Wireless as a means to overall equipment efficiency
Accurate machine run-time data helps determine why production goals aren’t met.
Field devices make operations visible and support data-driven decision making. Using technologies associated with the Industrial Internet of Things (IIoT), device-level data is accessible to operators and plant managers, offering insight into machine performance and process inefficiencies.
Real-time remote monitoring of machine status allows addressing issues as they arise, regardless of whether an operator is present. Personnel monitor multiple machines on a factory floor from a convenient location. Operators resolve small issues before they become big problems.
Wireless systems were difficult to install and complicated to maintain. Wireless technology has advanced significantly over the years. Today many remote monitoring solutions offer reliable wireless communication integrated into one inexpensive unit. These wireless I/O devices are easy to install, and then uninstall and move to a new location as monitoring requirements change.
One wireless I/O device can collect both digital and analog sensor readings and forward this data to a central collection point for analysis.
Furthermore, several sensors can connect to a single node, and 47 nodes can exist within a single radio network. This means multiple sensor readings aggregate into a single gateway device before being forwarded to a host-controlled system for analysis.
Serial-data radios further extend this wireless I/O network. Serial-data radios are back-haul devices that receive serial data from another serial-data radio, or a serial connection to a gateway, and forward the data to another remote serial device. Chaining data radios expands the network to meet the remote-monitoring needs of many applications.
An efficiency calculation
Overall equipment effectiveness (OEE) is a calculation of manufacturing process efficiency involving three primary factors: availability, performance, and quality. The availability factor considers events that decrease total runtime, including planned stops (such as for product changeover) and unplanned stops. The performance factor considers anything that decreases the speed of the manufacturing process while it is running. The quality factor accounts for parts or products that do not meet quality standards (parts that must be scrapped or reworked, resulting in wasted time).
An OEE calculation taking these factors into account expresses its result as a percentage value, with 100% meaning only good parts are made (quality), as quickly as possible (performance), and without any stops (availability). Calculation results provide actionable insights into the critical sources of waste in a manufacturing operation.
The OEE Foundation also identifies 6 "Big Losses" to manufacturing productivity:
- Unplanned stops for equipment failure
- Stops for setup, adjustments, or changeover
- Idling or minor stops (for issues such as a material jam or a blocked sensor)
- Reduced equipment speed
- Scrapped work
To reduce these losses and minimize their impact, visibility into where and when inefficiencies occur is essential. This is where access to data from sensors and indicator lights become very important. Logged data from sensors and indicator lights installed on machines can help calculate OEE and identify steps to improve efficiency of machines, processes, and people.
Machine runtime trends
Tracking machine and process data trends helps identify when and where losses are occurring. However, manually monitoring production machine status is time-consuming. Depending on facility size, manually monitoring machine status slows down production and requires additional time more effectively used elsewhere.
With a wireless system, on the other hand, using a tower light with a wireless radio base offers not only local indication of machine status but also remote status of each light module. By logging results from machine-status indicators like tower lights, users can track trends in individual machine up-time and cycle counts for timely updates.
Data can be used to identify whether a bottleneck is caused by a machine or personnel issue. Capturing machine status helps users identify causes of production loss. This information, necessary to identify and drive efficiency improvements, was most likely previously unavailable.
This was the case recently when for one manufacturer accurate machine runtime data helped determine why production goals were not being met. Operators blamed machine downtime for the failure and maintenance personnel blamed the operators. Based on the data, facility managers identified what exactly was transpiring.
A machine’s health
In addition to monitoring machine performance metrics, wireless sensor networks also check up on machine health. Machine predictive maintenance is challenging because minor performance changes can be hard to detect without the proper tools. Remote condition monitoring using a wireless system plays a key role in predictive maintenance and helps prevent costly downtime.
To take just one brief example, vibration is a key machine parameter. Machine vibration is often caused by imbalanced, misaligned, loose, or worn parts. As vibration increases, so can damage to the machine.
By remotely monitoring of motors, pumps, compressors, fans, blowers, and gearboxes for increases in vibration, problems are detected before they become severe. A wireless vibration and temperature sensor serves as a "check engine light" for machines by measuring RMS velocity, which provides the most uniform measurement of vibration over a wide range of machine frequencies.
After mounting the vibration sensor, a user must collect enough vibration data to establish a baseline for the machine. Initially set the threshold at 1.5 or 2 times the baseline. When the threshold has been exceeded, the wireless vibration and temperature sensor can provide local indication of the problem, the signal can be sent to a wireless tower light on a central location, or an email or text alert can be sent. The vibration and temperature data can also be sent to a wireless logic controller or programmable controller for collection and analysis.
Remote monitoring capabilities are making it easier for manufacturers to identify and remedy causes of waste within their facilities. By using wireless technologies, manufacturers can quickly and easily gather data needed for OEE calculations, as well as gain valuable metrics for predictive maintenance to maximize their machines’ performance.
Fritz Cleveland is product manager, wireless products, Banner Engineering.
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