Implementation of predictive maintenance systems
IIoT solutions with Siemens MindSphere / Azure / Splunk
Sudden, unforeseen machine failures cause performance drops, delivery bottlenecks and can bring an entire process to a standstill. Breakdowns are not only time-consuming and costly, but can also weaken your competitive advantage. By implementing predictive maintenance in your infrastructure and monitoring and analysing real-time and historical machine data, you can detect changes in condition at an early stage and thus reduce unplanned downtime to a minimum. The evaluation of the collected machine data also makes it possible to initiate maintenance processes precisely when they are most likely to be necessary.
in Siemens MindSphere
MindSphere is the cloud-based IoT operating system from Siemens that enables you as a manufacturer or operator of machinery to connect your products, plants, systems and machines and to use the data read and obtained from the IoT network for extensive analyses.
in Microsoft Azure
Overall Equipment Efficiency (OEE) is becoming increasingly important in the operation of industrial plants. Generate insights into how well your assets are performing in relation to their planned capacities with Microsoft's Azure IoT platforms. The end-to-end IoT solutions not only allow you to monitor your equipment, but also provide tailored analytics to optimize your business processes and use cases.
in Splunk
Splunk Industrial Asset Intelligence delivers real-time predictive analytics that enable users to proactively optimize operations and improve performance. Splunk collects, analyzes and visualizes real-time and historical machine data from any source - including sensor data, OT-connected assets and products to create a simple, real-time view of complex industrial data. www.industry-monitoring.com
As a system integrator, we integrate your machines into Siemens MindSphere, Microsoft Azure or splunk> and, if required, develop a solution that is specifically tailored to the needs of your business environment.
With the help of the Ewon multi-protocol routers from HMS Networks Solutions and its sales partner Wachendorff, we can establish a connection to almost any machine. If a machine does not want to talk to us, our embedded software developers will develop a suitable interface.
Data collection
The basis for a successful predictive maintenance system is data generation and collection. Our team ensures that you receive the data from your machine that is necessary and useful for optimized operation. We have experts in the fields of embedded programming and application development. In the hardware sector, we work together with well-known manufacturers such as HMS Industrial Networks and Wachendorff.
Data collection
The enormous amounts of data collected need to be stored in a central location. We advise you on which cloud-based system architecture is best suited to your use case and set it up together with you.
Visualization
After the automated plausibility check and cleaning of the data, it is ready for visualization. The visual processing of the data enables clear monitoring and analysis of real-time data. Changes in the condition of the machines can therefore be detected before the fault or failure occurs. We support you in the implementation and use of the leading data storage and visualization platforms Siemens Mindsphere, Azure, splunk and develop tailor-made dashboards and apps for you.
Forecasting and artificial intelligence
Reliable forecasts can be derived from the collected data using statistical and self-learning methods. These are used to draw the necessary conclusions regarding the maintenance of machines. Our mathematicians will advise you on the appropriate statistical methods for your application and support you in implementing them.
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