Pump Maintenance Evaluation Using Wear-Based Failure Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to accurately and reliably assess the wear state of machines, particularly those with rotatable components, due to numerous influencing factors, making early detection of wear and component faults challenging and leading to potential machine failure.
Innovation Solution
A method that determines relevant influencing variables, such as operation-related and operation-independent factors, and uses an estimation model, potentially with a machine learning algorithm, to predict the risk of failure, enabling timely maintenance recommendations based on the likelihood of failure calculated from these variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to assess machine wear state, then the system is simple to operate, but the measurement precision and reliability of wear assessment deteriorates due to numerous influencing factors
Solution Approach 1:
The patent segments the wear assessment system into multiple independent components: sensor modules for collecting different types of data (vibration, temperature, pressure), an evaluation unit for processing signals, and a database for storing historical data. This segmentation allows each component to specialize in specific functions, improving overall measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The evaluation unit serves as an intermediary between the sensors and the maintenance decision-making process. It processes raw sensor signals, applies algorithms to assess wear state, and generates maintenance recommendations. This intermediary layer filters out noise from numerous influencing factors and transforms complex sensor data into reliable wear assessments.
2Reliability
If comprehensive monitoring of all machine components is implemented, then the reliability of failure prediction improves, but the resource outlay and system complexity increases significantly
Solution Approach 1:
The patent applies local quality by focusing monitoring resources on specific critical components and parameters most relevant to wear assessment, rather than uniformly monitoring all machine components. The system identifies key wear indicators and concentrates sensing and processing capabilities on those specific areas, improving failure prediction reliability while reducing overall resource consumption.
Solution Approach 2:
The system implements partial monitoring by selecting a subset of critical parameters and components for detailed surveillance, rather than comprehensively monitoring everything. This partial action approach captures the most significant wear indicators while avoiding the excessive resource outlay that would result from complete system-wide monitoring.
3Productivity
If manual wear assessment methods are used, then the system complexity is low, but the productivity and timeliness of maintenance decisions deteriorates
Solution Approach 1:
The system implements self-service by automatically collecting sensor data, processing signals through the evaluation unit, assessing wear state, and generating maintenance recommendations without requiring continuous manual intervention. This automation dramatically improves the speed and productivity of maintenance decisions while the modular architecture manages system complexity.
Solution Approach 2:
The system incorporates feedback loops where sensor data continuously flows to the evaluation unit, which processes the information and generates maintenance recommendations that are fed back to operators. This continuous feedback mechanism enables real-time monitoring and rapid maintenance decision-making, significantly improving productivity compared to manual periodic assessments.
Data Source
AI summary
A method for evaluating a necessary maintenance measure of a machine includes determining one or more influencing variables, receiving the one or more influencing variables, ascertaining a risk of failure and/or a likelihood of failure, and generating a recommendation. The one or more influencing variables are relevant to the wear or damage of a machine component. The one or more influencing variables are received by way of the evaluation unit. The risk or likelihood of failure are ascertained by way of an estimation model. The recommendation is associated with a maintenance measure and is generated by way of the evaluation unit on the basis of the ascertained risk of failure and/or the likelihood of failure.