Predictive Maintenance for Biological Sample Analyzers
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Solution Overview
Problem
Current methods for maintaining biological sample analyzers, such as blood analyzers, lack accurate and reliable prediction of component malfunctions, often resulting in unplanned downtime and unnecessary maintenance.
Innovation Solution
A computer-implemented method that receives measurement results from biological samples and quality control samples to compute a time series of performance parameters, extrapolate future values, and determine an estimated failure time using a predetermined model, allowing for predictive maintenance without additional measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic preventive maintenance is performed, then apparatus reliability is improved, but unnecessary maintenance and operational downtime increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring performance parameters and extrapolating future trends to predict component failures before they occur. This allows maintenance to be scheduled at the optimal time - just before failure - rather than using fixed periodic intervals, thereby preventing unnecessary maintenance while ensuring reliability.
Solution Approach 2:
The system implements feedback by continuously measuring actual performance parameters, comparing them against predicted values, and using the deviation information to refine failure predictions. This closed-loop feedback mechanism enables dynamic adjustment of maintenance timing based on actual apparatus condition, reducing both unnecessary maintenance and unexpected failures.
2Loss of energy
If corrective maintenance is performed only when errors are detected, then maintenance costs are reduced, but unplanned downtime and extended operational disruption increase
Solution Approach 1:
The system performs preliminary failure prediction by extrapolating performance parameter trends, allowing maintenance to be planned in advance rather than reacting to unexpected failures. This transforms corrective maintenance into scheduled maintenance, eliminating unplanned downtime while maintaining cost efficiency by performing maintenance only when actually needed.
3Measurement precision
If additional measurements are performed for maintenance prediction, then prediction accuracy is improved, but measurement complexity and operational burden increase
Solution Approach 1:
The system achieves multi-functionality by using the same measurement apparatus and procedures that are already part of normal operational testing to collect data for both operational validation and maintenance prediction. This eliminates the need for separate dedicated maintenance monitoring equipment, maintaining prediction accuracy without increasing measurement complexity.
Solution Approach 2:
The apparatus performs self-service by automatically collecting and analyzing its own performance data during normal operation. The system uses its existing measurement capabilities to monitor its own health status, eliminating the need for external specialized measurement systems and reducing operational burden.
Data Source
AI summary
A computer-implemented method for supporting maintenance of an apparatus for analyzing biological samples, the apparatus including at least one hardware component subject to maintenance, the method comprising: receiving a plurality of measurement results, each measurement result being derived from a measurement performed by the apparatus for analyzing biological samples on a biological sample or on a quality control sample, the plurality of measurement results including measurement results obtained at different times; computing a time series of a performance parameter from the plurality of measurement results, the performance parameter being indicative of an operational state of said at least one hardware component, the time series representing a period of time having a start time and an end time, the end time being no later than a current time; extrapolating, using a predetermined extrapolation model, the computed time series to obtain an extrapolated time series of the performance parameter, the extrapolated time series including one or more estimated values of the performance parameter at one or more future times; comparing the one or more estimated values of the performance parameter with a predetermined condition indicative of a risk of component failure of said component to determine an estimated future time at which said predetermined condition is fulfilled; determining an estimated failure time from the estimated future time