In order to optimize the availability of industrial machines and thus keep pace with the ever-increasing requirements, plant operators and personnel need to have a good understanding of the dynamic, technical processes. The basis for this is the recording and, above all, the subsequent evaluation of all relevant process data. As different signal sources usually have to be taken into account here, causal relationships can only be identified if the data from all the control systems, sensors and devices involved are recorded synchronously, provided with a central time stamp and evaluated together.

This means that a high level of connectivity to different automation and bus systems is essential for systems for recording and analyzing measured values. It should also be possible to save the characteristic values calculated online in database or cloud systems for web-based analysis. For constantly running machines, there are usually reliable methods for monitoring this in common measuring systems. Most vibration monitoring systems are based on data that is recorded under reproducible measurement conditions and are therefore highly comparable.

However, the analysis becomes significantly more complex and, above all, more data-intensive in processes with variable behavior. In these processes, the states of the different components influence each other. In addition, most production lines have a wide range of different products. All this makes it very difficult to find reproducible measurement conditions. In these cases, methods are needed that use all types of data - for example vibration and process data - and enable reliable and time-synchronous monitoring under all process conditions.

Acquisition of vibration and process data in one system

The system for recording and analyzing measured values from iba, for example, is a suitable tool for this, as it records and analyzes process and vibration data in one system and thus helps to avoid unplanned downtimes. The more precisely the data is recorded, the better and more detailed the subsequent process and root cause analysis can be carried out. The greatest flexibility in analyzing process and machine data is achieved when the data is recorded as high-resolution raw data and not as pre-aggregated data. However, the disadvantage of this type of recording is the volume of data that is generated, as this can quickly add up to over 100 Mbyte/s. For this reason, it is important to extract the information and knowledge from the raw data using efficient methods in order to present the complexity of the process as clearly as possible to different user groups such as production and quality assurance.

Monitoring an inclined rolling mill

The production of seamless steel tubes is a complex and energy-intensive process

© Image: industryviews (Shutterstock) / iba

Such complex process behavior can be found in piercing mills, for example: For the production of seamless steel pipes, pipe flaps, i.e. thick-walled hollow bodies, are produced from solid blocks. In this process, a steel block is first heated to a temperature of over 1200 °C, guided onto rollers, rolled until it tears open centrally and pierced using a mandrel. The mandrel is stabilized and held in position by guide blocks. As all components must function perfectly and in coordination with each other for the process to run correctly, damage or wear to the various units has a major impact on product quality.

In addition, further process steps and the necessary cooling time ensure that a lot of time can pass between the occurrence and detection of a deviation - which can have costly consequences in the worst case: If quality control is only carried out when the tubes have cooled down, several tubes are usually affected and have to be melted down again or scrapped. With such energy and cost-intensive processes, monitoring should therefore take place in real time. Here, the iba system with 'Edge Analytics' offers the possibility of calculating the relevant characteristic values from the high-resolution measurement data directly on the process in the edge device, such as an 'ibaM-DAQ'. In this way, the data is evaluated exactly where it is generated and a quick and targeted response is possible in the event of a fault.

Meaningful KPIs from raw values

Ideally, the raw data is processed in the OT network; only the KPIs end up in the cloud.

© iba

Comprehensive data analysis is completed by connecting to higher-level IT systems such as cloud environments. This step exploits the full potential of the measurement data, as the insights gained can be made accessible to different user groups and used holistically. However, due to the immense amount of data, the available bandwidth and the interactions between OT and IT networks, it does not make sense to use all the measurement data for evaluation, but only to transfer the calculated characteristic values to the cloud. This data can then be displayed in meaningful dashboards using online visualization tools such as 'ibaDaVIS'. This allows operators to analyze and compare systems, machines and product quality and also identify potential for optimization. However, it is not enough to simply provide the key values; it is necessary to be able to drill down the KPIs in the cloud back to the raw values. This allows root cause analyses to be carried out in the event of errors without any loss of information.

The iba system

With the iba system, a system is available that supports the presented method of recording high-resolution raw data and edge analytics with its architecture and the various applications. Based on the structured data acquisition with central time stamping with ibaPDA and the comprehensive process connectivity available here, the raw data can be processed directly in the ibaDAQ or ibaM-DAQ edge devices. ibaPDA offers a printout editor for linking signals and calculating statistical parameters in real time.

The ibaInSpectra add-on is also available for online vibration analysis and the ibaInCycle add-on for monitoring cyclical processes, so that all relevant data can be recorded and monitored in one system. Alerts can be sent directly from the edge devices via various output interfaces. Finally, with ibaDaVIS, the iba system has a web-based analysis tool that can be used to create individual dashboards with different graphical elements to evaluate the long-term behavior of the monitored processes and product quality. Interactive and flexible filter options allow the characteristic values calculated in the edge device to be analyzed according to any criteria and technological and temporal filters.

Christian Reinbrecht, iba

© iba

The author :

Christian Reinbrecht is a product manager and expert for vibrations and state changes at iba in Fürth.