Retrofit solution saves thousands


Most brownfield sites have many machines functioning very well, but without the data output common on newer equipment. This data is critical in assessing the efficiency of machinery to realise valuable energy savings and identifying areas where improvements can be made while providing condition monitoring to avoid unplanned downtime. Turck Banner has a wide range of products capable of enhancing a machine and providing crucial data without interfering with the established control system. This is exactly what the company was able to do for a yarn producer with a century-long history, as MEPCA found out.

At its Dewsbury plant in northwest England, Lawton Yarns carries out a wide range of yarn-producing activities to provide the carpet industry with quality yarn products. It manufactures yarns for hand tufting, broadloom tufting, and Axminster and Wilton Carpets in 100% wool or wool-rich blends. The company can also combine wool with different fibres, and it excels in dry spinning, in singles, folded and to all colours, yarn counts, and twisted specifications.

Once the fibres are spun, they are transferred onto spools to be used in carpet weaving. This is a process which happens at high speed on machines capable of winding over 100 spools at the same time. If a thread breaks while being wound onto spools, this can damage the spooling machine if left unchecked or cause a machine stoppage, with the associated cost of lost production. To avoid this, on the original machines produced in 1999, the process was to ‘suck’ the broken thread into tubes, clear of the machine’s mechanism. This stopped the thread from building up into large balls and ensured the machine would not be damaged.

“The air used to suck the thread out of the way was constantly running, on every thread, to ensure any broken thread was captured,” explained Tony Coghlan, Managing Director at Turck Banner. “Lawton Yarns calculated that the 20 machines it had on-site cost the company £70,000 per year in energy to keep the air constantly on.”

Turck Banner was invited to the site to look at the application in 2020 and develop a solution in conjunction with Lawton Yarns’ engineering department. In 2020, a proof of concept was implemented that allowed Lawton Yarns to investigate the energy saving and ROI of the solution developed.

The first element of Turck Banner’s solution was to incorporate an inverter. This allowed Lawton Yarns to drop the air produced to around 15% on each machine. Then a BL20 Programmable Gateway was programmed to detect when the sensor output was triggered, turn on the inverter, and control an indicator. An input card and an output card were used to capture signals from a sensor and output triggers to the inverter to enable this to happen. An indicator was added to the system to indicate to the workers that a thread was broken somewhere on the machine. Finally, one Q45 long-distance sensor was incorporated into the system to detect when a thread was broken and inform the program that a ball was forming.

“Thanks to the system reducing the energy requirement when it was sucking air and only operating when there was a thread breakage, during Lawton Yarns proof of concept study, they saw an 80% reduction in energy on the machine over a six-month period,” added Tony. “The study also found that maintenance activities also reduced on the machine because the motor was not running at full speed constantly.”

Following the proof of concept study, Tommy Fisher, the engineer manager at Lawton Yarns, produced a report for senior management that showed if they installed this small decentralised solution on all 20 machines across the site, the ROI would be nine months for the complete solution.

The company is now making significant savings thanks to the solution provided by Turck Banner. In addition, further sensors have been added to capture data from the machine, which previously had no data capabilities, and pass it to the cloud where simple analysis identifies air leaks, bearing wear and other trends, which allows predictive maintenance to further reduce their costs.


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