Predictive Maintenance 4.0 at Anuga FoodTec 2022ide
New technologies are enabling an intelligent and connected production within the companies of the food industry. Here the focus is increasingly being placed on condition monitoring and predictive maintenance – two key innovations of Industry 4.0 – which promise the forward-looking maintenance of individual components and entire systems. The big players: Smart sensors, Big Data, Cloud services and machine learning approaches. In combination with each other they enable the permanent and real-time optimisation of processes, which increase the quality standards and guarantee the availability of the machines. In this connection, the exhibitors will present concrete solutions at Anuga FoodTec from 26 to 29 April 2022. In addition to the technical implementation, the usage of predictive maintenance from a business viewpoint and new after-sales service business models will be addressed at the Cologne fair grounds.
Monitoring to prevent downtimes
As a rule, in the case of a machine fault, a technician has to carry out an on-site repair. This leads to downtimes and turnover losses. The aim of the maintenance concepts presented at Anuga FoodTec is to find the optimal timeframe for the respective companies for servicing the plants or exchanging components. In order to achieve this, timely information is required directly from the machine – and thus smart sensors. They operate using edge computing and collect the raw data needed for the analyses and subsequently interpret and communicate these promptly. Furthermore, the sensors independently control the quality and robustness of their signals.
In the food industry this is demonstrated by the increased implementation of intelligent sensors, which are capable of communicating, in packaging lines. Whether muiltihead weighers, tubular bag machines, tray sealers or X-ray inspection systems: They all collect valuable data. For example, in the case of modified atmosphere packaging (MAP) – a method that allows the shelf life of ready-to-eat portions of food to be prolonged. Extreme caution is required here. Even the slightest inaccuracy when feeding in the film can lead to production losses. On a line where fresh cut salads are packed gently under protective gas, a condition monitoring system stops the machine as soon as the film is not conveyed properly or if the sealing pressure drops. Predictive maintenance goes one step further. Here the fact that the sealing pressure is dropping gradually can be determined much earlier and the fact that this trend will ultimately lead to the critical threshold being reached. If the machine itself is responsible for the fault, for example due to sealing tool abrasion, the technician can intervene in due time – and planned maintenance can be carried out instead of an unplanned downtime occurring.
Machine learning for precise prediction
Mechanical parts cause oscillations and noises, which vary across the shelf life of the components and give an indication of the state of wear and tear of a machine. Here AI-based software solutions come into play that record the states as Cloud applications and recognise so-called drifts with their algorithms, i.e. values that change over a longer period of time. The following applies here: The more data at the disposal of the algorithms, the more precise they are. However, the prerequisite is also a corresponding history in order to establish correlations between the machine’s condition and data. Ultimately, the probability of a breakdown occurring is calculated. Using the live data of the machines, predictive maintenance can also work in a preventative and proactive way, while also predicting when and where a problem will arise. In the case of a higher load, the maintenance interval is reduced to avoid damage occurring. In the case of a lower load, the maintenance interval is prolonged to save unnecessary costs and downtimes. Visualisation software displays all data, both on the machine control level as well as on mobile end devices.
And that is not all: With the help of predictive maintenance a company’ value chains can be linked up with the after sales service of the machine builder. As soon as the exact time a machine is to be serviced is determined, the respective logistics process can be initiated – in a totally automated process. This method also involves the use of cloud-based solutions. They can be integrated into the existing ERP system as an add-on. In this way, work and ordering processes that are coordinated with each other can be safeguarded, spare parts tracked and cost-optimised requirements planning ensured.
Retrofitted predictive maintenance
But how can existing machines be made fit for Maintenance 4.0 in the course of modernisation measures? The answer of the technology suppliers at Anuga FoodTec are retrofit packages that comprise of everything needed for a targeted retrofitting of machines – from robust sensors through to system solutions with flexible cloud monitoring. They teach themselves about the machine and can be simply integrated thanks to their battery operation and wireless communication. The outlook of Anuga FoodTec 2022 shows: Unplanned downtimes will soon be a thing of the past. Food producers are increasingly moving away from the reactive approach, i.e. not carrying out repairs until the machine has already broken down. What predictive maintenance already achieves today and which developments are expected to follow will also be a theme of the conferences and Guided Tours from 26 to 29 April, which are being organised by the DLG (German Agricultural Society).
The theme Predictive Maintenance is also addressed in the extensive congress and event programme:
2:30 – 3:00 p.m. – XTS – Enabler for Smart Factory in Food & Beverage
Organiser: Beckhoff Automation GmbH & Co. KG
Speakers Corner, Passage 4/5
11:50 a.m. – 1:20 p.m. – Artificial Intelligence – Science Fiction or Game Changer?
Main Stage Smart Solutions – Higher Flexibility, Halle 6, D 90 F 109
Main Stage Smart Solutions – Higher Flexibility, Hall 6, D 90 F 109
Organiser: Freudenberg FST GmbH
Speakers Corner, Passage 4/5
The event and congress programme will partly also be transmitted via Anuga FoodTec @home, including also the events described here, which will be streamed on the online platform and which will subsequently be available on demand up until 30.06.2021.