine using a cloud-based VPN service and within minutes can diagnose and often provide immediate correction, saving thousands in downtime expenses. Also, with extended secure data connectivity, data from sensors can be collected from many pieces of equipment and over time analytic tools can identify issues or patterns. This provides the organization with unprecedented information to leverage preventive and proactive service and keep the equipment in optimal operation. The combination of sensing with modern cost-effective Internet connectivity provides an new paradigm to serve any machine in the field and keep them running with optimal efficiency, which benefits both the machine builder and the customer alike.
Once the data is on Ethernet, there is no reason to not use protocols like OPC-UA to separate process data from diagnostic data. And once that has happened, the analysis of the diagnostics data can occur centrally using tools that are much better-suited than the PLC. In fact, such a central analysis system can even take inputs from other systems and correlate them. In the future, that may be done using big-data methods where diagnostics is self-learning.
By integrating the sensors with monitoring capability into a process control system, a user can instantly access the device, in a web-page format, for example, and perform setup, diagnostic, and process optimization tasks. Using this connectivity, a manufacturing system owner can gain a unique front-line look into machine efficiency, with data on not only what the sensor is doing, but also how well it's doing that task.
Remote teach functions or parameter setting allows owners to commission and adjust the device from a safe and convenient position without having to dismantle the machine and is a perfect example of remote connectivity improving productivity and troubleshooting. Sensors' parameters can be easily adjusted for product changeovers or process routing. Users can also fine-tune sensor functions in step with changes in environmental conditions such as ambient lighting, noise and dust particles in a saw mill or wood-product operations.
Some of these products are also equipped with large-capacity memory, so they can store a vast amount of operational data for later remote download and analysis. This feature can help users to pinpoint the source of anomalies, gauge specific functions over time to spot trends and evaluate corrective actions and assess the various factors influencing a machine's overall performance. For example, a meat or food processing plant could tap sensor data to assess how productivity might be affected by variables as diverse as product type and time of day.
Connected intelligent sensors can also give condition information of adjacent components, such as variations in fluid flow rates and process temperature, lubrication state, and other metrics that, if recognized too late, often lead to costly, unexpected system shutdowns. Similarly, users can communicate directly with certain encoder variants for status information such as temperature range; diagnostics such as error codes; and live data without the need to access or alter a PLC's programming. As a result, owners can plan, coordinate and implement corrective actions and preventive maintenance activities with the minimal effect on overall facility operations. Furthermore, encoders that have an integrated Web server can be accessed to perform firmware updates directly while they are on the machine, which saves cost and time since replacement of the encoder is not necessary.
Instead of walking catwalks and climbing service ladders, M&D professionals can log in and see data almost as if they were standing in front of the asset. With edge analytics, these systems can intelligently alert professionals when there is a yellow or red alert so they spend more time looking at information of interest and less time sorting through tons of data logged from machines that are operating normally.
Historically this would have involved mobilizing people and test apparatus from a distance, but, with remote connectivity and control, nearly the same effect can be achieved very quickly and using the instrumentation already installed and in place.
Remote connectivity opens up the possibility to re-calibrate, tune devices deployed in the field improving the quality of service, maintain equipment reliability and enable remote diagnostics.
If those communication channels allow a remote analyst to access data on demand and perform additional diagnostic tests as required, then the ability to detect faults at an early stage and develop an appropriate action plan is greatly enhanced.
Remote connectivity bypasses the delay and errors of manual data collection. Machine data becomes available for analysis, reporting and regulatory use and can be made available for use by on-site personnel, for example on a smartphone or other mobile device.
Additionally, Ethernet-based remote connectivity adds its own benefit. As data is exchanged over an Ethernet connection, the messaging includes status information on the Ethernet connection itself. Like information from sensors and devices, this data can be tracked and trended to identify communication problems.
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