Weighing Scale Diagnostics Using Statistical Outlier Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing weighing scale diagnostic methods face challenges in setting accurate individual component operating characteristic threshold values, leading to false alarms or missed alerts, especially when the normal value range is small, and require technical knowledge to select appropriate thresholds.
Innovation Solution
The method involves monitoring and comparing operating parameters common to multiple like components, such as temperature, digital signal voltage, and zero balance change, using statistical tests like Chauvenet's Criterion to identify outliers and deviations, eliminating the need for setting individual threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual component operating characteristic threshold values are set for monitoring force measuring devices, then component failures can be detected, but false alarms occur and technical knowledge is required to select appropriate thresholds
Solution Approach 1:
The system performs self-diagnosis by automatically comparing operating parameters of multiple like components and using statistical analysis to identify outliers. The monitoring system serves itself by eliminating the need for external expert intervention in threshold selection, automatically adapting to changing conditions through statistical methods.
Solution Approach 2:
The invention changes the monitoring approach from using fixed predetermined threshold values to using statistical parameters derived from actual component data. By calculating mean, standard deviation, and using Chauvenet's Criterion, the system dynamically adjusts monitoring criteria based on observed parameter variations, eliminating the need for manual threshold setting.
2Measurement precision
If low threshold values are set for monitoring, then sensitivity to component problems increases, but false alarms are triggered
Solution Approach 1:
The system uses feedback from multiple like components to establish normal operating ranges through statistical analysis. By continuously monitoring parameters across all components and comparing individual readings against the statistical distribution, the system dynamically adjusts detection sensitivity based on actual system behavior, reducing false alarms while maintaining high detection accuracy.
Solution Approach 2:
The invention monitors operating parameters of multiple like components simultaneously rather than relying on a single component's absolute threshold. By comparing parameters across multiple components and identifying statistical outliers, the system achieves higher detection precision without triggering false alarms, as it requires deviation from the group norm rather than absolute threshold violation.
3Reliability
If high threshold values are set for monitoring, then false alarms are reduced, but component problems may be missed
Solution Approach 1:
The system monitors operating parameters of multiple like components simultaneously rather than relying on a single component's absolute threshold. By comparing parameters across multiple components and identifying statistical outliers, the system achieves higher detection precision without triggering false alarms, as it requires deviation from the group norm rather than absolute threshold violation.
4Ease of operation
If predetermined threshold values are used for monitoring, then the diagnostic process is simple, but the system cannot adapt to changing conditions
Solution Approach 1:
The system transitions from static predetermined thresholds to dynamic statistical thresholds that automatically adapt to changing operating conditions. By calculating mean and standard deviation from actual component data and using Chauvenet's Criterion, the monitoring criteria evolve with the system, maintaining simplicity while achieving adaptability to varying environmental and operational conditions.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
Embodiments of the invention generally relate to weighing scale diagnostic methods employing a comparison of like component operating parameters. In certain embodiments, the difference between any two current operating parameter values may be compared against a maximum allowable difference, and/or the deviation of current operating parameters from a calculated measure of central tendency may be determined and compared against a maximum allowable deviation. Alternatively or additionally, a standard statistical test for outliers may be employed. An outlying difference or deviation may be indicative of a problem with the associated component. In other embodiments, the current operating parameters of like components may be compared against calibrated parameters and any deviation of the current parameter for a given component may be compared against the total deviation to determine the percentage of deviation attributable to that component.