Intelligent fault detection & maintenance planning
Detect faults early and implement a proactive maintenance plan to avoid unscheduled downtime
Faults in plant and machinery are a major problem for many manufacturers and customers. They may not occur frequently, but they are often detected too late leading to unscheduled downtime. The resulting maintenance operations can be lengthy and inefficient. This in turn can lead to significant extra cost, unhappy customers and in the worst case, considerable damage to your reputation.
With our intelligent fault detection, you can monitor your plant and machinery in real time so that faults and breakdowns can be detected or predicted early on. This allows you to implement a proactive maintenance plan for increased efficiency and lower costs.
We start with your challenges
These are the most common challenges involved in achieving intelligent planning of your maintenance processes:
- Networking and management of the machines, e.g. via the use of suitable IoT technologies.
- Efficient processing and storage of machine data, e.g. in a cloud-based application.
- Monitoring the condition of the assets, e.g. through the use of suitable dashboards.
- Selecting and using suitable methods to detect and predict faults, e.g. rule-based or machine learning methods to analyse high-frequency vibration data.
- Plan maintenance in advance, e.g. through automated notifications.
- Integrate external data sources, e.g. by using standardised APIs for ERP or MES systems.
Our solution: Intelligent fault detection and maintenance planning
Our approach to intelligent fault detection
Example: High-frequency vibration data is measured in real time on a networked ball bearing and sent to a backend provided by us. The data is analysed using a machine learning model. Any damage to the ball bearing is thus detected at an early stage and with a high degree of accuracy. This allows an intelligent, proactive maintenance plan to be set up before a fault or breakdown occurs, avoiding lengthy downtimes. Depending on the given framework conditions and the data available, simpler approaches for condition monitoring or rule-based analysis, for example, can be implemented in the first step if required and then enhanced later in a second step to implement predictive AI algorithms. This can be the first step towards intelligent fault detection
How we support you with predictive fault detection and maintenance planning
- Connect: Remote device management and administration, integration of third-party systems, user management and user personalisation
- Maintain: Efficient processing of large data volumes and real-time data, rule-based device condition monitoring with customised dashboards, condition-based monitoring, AI-based fault detection with sophisticated machine learning methods
- Consult: Exploration and requirements gathering, introduction / installation & setup / customising / project planning, training, operations and support, consulting services regarding the introduction of new, billable business models and digital services
Results and artefacts:
- You get a custom-built software solution with which you can monitor plant and machinery, detect faults early, and implement a proactive maintenance plan.
- The software is cloud ready but can also be provided as an on-premises solution
What we bring to the table:
- Multiple years of project experience in the connected things environment of mechanical and plant engineering.
- We know from numerous predictive maintenance projects which processes, methods, technologies and best practices are best suited to your needs.
- We have established partnerships (Azure Gold Partner, PTC Partner, and so on).
- We offer professional project management using agile methods and prefabricated software artefacts for a shorter time to market
- We have extensive experience in implementing new, billable digital services.