Data collection: from manual to automated

INFORMATION IS everything in the modern world – and it can help manufacturers to streamline their operations. By keeping tabs on production data and machine health, manufacturers can prolong the life of critical assets to boost overall equipment efficiency.

Just as a well-maintained car is more efficient in the long run, so production machinery benefits from constant monitoring. Manufacturers have always understood this, but the way in which it is done has changed radically. The outdated approach of ‘reactive’ maintenance – where assets were fixed when they developed a problem – has been replaced with a more proactive regime. Here, predictive maintenance keeps a close eye on machine health and flags up problems as soon as they occur. Any small problem – such as a worn bearing – can be detected and fixed before it becomes serious. This helps to avoid situations such as unscheduled shutdowns – or catastrophic machine failure.

The approach, also known as condition-based monitoring, will become more commonplace in future. As well as detecting actual problems, it uses insights from previously collected data to forecast when a problem is likely to arise with a machine. This means that maintenance schedules can be timed more accurately, to reflect actual machine performance.

Automated or manual?

There is a key choice to make when selecting a method to collect machinery data: automated or manual. Manual data collection involves equipping maintenance staff with handheld devices – such as tachometers – that can record machine parameters during a typical ‘walkaround’ inspection of the factory floor. Alternatively – and this is becoming more commonplace – a company can install permanent sensors to gather this data automatically and transmit it across a network.

When first introducing data collection to their organisation, companies tend to opt for manual methods as these are typically easier to perform – and less expensive. Manual methods also fit with existing ‘physical checks’ that are typically performed on the shop floor – such as relubrication. Once companies are comfortable with manual collection, they may introduce a level of automation – with a view to introducing a fully automated system in future.

However, many companies are happy with a ‘mixed’ approach that blends manual and automated data collection. They do this because it allows them to balance the costs and benefits of each approach. For instance, the extra cost and complexity of automated data collection can be justified by insight it gives to critical asset performance. However, it probably cannot be justified for smaller machines. With this in mind, organisations often use automated systems for their most critical assets and stick with handheld data collection for everything else.

The handheld approach has also become more sophisticated. A good example of this is seen in a new equipment range from SKF. The company has expanded its range of tachometers – which measure rotational and linear speed – with new digital models. These use laser or contact measurement for fast, accurate collection of data. As well as controlling costs, this approach has another advantage: it allows data to be collected in areas where ‘straight line’ access is difficult.

Digital benefits

The ongoing digital revolution will transform how companies collect machine data – and what they do with it afterwards. As ever, economics plays a key role – and the greater affordability of these systems is a critical advantage to manufacturers.

For one, the cost of permanently installed data collection systems continues to fall. This is largely due to the continuing development of robust sensors that are inexpensive enough to be deployed across the factory floor. Once, devices such as vibration sensors could only be justified for the most critical machinery on the shop floor. Now, they can be used more widely thanks to their inherent affordability.

In addition, the cost of gathering and analysing data – and connecting the sensors together – has also become simpler and cheaper. This is critical, because it reduces installation cost – which can account for up to 75% of the cost of a permanent condition monitoring system.

Modern systems can reduce installation costs because data acquisition devices – such as sensors – can be connected directly to their existing wired networks.

Alternatively, they can easily go wireless – as secure Wi-Fi networks are increasingly common in factories.

Switching to wireless

One way of hooking into wireless is to install a low-power wireless “mesh” network. This makes it possible to install sensors that run on battery power alone. While the system is easy to install, the energy cost may be difficult to justify.

However, modern wireless condition monitoring systems are improving rotating equipment performance programmes in a way that was previously considered uneconomical. This is done by taking knowledge on machine health monitoring – collected over many decades by manufacturers – and combining it with network technology from connectivity specialists. At the same time, it minimises energy consumption.

SKF has developed a wireless condition monitoring system that can automate vibration data collection within a service contract. In this case, a mesh network protocol enables sensors to exchange data by navigating around obstacles – such as pipework and liquid storage tanks – instead of trying to punch through them.

Smart analytics

Condition monitoring systems are also becoming more economical to run, thanks to new analytics approaches – such as the use of machine learning technologies. These methods automate the interpretation of machine condition data to a higher degree than was previously possible. This allows companies to monitor more assets with fewer skilled analysts.

Machine learning uses special algorithms that give computer systems the ability to learn without being explicitly programmed. It relies on gathering, categorising and interpreting large amounts of data. Just as humans cannot acquire new skills without information – or examples to follow – so machines are incapable of learning without access to data. Machine learning algorithms effectively learn models of behaviour from the data sets that are presented to them.

Advanced data analytics and machine learning are already helping manufacturers to raise productivity and efficiency. In particular, they allow companies to increase capacity without significant capital investment.

New technology is also changing the way that machine condition data is used. While centralised – or remote – data analysis is well established, the Internet and cloud computing have made it far easier and cheaper to implement. The ability to access real-time data from a remote location has caused a shift in productivity.

This can have huge benefits for companies that operate multiple assets around the world. These technologies make the results of analyses far more accessible. For instance, it gives a factory manager instant access to the status of a facility – from a glance at their phone.

End of the walkaround?

At every turn, the digital revolution seems to tear down established traditions. It delivers critical data to even the most remote analyst or factory manager. However, it is unlikely to spell the end of the walkaround – but will change the nature of this age-old manufacturing tradition.

Maintenance walkarounds have historically been used to perform routine checks and measurements. In future, the raw data will already have been logged and analysed – giving maintenance specialists time for ‘value added’ activities. Raw data does not solve problems – such as a worn bearing or malfunctioning machine – but identifies the cause and points to a solution.

For instance, the mass of data can help a maintenance specialist prioritise which machines need attention, and in which order. At the end of the day, there is no substitute for a hands-on approach. It’s a little like when your car breaks down. You may have all the fault data at your fingertips, but only a seasoned mechanic can make sense of it all – and fix the car.

www.skf.com/uk