Two key advantages of utilizing predictive maintenance methods for continuous machine monitoring are enhanced machine reliability and uptime. Machine health monitoring allows equipment to notify maintenance workers of detected issues and troubleshooting options. Machine health monitoring is primarily enabled by artificial intelligence (AI) and the Internet of Things (IoT).
However, the capability to use the insights gathered from the machines to increase operational efficiency is still in its nascent stages. Maximizing the capabilities of machine health monitoring will significantly improve processes and reduce machine error.
Labeling operations serve as a good reference for the merits of machine health monitoring. Plastic bottles are labelled using printers that use a substrate to create finished labels using different techniques and inks. Commonly identifiable label defects include but are not limited to ragged cut, misalignment and flagging, which is used to describe a part of the label sticking out. The issues could also arise from something as simple as a missing label.
The following are use cases of machine health monitoring for improved labelling operations.
>Ensure clean cuts.
Ensuring the machinery edge that cuts labels is sharp and equipment components are well fitted are critical to increasing the speed and efficiency of bottle labelling. A manual approach to quality assurance of the process is not feasible because of an accumulation of many poorly labelled bottles in a production line before being detected. Automatic defect sensors would not suffice for this if misaligned or If a defect is very small. This causes bottles to move undetected into the supply chain without being labelled thereby resulting in excessive waste.
Some companies have implemented auditing exercises that require operators to inspect label quality every thirty minutes. While this is better than not inspecting at all, there is a luck component required for early fault detection. Real-time automated monitoring of labeling performance can be achieved through predictive analytics. Dull and raggedy cutter blades, which are primarily responsible for inefficient bottle labelling can be detected early and replaced with newer ones during scheduled equipment stops and downtime. This eliminates the possibility of poor labelling due to dull blades.
>Caution should be taken label variations.
Labelling operations globally encounter challenges when a finished roll of material is replaced with a new and identical one. Machine health monitoring can assist in providing production workers with information on the possible reasons for the issues by comparing the new and old rolls. These issues, which significantly decrease equipment performance might have sprung up from a myriad of factors such as increased vibration, higher amperage from motors, machine stress, differences in ink dye etc. In many occasions, data insights captured by the machine health monitoring tools confirm what machine operators suspected as a result of years of experience.
Machine health monitoring uses predictive analytics to provide valuable insights that are capable of improving process efficiency and quality in various manufacturing processes. This has been made very accessible predominantly by today’s information feedback loop system technology. The amount of value realized from leveraging these predictive analytical tools cannot be over-emphasized.
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Olamide is a technology consultant with cognate experience providing digital transformation services for small and large-scale clients globally. With a focus on emerging technologies like IoT, Extended Reality, Blockchain and Artificial Intelligence, he has spent three years developing numerous articles on these knowledge areas for different platforms online and offline.
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