How can measurement data be used across departments for analysis and optimization in industrial plants?

Cross-departmental use of measurement data requires a centralized, high-resolution acquisition across all process domains. With ibaPDA, process, control, and sensor data are captured synchronously and consolidated into a unified data base, preventing isolated data silos. iba HD server enables long-term storage and flexible evaluation for different user groups. In addition, tools such as ibaAnalyzer and ibaDaVIS support role-based analysis and visualization down to raw data level (drill-down capability).

How can end-to-end process connectivity be implemented in complex industrial plants?

The broad process connectivity of the iba system is achieved by integrating heterogeneous data sources such as PLCs, fieldbus systems, sensors, and cameras into a unified measurement system. ibaPDA provides extensive interfaces, including EtherNet/IP, PROFINET, and other industrial protocols, to connect diverse automation environments. Video data can also be integrated via ibaCapture and synchronized with process signals. This creates a time-synchronous, vendor-independent holistic view of the industrial process.

How does edge analytics work for real-time evaluation of process and machine data?

Edge analytics processes high-resolution raw data directly at the source, enabling time-critical decisions without cloud latency. With devices such as ibaM-DAQ or ibaDAQ, key performance indicators can be calculated directly at the edge and monitored via ibaPDA. Methods such as threshold monitoring, online frequency analysis, or TSA enable real-time detection of deviations and wear. This allows immediate alarm generation and proactive process adjustments.

How can industrial data be efficiently stored and used for long-term analysis?

For long-term analysis, measurement data is stored in a historical data server (HD server), either time-based or event-driven. With ibaPDA, data is acquired in high resolution and in context, enabling both trend analysis and product-related evaluation. Combined with ibaAnalyzer, it supports in-depth post-analysis including drill-down to raw data. This enables quality verification, root cause analysis, and long-term process optimization.

How does the integration of edge, IT, and cloud systems support industrial data strategies?

Combining edge processing, centralized data storage, and IT/cloud integration enables an end-to-end data value chain. While ibaPDA and edge components such as ibaDAQ compute KPIs directly at the process level, aggregated values are transferred to IT systems or cloud platforms. Raw data remains locally available and can be retrieved for detailed analysis via drill-down when needed. Tools such as ibaDaVIS also provide dashboard-based visualization for different user groups.

Unlocking the Full Potential of Measurement Data: A Value Creation Approach Through Digitalization

Maximizing the Use of Measurement Data

Different departments—such as maintenance, production, technology, and quality management—often follow different work habits and procedures, leading to untapped synergy potential.

These synergies can only be realized when employees establish a common ground for communication. A proven approach here is the recording and analysis of process data. However, this step also requires the introduction and implementation of a unified and holistic digitalization strategy.

Measurement data should not belong to individual departments or be recorded for a single purpose and then hidden in departmental data silos. Instead, they must cover the entire process and be utilized across departments. Only then can the full potential of the measurement data be unlocked.

High-Resolution and Comprehensive Data Collection

A key prerequisite is the nature of data collection: Only when data are recorded as high-resolution raw values, rather than pre-aggregated, can Key Performance Indicators (KPIs) be flexibly calculated according to user and task-specific aspects and then related to each other. To analyze the causes of unusual behavior patterns without losing information, and thus improve process understanding, high-resolution raw data must remain easily and quickly accessible from the KPIs via drill-down. This creates a company-wide and unified data basis, simplifying discussions and enhancing cross-departmental collaboration.

To ensure that all departments can collaborate effectively when implementing the digitalization strategy, it is essential to capture the entire process. Individual measurements, such as vibration or energy data, must not be recorded separately but instead made available in a unified data set along with all other process data. This approach allows for the identification of correlations between production, processes, and any wear-related effects, which is crucial for future optimization efforts.

Process Connectivity as a Crucial Element

The heterogeneity of automation components within manufacturing plants presents a significant challenge for measurement systems. The more complex the processes, the greater the number of interacting signal sources and automation systems. In addition to programmable logic controllers, sensors, and bus monitors, cameras can also be used as data sources. Especially when no sensors are available for critical process variables, or they cannot be installed due to cost or space constraints, time-synchronized video data provides valuable information. Comprehensive process connectivity is thus a crucial element of a powerful measurement system: All data, regardless of manufacturer, device generation, or data format, can be captured isochronously and with high resolution, offering users a global and non-intrusive view of the technical process.

Long-Term Data Recording and Analysis

To analyze measurement data over a long period and thus identify trends or demonstrate compliance with quality requirements, the data must be efficiently stored. This is achieved using a historical data server (HD server), with recording triggered either by events or time-based, depending on the need. Event-triggered data recording is ideal when only signals related to a specific workpiece should be captured in a file. The start and stop conditions can be customized and derived directly from the recorded signals, making product-specific evaluations easier and improving the comparability of similar products.

Large datasets can be interactively searched and analyzed, and utilized for key figure development and report generation. The actual increase in value comes from the analysis of the measurement data. By applying the described methods and techniques for data acquisition and recording, it is ensured that the data can be used by various user groups, such as maintenance teams, process engineers, production and quality managers, as well as data analysts.

