Careful plant data collection is a basic requirement for optimum management. Only data that has been collated comprehensively and precisely and then properly compressed and archived can form the basis of effective optimization measures.
The data collected must be valid. The data must only be able to get to the system in unaltered form. Precise time information, that is coordinated and saved throughout the system is just as essential as the care to be taken when compressing and archiving the data.
Data that is accessed directly by sensors and machines must be handled in accordance with standardized rules. For example, if data from different system environments are to be processed with one another. Even some incorrectly-interpreted time stamps or badly-synchronized units of measurement can make laboriously-created optimization processes obsolete.
The starting point for any process of optimization is thus ensuring a valid basis of data. Not just at the start, but throughout the process, over many years. Before the first data is collected, it should be clear which data is needed, in which formats it must be present and how it must be obtained, compressed and archived with consistently high quality.
Examples of optimization with plant data collection:
- Performance optimization:
The adjustment screws that make it possible to bring equipment to its optimum state can only be found out slowly and in a targeted manner. In addition to a precise strategy, with a precise plan to be adhered to, much diverse data is needed. This data must be archived over longer periods and able to be re-evaluated. It must also be suitable for many different analyses and possible considerations.
In doing so, it is important for plant recording that data is accessed from machines and systems as directly as possible. It must be possible in real time and with very short time intervals, with precise time information. In doing so, the data from the individual machines must always remain clearly separated.
- Quality assurance:
Which lot would then be produced under which conditions? A basic question in quality assurance. However, for targeted optimization of quality in the production and distribution of goods and energy, it is a matter of much more detailed data. Only when interrelationships and developments can be displayed in detail are weak points discovered.
To do this, much individual data most be available very quickly and also be archived for many years in such a way that it can be called up in unaltered form at any time.
- Project documentation:
Creating major automation projects requires much time, know-how and energy. However the documentation often takes up another large part of the time available. With the right technology and software, accurate project documentation can be created without problems and virtually automatically.
A prerequisite for this is that all necessary data is automatically collected when engineering the project and that all machine and project engineering data is also logged properly.
- Energy Management:
More and more companies are switching to an energy management system in accordance with ISO 50001. Data on energy consumption as comprehensive a possible and structured experiences on developments allow optimization of energy consumption, acquisition and performance and cost-effective management thereof.
Energy management in accordance with ISO 50001 needs actual support through automated procedures for collecting and processing plant data. The data should ideally be automatically logged and processed. That means targeted HMI data collection, special data collection modules in the SCADA, software specialized in data evaluation. Here it is recommended that systems and software are used that are familiar with and support ISO 50001.
PDC is top priority
The collection of plant data is a matter of top priority. This does not mean that management needs to worry about the collection and processing of data. However the quality of this data does influence decisions made by management to a large degree. Therefore stipulating and checking the requirements for the plant data collection and the quality criteria is also a task for management.