Posts Tagged ‘Data Flow’

Big Data in the Production Environment?

Friday, October 9th, 2015

Big Data has been one of the hyped topics in the IT field for some time. But what’s the situation in automation infrastructures? Is the handling of large amounts of data also becoming more significant in the industrial production environment?

In the recommendations for implementing Smart Factory, Big Data is named as one of the future technology requirements. But where are we now? Handling data amounts in petabyte dimensions is currently the exception in production. However, today there are already application scenarios that lead us towards very large amounts of data.

Typical Status Quo

When archiving production data, choices are made selectively in relation to which data is saved. Some data is automatically deleted again after certain time periods. With applications such as Energy Management, for example, a multitude of data points are often archived, but generally with a very rough granularity, which in turn keeps the amount of data low. In HMI applications, machine-orientated data is often saved at the panel directly and, at least in part, exported to central archives or databases.

Possible Scenarios

With Big Data in manufacturing, all relevant data in relation to the complete life cycle of a machine or plant can be saved. Based on this data, completely new possibilities for analysis arise. The operators of machines and equipment (manufacturing companies) can work with the data from their entire production estate. The machine and equipment manufacturers have data from a number of the same machines available for their evaluations.

The Benefit of Big Data

Manufacturing companies can uncover unexploited potential in equipment efficiency and effectiveness as well as quality management with Big Data analyses, which in turn increases the Overall Equipment Effectiveness (OEE). Another advantage is that it allows predictive maintenance. Optimized maintenance management has a positive effect on production costs and overall equipment effectiveness.

Machine and equipment manufacturers can also gain additional valuable findings in relation to improvements in construction using Big Data analyses which can, for example, consequently increase the manufacturing capacity and energy efficiency at the same time.

Big Data applications with zenon

Big Data applications with zenon

Big Data with zenon

zenon already offers possibilities for handling very large amounts of data. Data security, the rapid evaluation of data and ergonomic handling are the main focus. Users can freely choose whether they operate zenon purely on-premises or scale it with enhancements to the cloud. With version 7.20, zenon can be seamlessly integrated with the Microsoft Azure cloud platform.

In addition, we are working in both Product Management and Research & Development to make zenon even better at handling ever-larger amounts of data.

High quality data for high quality decisions

Tuesday, May 27th, 2014

Not only the speed of information is crucial, but also the quality of the data provided. Without guaranteeing a certain level of data quality, Business Intelligence is, in the best case, untrustworthy. Worse, it could lead to the wrong decision. Strategies to ensure high quality data start at field level, where sensor data is acquired.

Three criteria for data quality

  1. Enough, but not too much:
    Yes, the amount of data is a factor in data quality. Even if analytical tools are constantly evolving and in cloud applications hardware power is easily scalable, it is a lot easier to find the needle if you remove the haystack first.
  2. Consistent and complete:
    Applications in quality control are a good example for the importance of data consistency, whether in manufacturing, disaster prevention or electrical grid control. Reports may look fine, even when a critical value which, by chance, is out of range and is exactly the one missing from the database.
  3. Correct:
    It nearly seems too obvious to mention that data needs to be correct to serve for correct analyses. Often it is a quite complex challenge to deliver correct data from field level systems. Especially when underlying data is already a derivative of primary data, like availability times of production equipment or infrastructure components.

To ensure data quality, straton and zenon offer a wide range of capabilities for on-the-fly data preprocessing. Data is being checked and processed on different levels while it is being moved upstream.

With zenon and straton, on-the-fly data preprocessing is being performed on different levels.

With zenon and straton, on-the-fly data preprocessing is being performed on different levels.

