Head of IoT Technology at SKF, Erwin Weis, says that collecting process data and using it correctly is a vital part of predictive maintenance programmes. Collecting data is one thing, but making sense of it is what adds value. In modern industrial parlance, its about turning big data into smart data.
Big data is often considered to be simply the vast amount of data generated from sensors, devices, systems and other measurement equipment. However, it is more than that. Data does not all take the same form. Some is structured, in the form of sensor output for example, which can generally be organised into a database format. Other data is unstructured, and might include text, images, audio or video. The mixture of these two very different data sets is part of the complexity of big data. In general big data is characterised by a new level of complexity and by requirements in terms of volume, velocity, variety and veracity.
The challenge is to make sense of it and turn it into smart data. Enriching raw data using knowledge and expertise is the way to achieve this. In an industrial context, this process is most often applied to operations and maintenance. Gathering large amounts of process data and interpreting it properly gives operators the information they need to improve running conditions. Correct sifting and interpretation of the data can help to improve machine performance or prolong its life by adjusting conditions based on the results.
This structured data forms the basis of condition-based monitoring and predictive maintenance regimes. Taking the correct measurements, and taking action as soon as they stray from the norm, helps to keep machines running for longer. A simple example is vibration monitoring of bearings, in which a single data set can help to prolong machine lifetime and boost reliability.
SKF engineers recently helped the Scuderia Ferrari F1 team gather data from its test chambers in real time. A platform based on SKFs IMx platform continuously monitored the vibration behaviour of drive components in the test chamber, processing up to 100 000 observations per second. This data was collated up to 20 times per second to break it into more manageable chunks for analysis. According to Scuderia, this helped the team focus on results rather than data.
Previously online checking of high frequency data in real time was impossible. This made troubleshooting a slow process and it was not possible to create forecasts on the service life of components based on trend values.
Structured data can be interpreted automatically. If a certain parameter rises, normal and abnormal behaviour can be identified and it can be adjusted, or a diagnosis made. The ongoing challenge is to automate everything, including the unstructured data.
Today, customers are often given a written report on the behaviour of a machine. SKF engineering specialists deliver many such reports to clients every year. So, what if the results of these reports could be produced automatically and be used for improving analytics capabilities? There are precedents for this. Machine vision systems can tell whether a defect is serious because they have been shown many examples. The principle is used to check everything from products to quality inspection. In the past, such defects could only have been recognised by a human operator.
Now, a similar principle is at work for more complex machine problems. Automated systems will soon be able to interpret a mass of both structured and unstructured data and automatically diagnose the problem. They might compare a current picture with a historical one, or extract data directly from a written report. With every text, image, audio or video, the automated system will learn and improve. At the same time experts can focus on problems which are yet not detected by the system and trigger a supervised learning.
Of course, there are hurdles to overcome. While the hardware and software are in place, the systems produced by different vendors still need to communicate with one another seamlessly. Data access, exchange and interoperability have long been a concern, but there are signs that things are becoming more open. Served by multiple suppliers, end-users especially are pushing for systems to work in harmony with one another.
Moving from big data to smart data means moving from knowing what is going on, to knowing what will happen, why it happened and what needs to be done. If we are able to get this insight in real time then we can create benefit and value for the industry.
For more information contact Samantha Joubert, SKF South Africa, +27 (0)11 821 3500, [email protected], www.skf.co.za
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