Watching The Detectives

Stuart Pritchard October 21, 2016




JONATHAN RICHARDS, Head of Marketing at Mettler-Toledo Safeline Metal Detection, takes a closer look at what’s possible within the massively evolved world of metal detection…

Predictive Analytics, put simply, is an analytical monitoring tool aboard the latest metal detection systems that has the ability to monitor metal detector performance to a level of certainty that potentially replaces the need for the user to check its performance. In order to appreciate the benefits, it is important to understand exactly how it works. Essentially the technology has evolved from a software enhancement called Condition Monitoring that was developed as a preventative maintenance tool. The metal detector monitors its own performance and functionality and, if a change is detected, an alarm is triggered to alert the relevant operator. The innovative element is that the alarm is able to sound in advance of a failure developing within the system, enabling maintenance engineers to rectify the problem quickly and avoiding costly and unnecessary downtime.

This technology has been developed further in the form of Predictive Analytics, which has seen monitoring capabilities extended to the operating sensitivity of the metal detector. Once Predictive Analytics is set the alarm will be triggered when any change is identified and, importantly, it will be triggered well before the system drops below the set specification for the line on which it is operating.


On average most manufacturers will test at intervals of somewhere between one- and four-hours, but for the

purposes of this article let us take a recent customer example – a snack food manufacturer – and agree that testing is undertaken every two-hours.

Mettler Toledo identifies food-safety standard myths

Mettler Toledo identifies food-safety standard myths

Most snack foods are not shipped sooner than 24-hours from their time of manufacture, which begs the question: why are tests being carried out at such regular intervals? It suggests perhaps that the incidences of test failure are very high. However, when looking at food producers from around the world we do not notice large stocks of product lined up ready to be manually re-tested by the metal detector due to a prior test failure, which would tend to suggest the occurrence of test failure is actually not that high.

Another explanation is that testing has been carried out in this manner for many years. The first metal detection systems (released some 50-years or so ago) displayed stability and reliability that was average at best and the test failure rate was so high it was deemed necessary to test on a very regular basis. This mentality has not changed and a good reason to change the status quo has not been articulated effectively; until now.

Considering again the snack food manufacturer, as many manufacturers do, having an OEE improvement programme in place to review all of the usual suspects on the production line with regard to lost efficiency. When it came to the metal detectors it was uncovered that they were the most reliable pieces of equipment in the entire factory, with feedback suggesting that they rarely break down but simply continued doing their job day-in day-out, making them an unlikely candidate for OEE improvement activity. However, during the review it was observed that the manner in which these detectors were tested, along with the frequency, was having a negative impact on OEE.


Every two-hours one operator would climb up to a mezzanine floor and drop a metal test object into the Weigher, which in turn would be delivered to the metal detector. This, having detected the piece, would stop the bag-maker

Mettler Toledo T-Series with RP

Mettler Toledo T-Series with RP

which would then stop the line. A second operator on the ground floor would then remove the bag containing the test piece, reset the bag-maker and restart production – a process that is carried out three times at every two-hour test interval.

We calculated that the process took approximately three-minutes (six-man-minutes due to the multiple operators involved) plus the time it took to document the results which, if you do the calculations across the 24 metal detectors in operation, means the customer was employing two operators all day, every day to do nothing but test the systems. Depending on your labour costs this alone could have a significant financial impact.

By employing metal detectors equipped with predictive analytics, manufacturers can effect a significant reduction in testing frequency – as, unless the alarm sounds to indicate a potential issue with the system, there is simply no need to halt production to carry out checks.

Having seen a demonstration of this in action, the customer changed to the new format and was immediately able to reduce its testing regime from every two hours to every 12-hours. The reduction in test frequency and the knowledge that all product has passed through the detector within the required specifications led to significant OEE uptime advantages and its associated financial rewards.