INDUSTRY 4.0/IIOT
PRODUCTION PARADISE W
henever "Big Data" is mentioned, most people think of social media or the analysis of customer
behaviour in online commerce. However, strategic data analysis is also gaining momentum in the production environment. Frost & Sullivan believes that data analysis in the industrial sector has immense potential – production efficiency could be increased by about ten percent, operating costs could be reduced by almost 20 percent and maintenance costs could be minimised by 50 percent when focusing on utilising the data already in the production process. The issue for many factories today is that while the data is easily collected and stored, little happens after that, and important insights that are hidden in the available information are lost. In addition, there is often a lack of budget and personnel to devote to this task. But those who overcome these hurdles and focus on Industrial Data Science
will soon gain new insights and transform production into a data paradise. Channelling the huge flood of data and extracting value from the information collected by sensors, controllers and machines is undoubtedly a complex task, as it involves more than standard statistical methods and tools. Manual evaluations and the creation of dashboards and reports are not enough. One reason for this is that dashboards become increasingly complicated as data volume expands. They also don’t show relevant information at the right time so that an operator can see at a glance what is going on and take action. The routines implemented in a normal machine
THE PHASES OF APPROACHING DATA SCIENCE
Phase 1: Preparation - All participants and area experts deal with the problem or the specific requirement in order to arrive at a clearly defined project goal. This is carried out by analysing the machine and/or the production process in detail in order to gain an overview of which data is already available and which still needs to be collected. At the end of the preparation phase, a report is produced that provides insights into the expected generated value and a realistic ROI. Phase 2: Analysis and application development - The data is collected over a longer period of time in order to obtain a representative picture of the machine and process behaviour. Depending on the project objective, a data pipeline contains the following stages: Data collection, Data pre-processing, Data analytics, and Application. Phase 3: Evaluation - The application is used in the production environment where performance and business results are evaluated. If the performance does not meet expectations, the previous project phases are repeated. Phase 4: Service and maintenance - Production processes and machine behaviour are subject to constant change over time, due to updates or wear and tear. Therefore, a regular revalidation of the solution is necessary to ensure that it works realistically and retains its value. Also, existing (machine learning) models should be reviewed regularly.
Strategic Data Science is an essential pillar of every Industry 4.0 scenario. Here is a four-step data mining approach outlined by Omron...
control system for monitoring production processes and detecting errors can identify current deviations and problems. However, they are not able to predict future problems, link information in a meaningful way and perform advanced analysis.
The central task of data analysis in Industry 4.0 scenarios is to extract decision-relevant information from collected data and present it to the right user at the right time. This involves converting data into useful information before implementing it. The process requires close cooperation between data experts (data scientists) and specialists in production processes who know the story behind the data.
Data scientists are especially familiar with the “3 V’s" of large data sets: Volume, Variety and Velocity. A modern packaging machine, for example, can easily generate gigabytes of data per day that can be stored over a long period of time. For inspection machines, the systems may generate many terabytes each day. Storing this amount of data is not a problem, but using it is a challenge. Furthermore, machines today not only produce data, but the type of data is much broader than it was a few years ago – measured values stored, as well as raw information from sensors and other metadata. It is not only about maintenance results, but also associated images. Additionally, data can be generated by the machine operator. This includes cycle times and even written and spoken feedback. But that's not all: raw data from sensors is typically read every millisecond and must be
26 DECEMBER 2020/JANUARY 2021 | PROCESS & CONTROL
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