Collecting data is one thing, but making sense of it is what adds value. In modern industrial parlance, it’s 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 measurements equipment – which one then has to make sense of, however, it is actually a bit more than that.
Not all data is the same
Data does not all take the same form. Some is ‘structured’ in the form of sensor output, for example, which can generally be organised in 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, requiring new database systems to analyse and make use of it.
The challenge is then to turn it into ‘Smart Data’. Enriching the 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 lots 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. At its simplest, an experienced operator might take several readings temperature, pressure, and vibration, for example, and make a ‘diagnosis’.
Structured data
This structured data forms the basis of condition based monitoring (CBM) 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 that of vibration monitoring for 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 system based on SKF’s 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 before it was analysed. This, says Scuderia, helped the team focus on results rather than data. To accomplish this SKF needed to adapt its IMx platform to suit this application, as it was more accustomed to monitoring applications such as wind turbines that required far lower data quantities, fewer channels and lower computer speeds.
Structured progress
Structured data can be interpreted automatically: if a certain parameter rises, for example, normal and abnormal behaviour can be identified and a diagnosis made. The on-going challenge is to automate everything, including the unstructured data.
Today, customers are often given a written report on the behaviour of a machine. Based on experience, engineering specialists like SKF 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, for instance, ‘know’ whether a defect is serious because they have been ‘shown’ by example. This principle is used to check everything from products to quality inspection.
A similar principle can be used 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. It might compare a current picture with a historical one, for example, or extract data directly from a written report. At the same time, the experts can focus on problems which are not yet known by the system and then input these as supervised learning in a continuous improvement cycle.
While the hardware and software to do this are pretty much in place, it is still necessary for all the systems, often produced by different vendors, to communicate with one another. Data access, exchange and interoperability has long been a concern, but there are signs that things are becoming more open as end users, served by multiple suppliers, push for systems that work in harmony with one another.
Moving from Big Data to Smart Data, means moving from knowing what is going on, to knowing what is going to happen, why it is going to happen and what needs to be done to prevent it. Getting access to this insight in realtime will create future benefits for 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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