In Industry 4.0, we use predictive maintenance to facilitate planned servicing of machines and minimise downtime.
We use predictive maintenance methods to help our customers predict the future condition of their machines and equipment. This enables them to take proactive steps to minimise downtime, increase productivity and customer satisfaction, and offer new service products.
We’ll use our expertise in the Internet of Things (IoT), data engineering, and data science, as well as mathematical modelling, to develop predictive maintenance solutions and integrate them into your operational systems – from edge computing to cloud and hybrid solutions.
We’ll accompany you through every stage of your data-driven transformation. Our customer solutions are tailored to the maturity level of your systems and include:
We’ll work with you to build data-driven mathematical models that offer real added value for your company and its customers, and we’ll support you throughout the development cycle of your predictive maintenance solutions.
Do you know where you want to go, but you don’t know how to get there? We’ll support you in the development of new product ideas.
We’ll carry out feasibility analyses and provide proofs of concept to give you initial insights into the feasibility of your product vision and what it could look like in practice.
We’ll train machine learning models which will enable you to reliably detect and predict malfunctions in your devices.
After the planning phase, we’ll seamlessly integrate your chosen predictive maintenance solution into your systems and business processes – for a tailor-made product rather than an “off the shelf” one.
Every project and every customer need is unique. Of-the-shelf solutions often feel like a compromise and are rarely successful. Our many years of project experience enable us to quickly familiarize ourselves with specific domains and identify important requirements. Our process model for the development of predictive maintenance solutions is based on incremental development. This method is designed to quickly generate added value and to learn from data and users. Even the initial dashboards and threshold-based alerting solutions are a first and important step towards creating a predictive maintenance solution.
Our approach enables you to quickly generate added value for your company, even if (whether due to few failures or few machines) you only have a small volume of data – without compromising on quality.
As part of the Service Meister (Service Master) research project, KROHNE Messtechnik GmbH and inovex designed a system for SMEs that uses machine learning to automatically detect and evaluate anomalies in measuring instruments.
We supported a commercial kitchen manufacturer in setting up a reporting system that continuously monitors equipment data to evaluate the company’s operations and enables processes to be further optimized.
We worked with an equipment manufacturer to implement continuous monitoring of the equipment data and the display of that data for end users. We also made it possible to detect anomalies in equipment operations and set up alerts. This, in turn, enables the factors affecting equipment operations to be determined.
Listen to the Predictive Maintenance episode of the Digital Future Podcast by inovex.
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