In addition to interactive offline analysis and the calculation of key metrics for abstracting, evaluating, and documenting the process, the role of automated real-time analysis is becoming increasingly important for the early detection of process deviations, especially in the context of proactive maintenance.

Automatic Online Evaluation

Automatic online evaluation allows real-time proactive monitoring of a process to detect and avoid process errors early or to improve product quality. This type of evaluation is also known as edge analytics, as the measurement data are processed directly at the edge, between the production and IT networks—exactly where the data are generated.

Here are four detailed examples of edge analytics:

Threshold Monitoring Signal values can be monitored in the acquisition system for specific threshold limits. If these limits are exceeded or not met, an alarm is triggered. In addition to raw signals, the signals can also be aggregated, linked, or averaged, and the threshold check can be conditioned by specific parameters. For example, a threshold check can be performed only when the machine is in automatic mode.
Machine Vision Applications Machine Vision (MV) applications allow numerical values, text, or classified information such as "material present/not present" to be extracted from video data. Since video data is captured synchronously with process data and the MV information is available in real-time, the results of the MV application can be treated like sensor values and recorded in the measurement system. With MV, products can be measured, material tracking can be realized, or the process can be monitored.
Online Frequency Analysis for Vibration Monitoring If the vibration behavior of a process is recorded using IEPE sensors, the necessary frequency domain analysis can be automatically performed directly on the edge device. Frequency bands to be monitored can be defined either as fixed or dependent on process values and checked for threshold violations. Process vibrations are thus detected in real-time, and an alarm is triggered in case of a fault.
Monitoring with TSA and Auto-Adapting The Time Synchronous Averaging (TSA) method can monitor a process based on already captured measurement signals, such as temperature, pressure, or motor current. Based on the signal progression, both slowly emerging process deviations, for example due to wear, and sporadic anomalies can be detected early, and their effects on product quality and machine condition can be reliably predicted. In the time domain, wear indicators are calculated online, and alarms are triggered in real-time when thresholds are exceeded. This allows users to make timely adjustments, preventing machine or system malfunctions, negative impacts on product quality, and unplanned downtime. The process behavior can be automatically learned for different conditions using the Auto-Adapting method.

Conclusion

The key to increasing value through digitalization lies in the multiple and shared use of measurement data. Only when the digitalization strategy is implemented collaboratively across all departments can cross-departmental synergies be realized.

To achieve this, appropriate tool support is essential, such as the solutions provided by the iba system. This ensures that access to the data is simple and intuitive, allowing each user group to independently carry out analyses tailored to their specific needs.

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Frequently Asked Questions (FAQ)

How can measurement data be used across departments for analysis and optimization in industrial plants?: Cross-departmental use of measurement data requires a centralized, high-resolution acquisition across all process domains. With ibaPDA, process, control, and sensor data are captured synchronously and consolidated into a unified data base, preventing isolated data silos. iba HD server enables long-term storage and flexible evaluation for different user groups. In addition, tools such as ibaAnalyzer and ibaDaVIS support role-based analysis and visualization down to raw data level (drill-down capability).
How can end-to-end process connectivity be implemented in complex industrial plants?: The broad process connectivity of the iba system is achieved by integrating heterogeneous data sources such as PLCs, fieldbus systems, sensors, and cameras into a unified measurement system. ibaPDA provides extensive interfaces, including EtherNet/IP, PROFINET, and other industrial protocols, to connect diverse automation environments. Video data can also be integrated via ibaCapture and synchronized with process signals. This creates a time-synchronous, vendor-independent holistic view of the industrial process.
How does edge analytics work for real-time evaluation of process and machine data?: Edge analytics processes high-resolution raw data directly at the source, enabling time-critical decisions without cloud latency. With devices such as ibaM-DAQ or ibaDAQ, key performance indicators can be calculated directly at the edge and monitored via ibaPDA. Methods such as threshold monitoring, online frequency analysis, or TSA enable real-time detection of deviations and wear. This allows immediate alarm generation and proactive process adjustments.
How can industrial data be efficiently stored and used for long-term analysis?: For long-term analysis, measurement data is stored in a historical data server (HD server), either time-based or event-driven. With ibaPDA, data is acquired in high resolution and in context, enabling both trend analysis and product-related evaluation. Combined with ibaAnalyzer, it supports in-depth post-analysis including drill-down to raw data. This enables quality verification, root cause analysis, and long-term process optimization.
How does the integration of edge, IT, and cloud systems support industrial data strategies?: Combining edge processing, centralized data storage, and IT/cloud integration enables an end-to-end data value chain. While ibaPDA and edge components such as ibaDAQ compute KPIs directly at the process level, aggregated values are transferred to IT systems or cloud platforms. Raw data remains locally available and can be retrieved for detailed analysis via drill-down when needed. Tools such as ibaDaVIS also provide dashboard-based visualization for different user groups.