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Internet of Things (IoT) and ELMAS

Internet of Things (IoT) is a large and complex network that is based on monitoring and controlling devices through a network infrastructure. IoT allows to collect various types of measurement data from the devices and store it to a certain predefined data storage. The collected data is then refined in a way that it can be used to provide useful feedback for single devices and also for the operation of the whole system. To enable this IoT also includes different analytics that make it possible to analyze large amounts of data and to create practical actions based on the achieved results.

In industrial world the Internet of Things is often referred to as Industrial Internet of Things (IIoT) where the amount of data collected from the equipment is extremely large in many cases. These large data sets are also known as Big data and their size and complexity makes it hard to manage them. The challenging qualities of the data sets therefore create a need for more effective analysis methods and the requirements for these analytics are growing as IIoT is advancing in industry.

The analytics used in IIoT can be roughly divided into two main categories: real-time analytics and advanced analytics. Real-time analytics create for example control signals for the equipment based on the analysis results of their measurement and sensor data. Advanced analytics in turn is based more on preventive than reactive actions, and is used to develop the operation of the equipment in the long run by utilizing different data models and analysis tools.

Main processes of Industrial Internet of Things (IoT) and the role of ELMAS


ELMAS – Advanced analytics into Industrial Internet of Things

ELMAS is a modelling and simulation software specialized in reliability management and it operates in the field of advanced analytics when it comes to Industrial Internet of Things. Thanks to IIoT the amount and especially the quality of the collected data will be increasing and therefore significantly reducing the work effort required to use advanced analytics. Also ELMAS will benefit from the increased quality and accuracy of the available data as the need for reviewing the imperfect history data using various expert resources is no longer as critical as before.

Advanced analytics allow organizations to prepare for future events in a preventive manner (predictive analytics) and to create versatile calculation models for different improvement alternatives and use the results acquired from these calculations to develop the overall operations of the organization (prescriptive analytics). When operating with completed IIoT systems ELMAS provides assistance in understanding the consequences of different equipment events and event chains for the operation of the whole system. The cause-consequence models describing the equipment operations are created into the software and they utilize the history event data collected into the data storage. By simulating the models together with the event data ELMAS can provide a clear view on the expectable future behavior of the equipment considering their reliability and life-cycle costs. This way the available raw data can be processed into important knowledge about the most significant equipment risks and used to create profitability calculations about different investment options considered for the equipment or the whole process.

Creating basis for IIoT during startup

The startup phase of a new IIoT system can take years until the system is properly defined and have collected enough event data to support all of the analytics. This phase of waiting for new data streams doesn’t though mean that advanced analytics can’t be applied. For example ELMAS can use the already existing imperfect data with the help of local expert resources. This way it is possible to create the same improvement alternatives for the equipment as with the completed IIoT systems, but in addition to this the analysis also provides important feedback for the ongoing IIoT development.

With the results of ELMAS analysis it is for example possible to define which devices should be monitored to ensure the system operation and how much should be invested on the diagnostics on each device. Going through the old event history with the expert resources presents another major advantage by providing valuable information for the upcoming IIoT system. The information includes for example facts about the most problematic areas with the previous event data and gives understanding on what kind of data to collect in the future and in which form it should be transferred into the data storage.

All in all ELMAS brings more intelligence for the management and processing of the large data sets in the IIoT world. The analytics it provides allow to maintain and develop the reliability of the equipment and processes and therefore reducing the overall costs by improving the process efficiency. ELMAS software has a strong history in refining imperfect data to support the decision-making of Finnish industry organizations. The new data streams and the increased attention on data quality coming with the IIoT world further strengthens the possibilities ELMAS can provide for the development of operations.