The secret weapon for business intelligence and Big Data Applications

Thursday, May 22nd, 2014

straton is a flexible and powerful IEC 61131-3 environment. Many hardware manufacturers and system integrators rely on it to engineer great automation solutions and equipment. It is not a secret that straton shows off its strong points particularly in combination with zenon. What is not so obvious, however, is that straton is an excellent tool for Business Intelligence and Big Data applications. This blog entry will show you how excellent.

straton and zenon – Power Play for Business intelligence

Business Intelligence Solutions depend on having the right data available. As a basis for reliable analyses this data needs to be correct, complete and consistent. In industrial and infrastructure environments, this data originates on the sensor level. Through PLC systems this data can be communicated onwards through SCADA or gateway levels to finally end up in an archive or a database. When stored properly the data is retrievable and available for business intelligence tools (like zenon Analyzer) or for Big Data applications.

extended data sets for Big data applications

Traditional Big Data applications most often look at data sets ranging from ERP, CRM, Web 2.0 and market data to generate relevant insights. Including data sets from manufacturing and infrastructure (e.g. electrical grids, water supply and transportation) can substantially increase the power of Big Data analyses.

real-time data for real-time decisions?

When a PLC engineer and a C-level executive talk about real-time data they often have completely different things in mind. In the world of PLCs, the most common unit in this context is milliseconds. For a manager, ‘real time’ can mean having data from last week’s production available within a few days. How can we match these two mindsets and deliver real-time data about production and infrastructure to Business Intelligence levels? With straton and zenon you can easily realize architectures which allow you to obtain data from sensors at high speed. In practice, this means you can have critical information at the IT tier within seconds after it originated on the sensor.

From acquisition to reporting – the data flow for dynamic reporting with zenon, as used in ISO 50001 energy management (part 3: Reporting)

Thursday, August 8th, 2013

Now, as all relevant historical data is consistently stored in respective data archives, we are prepared to enter the next stage, namely to exploit the information in the context of general and specific trends. Means to comprehensively access (centrally or distributed) information sources is, once more, key. Moreover, the required values have to be selected, matched and interrelated in order to generate meaningful Key Performance Indicators (KPIs). This is where zenon Analyzer comes into play. Its seamless integration into the overall data flow of a zenon application builds the foundation of a meaningful and reliable reporting system at the transition point between production and corporate management level.

What does this look like in practice?

In applications like ISO 50001 energy management it is beneficial to collect consumption related data centrally, e.g. on a dedicated resource consumption server. However, in order to relate energy consumption with general productivity figures (e.g. OEE), it is typically also necessary to relate series of measurements and key figures from separate sources. For example, the ISO 50001 standard recommends the determination of so called Energy Performance Indicators (EnPI). Examples range from the simple caluculation of “consumed electricity per shift and/or equipment unit” to more demanding calculations, such as “electricity costs for the provision of air pressure, related to each produced unit”.

A system for a high-performance and reliable determination of EnPIs facilitates internal and external performance reviews and benchmarking.

zenon Analyzer offers comprehensive functionalities to generate, visualize and deliver reports. In the context of a zenon application, a variety of report templates can be directly applied. This means that specific attributes of variables, such as related equipments, shifts or administrative units like cost centers, can be used directly for filtering and statistical evaluations. A data-access architecture based on abstracted connectors allows access to both realtime SCADA data as well as historical values and database archives. Mechanisms of report synthesis are largely based on the Microsoft Reporting Services technology, including web-based configuration and user interfaces. Stored procedures and user-defined functions are integrated into the data preparation processes in order to allow for high-performance reporting cycles.

Quite naturally, reports can be automatically generated, e.g. depending on a fixed schedule, in the case of specific events or when particular values are exceeded. Respective actions simply have to be configured, so that a user can find the according document (PDF, Excel etc.) in the defined folder or document archive.

The ability to integrate information from various data sources into a consistent and clearly arranged data flow is fundamental with respect to advanced energy data management.

In all stages, flexibility is required to integrate various sources of information and to preserve sufficient space for new ideas and approaches. This seems to be crucial for companies to be able to retain the technical and analytical capabilities to support their business and, ultimately, to maintain their competitive position.

From acquisition to reporting – the data flow for dynamic reporting with zenon, as used in ISO 50001 energy management (part 2: Data Archiving)

Thursday, August 1st, 2013

In the preceeding part we discussed the possibilities for data acquisition with zenon. Now let´s assume that the desired variables are available in the zenon Runtime context. A number of data processing, visualization and control features can be utilized in the context of ISO 50001 energy management. For example, variable values can be pre-processed and limited, automatically scaled or monitored. Respective violations and inconsistencies are captured and – if desired – trigger specific reactions, such as control interventions, warnings or alarm messages.

