Predictive Maintenance Planning Using Sensor Pattern Matching
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Solution Overview
Problem
Current maintenance planning for machines is inefficient, leading to unnecessary downtime and potential damage due to inadequate monitoring and resource management, as existing systems rely heavily on large data volumes and manual rules for error diagnosis and condition monitoring.
Innovation Solution
A device and method that utilize sensor data to compare with model data patterns, selecting maintenance models and resources based on conformity, leveraging machine learning and AI for predictive maintenance, ensuring timely and resource-efficient maintenance operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rules and large data volumes are used for error diagnosis and condition monitoring, then reliability of maintenance planning is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-defining maintenance models with maintenance steps and resources, and model data patterns representing normal and abnormal machine states. These are stored in advance in the database, so when sensor data indicates a deviation, the corresponding maintenance plan is already prepared and can be immediately selected, improving reliability without requiring complex real-time analysis
Solution Approach 2:
The system creates simplified copies of machine operating states through model data patterns that represent normal and abnormal conditions. Instead of analyzing raw sensor data directly, the system compares sensor data against these pre-defined pattern copies, reducing computational complexity while maintaining diagnostic reliability
2Reliability
If maintenance work is carried out regularly and in time, then machine reliability is improved, but unnecessary downtime and loss of productivity increase
Solution Approach 1:
The system dynamically adapts maintenance planning based on actual machine condition. Instead of fixed periodic maintenance schedules, the system continuously monitors sensor data, compares it with model data patterns, and adjusts maintenance timing according to the actual state of the machine. This allows maintenance to be performed only when necessary, maintaining reliability while avoiding unnecessary downtime
Solution Approach 2:
The system implements feedback by continuously monitoring sensor data and comparing it with model data patterns to detect deviations indicating abnormal machine states. This feedback loop enables the system to adjust maintenance planning in real-time, ensuring maintenance is performed based on actual machine condition rather than fixed schedules, thus optimizing both reliability and productivity
3Reliability
If resource availability is checked and requested for maintenance steps, then maintenance execution reliability is improved, but planning time and process complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-defining maintenance models that include all necessary maintenance steps and their required resources. Resource availability is checked in advance against the resource database, and resources are requested beforehand. This preliminary preparation ensures that when maintenance is needed, all resources are already confirmed available, improving execution reliability without adding significant planning time during critical moments
Data Source
AI summary
A device and a method for planning of maintenance work on a machine. Based on sensor data S, input data E are generated. The input data E are compared with model sensor data of different model data pattern and are checked regarding conformity. Each model data pattern is assigned a maintenance model in the model database. If a conformity of input data E with model sensor data M of a model data pattern has been determined, the assigned maintenance model can be selected for a maintenance of the machine. The maintenance model comprises at least one maintenance step and the resource required for it to be carried out. Subsequently, the availability of the required resource is checked and the resource is requested, if available. By such a device or method, a predicted maintenance can be performed based on empirical knowledge contained in the maintenance models of model database.