At IPN we are committing ourselves to one of the core topics of industrial maintenance and offer you an intelligent product to plan your maintenance tasks based on the future state of your machinery.
The future state and maintenance requirements of your machines are predicted in real time. This means that unplanned downtimes and outworn tools belong to the past and you can focus on your core business, production.
Your production will not be interrupted by unexpected downtimes, missing spare parts and availability of qualified technicians. You enter lead times of spare parts to the system and our product plans the optimal time for maintenance work regarding machine priority, failure severity and production plans.
The predictions are of increasing accuracy and automatically detects new events. This is possible because our system gathers machine and process data and therefore learns about your machines with every data record it receives.
Every company has a set of different production processes with different machine priorities, so does yours. You can define risk profiles for your processes and machines in order to reflect the importance of your machine according to your specific needs. Our product treats bottleneck machines more conservativly when creating it’s maintenance schedule in order to reduce the risk of unplanned downtimes.
In order to continuously improve machines and processes, companies invest a lot of time and money into failure analysis. Our product informs you about those sensors and parts of your machine which are the drivers of these problems. This will highly improve your continuous improvement process.
OFFSHORE – WIND ENGINES:
During the planning and the construction of offshore wind parks, the optimal and most efficient maintenance strategy is of big concern. The most common strategy is the one of preventive maintenance in alignment with the manufacturer’s specifications. This strategy imposes immense costs for the operator of the wind park as it means that at least three highly qualified technicians have to be carried by ship to the wind engines. These technicians have to spend a whole working day at the wind engine to perform maintenance tasks regardless of the actual severity of the interruption. At the end of the day the technicians are shipped back to the mainland. Another cost driver is the root cause analysis in the case of interruptions. In this case an alarm is triggered in a central control station and a technician has to manually identify the cause from the sum of all sensor data (e.g. wind speed, rpm, oil-level, etc.). After identifying the cause, this technician triggers a malfunction message and the necessary repairs are being planned.
Our product enables the operator to switch from a preventive maintenance strategy to a predictive strategy and allows for an automated root cause analysis. In order to do so, our product permanently monitors and archives all the process and sensor data from the wind engines. This data is analysed for previously identified failure indicating patterns in real-time. Based on the patterns found it predicts the time to failure and the timeframe to perform maintenance work to prevent the failure. The result is a dynamic maintenance schedule based on the real demand (time and type of failure). In case of a failure the malfunction information is automatically sent to the technicians including a root cause analysis based on the available data. The output for the operator is clearly an increased availability of the wind engines together with reduced costs for maintenance and repair work.
After sales service is one of the core business areas for machine manufacturers. In order to be considered as a supplier for producing companies they have to guarantee a minimum machine availability and a maximum response time in case of a malfunction. This means that they have to keep all the spare parts and qualified technicians on call which implies high overhead and direct costs. As a response, many manufacturers offer a preventive maintenance strategy that builds on performing maintenance tasks within fixed intervals. These intervals can be based on actual use e.g. ‘replace component X every 100.000 cycles of operation’ or on time e.g. ‘replace component A every 3.000 machine hours’. Although this strategy increases the predictability of maintenance needs, it is ignoring the actual future state of the machine leading to high costs for both the provider and the operator. This comes from the fact that following this strategy maintenance tasks are performed either too early (high planned downtimes, high costs for unnecessary spare parts) or too late (machine breakdowns).
Our product enables the manufacturer to plan the maintenance work based on the actual future state of the machines sold to its customers. It permanently informs the manufacturer about the as-is state of the machine and the predicted time of a failure. With our product, manufacturers are able to provide the necessary technicians and spare parts just in time and just when they are needed. This cuts the costs of the manufacturer as wells as its customer.
We are happy to serve you with an individual offer and are looking forward to answering your questions in a personal call.