One particularly important task with respect to ISO 50001 energy management and the related reporting is data archiving.

ISO 50001 Overview

Organize and retain your data

In zenon, data archives can be composed freely from any number of type-independent variables (i.e. binary, numerical, string). Values can be stored on change, cyclically or on event. System functions to activate and deactivate logging allow further customization of the archiving system; for instance, to use it in batch-oriented production environments.

Another important feature is the flexible creation of archive structures. Hence, in zenon, data from central archives can be automatically transferred to other archives. This facilitates data aggregation from archives with high frequency granularity into summary or trend archives.

Archive Cascading Example

The archiving mechanisms of zenon are transparently integrated into the overall data flow of an application. Thus, archive data – no matter if it is kept locally in files, on hard-disk or remotely on an SQL server – remains accessible for zenon´s functional modules, e.g. trend visualization or event parsing. Moreover, the archiving features fully comply with zenon´s redundancy standards and are protected against data consistency issues or unauthorized access. Exporting data into open formats such as XML, CSV or dBase is possible for any archive.

Exploit your data

zenon Analyzer is able to access these archives directly and to generate reports from the historic data, utilizing various filtering and data aggregation features. In the next post we will explore this in more detail.

From acquisition to reporting – the data flow for dynamic reporting with zenon, as used in ISO 50001 energy management (Part 1: Data Acquisition)

Thursday, July 25th, 2013

What is it all about?

A variety of performance improvement initiatives in the field of industrial production use zenon´s cutting edge technology to manage respective data volumes. For ISO 50001 energy management, this specifically relates to the monitoring and evaluation of corporate energy consumption.

This article will give you an insight into how data handling mechanisms are arranged by zenon in order to generate a consistent data flow, starting with data acquisition, followed by data archiving and reporting.

Generic Data Acquisition

The industrial IT and automation environment comprises a large amount of entities to monitor and control specific equipment, such as machine, process or building automation equipment, as well as business related systems (ERP, CAQ etc.). Data interfaces can range from well-defined and standardized (i.e. branch related) to proprietary communication mechanisms.

The ability of zenon to hook up to those entities is based on four pillars:

  • Direct out-of-the-box support of a large variety of communication standards and protocols
    More than 300 communication protocols can be utilized right away with zenon, with minimum development effort. All 300 are well integrated and tested. If a 3rd party product supports the generation of configuration data, i.e. variable list export, zenon can import this data accordingly to reduce implementation time and potential mistakes.
  • Flexible network technology
    Application design using zenon allows the use of powerful network design features, such as multi-server/multi-client or redundancy. Moreover, the system can be altered at any point in time, without indepth networking know-how. Hence, it can be easily modeled, according the best fit with regard to the coverage and the logical arrangement of information sources on all levels.

If the integrated communication features of zenon are insufficient, one has the option to use predefined programming interfaces in order to realize specific data links. This will require some programming.

  • Integrated scripting module (API)
    Script modules can be embedded as an integrated part of a zenon project. These establish a link between the zenon Runtime application and the “external world”. By means of VBA, .NET or C# scripting the developer can, using a COM interface, create a customized bridge between zenon Runtime and external components (e.g. SQL database).
  • Modular integration of communication drivers (“driver kit”)
    The integration of communication protocols, as described above, is based on a well-defined interface which connects a communication driver module (Windows .exe or dll) with the central communication management module of zenon (Dispatcher). This interface is open to use for a modular implementation of any further communication standard.

Preserve your flexibility

Typically, there are multiple sources of data within the industrial environment which need to be accessed. It is important for the practitioner to consider the limits of any system before specification, in order to ensure the system deployed can support possible future extensions to the system as well as current requirements.

In the next part we will have a look into the task of data-archiving, typical requirements and how zenon manages this.