Digitising legacy systems: Challenges & opportunities

We all prefer production and maintenance decisions to be made based on effective monitoring, rather than fixed schedules or guesswork. But it is not uncommon to see plant equipment still working after 30 years. Can these systems be integrated into the requirements of modern production?

Older systems, while reliable, will almost certainly be more vulnerable to failure simply by virtue of their age. Yet, the savings that can be made and the ROI that can be achieved by making repairs and reducing downtime in existing equipment can be better and realised faster than investing in new machines. Furthermore, if a brownfield automation system is directly linked to the production and value chain, it should also be digitally integrated. At the same time, condition monitoring should be implemented as the basis for predictive maintenance to reduce downtime.

Requirements for digitising legacy systems are demanding: for most brownfield systems, there will no OPC UA (the Unified Architecture machine-to-machine communication protocol for industrial automation developed by the OPC Foundation) available during the original installation. There probably won’t even be an Ethernet interface. But even so, there are ways to collect system data that can be used to improve overall system efficiency.

For example, sensors can be connected to the power supply to help ensure efficient energy management. In conjunction with recording the start and stop times of production machinery via decentralised I/O, this can provide essential comparative data. Sensors and cameras can register and record pass/fail product data at different points on the production line.

Users can monitor their plant’s condition by implementing a so-called Smart Condition Monitoring system: here, a vibration sensor is attached via a Power-over-Ethernet cable to rotating machinery such as fans, gearboxes and motors, without accessing or changing the machine control system. This easy-to-integrate approach has been developed by Mitsubishi Electric in conjunction with its partner Schaeffler, a member of the e-F@ctory Alliance network.

When and where to start
The more data that can be collected, the more opportunities there are for system optimisation through real-time edge or cloud analysis. But what is important is to examine the possibilities for individual systems, taking into account the return on investment in each case. Certainly, though, in many cases old brownfield installations can be made fit for a digital future with minimum effort. Companies will benefit from more transparency and flexibility of their production facilities, which is a prerequisite in order to remain competitive on a global scale. Time to market and the ability to produce individualised products cost-effectively will depend on the degree of digital automation. It is therefore a question of ‘when’ not ‘if'.

Artificial intelligence within manufacturing
Emerging technologies such as artificial intelligence (AI), which are yet to mature, will build on the key elements of analysing smart data and big data.
Analysis of smart data and big data enables better production decisions to be made, and the techniques of deep learning and machine learning are emerging to automate the planning of production actions. This lays the foundations for the broader use of AI to achieve maximum flexibility in volatile markets. In this way, individualised products can be produced for the same price as mass-produced products. Smart data, big data and the analysis of these in conjunction with AI will support users to realise market requirements.

At Maintec on 30-31 October (NEC) Muntons Malt, one of the UK’s largest producers of malted barley, will explain why it has chosen the Smart Condition Monitoring (SCM) system from Mitsubishi Electric to protect fans and motors vital to its production process. It uses sophisticated control automation and bearing monitors to accurately predict maintenance requirements.