Machine data is normally stored to generate data series that can be analysed. Where and how data is stored depends on its structure and application. If data is collected specifically for a target system, the target system will also handle the data storage itself.
The most sensible way to store machine data is to store it in a central database. Classical relational databases are often used for this purpose. In these databases (e.g. Microsoft SQL Server, Oracle, MySQL) data is stored in fixed table structures and can be made available from there to various target systems.
In addition to relational databases, more and more databases with new concepts are gaining acceptance. In the so-called NoSQL databases, data is not stored in tables but in loose structures, which allow a completely new type of access, larger amounts of data and a high degree of flexibility with regard to the structure of the data. Special database types are available for different types of data (document oriented, graph databases, time series, key-value, multi-value).
Which database should be used in particular must be checked against the target systems, the type of data and the types of use.
The term “machine data” summarises all data that can be retrieved from a machine, typically at run time. However, what the content of data is and how it is to be interpreted can be very different. In principle, a distinction can be made between simple data points and complex data sets.
Simple data points that are recorded continuously or when changes occur are process data. This data is recorded and stored with the current value and a time stamp as reference. This allows the status of the machines to be analysed using the historical data series.
More complex data from the machine are recorded as coherent data sets. In addition to the optional time stamp, these data contain above all unique references to data objects for which they provide associated data. These can be production or manufacturing orders or also material numbers, machine numbers, recipe numbers, batch numbers, etc. The data supplied in the data record for this purpose is then dependent on the application. For example, it can be test results, consumption data, finished goods information, traceability data and similar. The aim of the industry-specific communication standards is to describe many of these applications in general terms and thus to define data records that are as similar as possible, independent of the machine manufacturer.
Of course, there are also costs involved in setting up machine data acquisition. These have to be invested in physical networking, the expansion of control systems, software for recording and storage and, last but not least, the work involved in planning and implementation. But is the effort worth it?
The benefits of the recorded machine data are manifold and the investment is almost always worthwhile. Especially the use of the data in different systems for ever new applications constantly increases the value of machine data acquisition. Some of the many profitable applications are:
- Automation of manual capture
- Optimisation of production processes based on the knowledge gained from machine data
- Improvement of reaction times within production through fast information
- More precise planning options for personnel and raw materials through more accurate time recording and material consumption reports
- Avoidance / shortening of machine failures by immediate fault message distribution
- Improved customer service through more accurate information on delivery times
- Optimised machine maintenance by more precise maintenance cycle compliance and predictive maintenance
- … and many more
If a machine is networked for machine data acquisition, IT security must also be considered from the outset. Because with networking, a machine is always more vulnerable than without networking. That is a fact. But there are many ways to reduce the resulting risks to a minimum by using generally accepted security mechanisms.
The most basic protection against unauthorised access is strict network separation. A clear distinction must be made between the different network areas, how they are connected and which communication between the networks is permitted. This is implemented with a firewall. For production networks, solutions are also available which segment the production network as such again and seal off individual production islands separately. Only the paths required for machine data acquisition are opened for defined clients.
The standardised communication protocols also contain integrated mechanisms to guarantee the security of data collection. Thus, security has been implemented as a standard requirement in the OPC UA concept. In addition to encrypted transmission, OPC UA also provides for certificate exchange between client and server. Unfortunately, many other protocols originate from a time when the focus was not yet on security, so that integrated mechanisms are missing and always have to be configured additionally. OPC UA is therefore also a good choice from a “security” perspective.
Last but not least, user authentication is also required for machine data acquisition in order to restrict access to the data by role. For modern systems for data storage (databases / cloud) these functionalities are standard and only need to be actively used.