Fault Classification for Cash Handling Machine Servicing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional error tracking and classification systems for cash handling machines, such as ATMs, lack the capability to efficiently process information needed to determine when a machine is problematic, leading to inefficient resource usage for servicing.
Innovation Solution
A method is introduced to track faults in machines by determining the existence of faults, assigning classification values based on predefined criteria, and evaluating the machine's status regarding hardware, cash reject rate, image handling, crash rate, and user claim rate, which can improve resource allocation for servicing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional error tracking systems are used, then machine monitoring is simple, but resource usage for servicing is inefficient
Solution Approach 1:
The patent segments machine performance into multiple classification values (e.g., green, yellow, orange, red) based on different fault criteria. Each classification represents a specific service priority level, allowing the system to differentiate between various degrees of machine problems rather than treating all errors uniformly. This segmentation enables more efficient resource allocation by directing service attention only to machines that truly need it.
Solution Approach 2:
The system changes the parameter of error tracking from simple binary (working/not working) to a multi-dimensional classification system that considers multiple fault types and their frequencies. By introducing classification values based on combinations of fault criteria, the system transforms raw error data into actionable service priorities, improving servicing efficiency without requiring overly complex infrastructure.
2Measurement precision
If multiple classification values are assigned based on multiple criteria, then machine status evaluation is more accurate, but processing complexity increases
Solution Approach 1:
The patent divides machine status evaluation into distinct classification categories (green, yellow, orange, red) based on specific fault criteria segments. Each category represents a clear service priority level, making the complex multi-criteria evaluation results interpretable and actionable. The segmentation approach maintains measurement precision while ensuring the complexity is managed through structured categorization.
Solution Approach 2:
The system implements feedback loops where classification results trigger specific service actions, and service outcomes feed back into the classification system. This feedback mechanism allows the system to learn and adjust classification thresholds, improving accuracy over time while maintaining a manageable level of processing complexity through automated decision rules.
3Reliability
If machines are serviced based on detailed fault tracking, then unnecessary replacements are reduced, but data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant fault information needed for service decision-making, rather than processing all possible machine data. By focusing on specific classification criteria (e.g., cash reject rate, image handling errors, crash rate) and assigning weights to different fault types, the system extracts essential data elements that drive replacement decisions while minimizing unnecessary data processing.
Solution Approach 2:
The system transforms raw fault data into classification parameters that directly indicate service needs. By changing the parameter representation from detailed fault logs to aggregated classification values, the system reduces data processing requirements while maintaining the ability to make reliable replacement decisions based on the synthesized classification information.
Data Source
AI summary
A method of tracking repeated performance problems in a machine is disclosed. The method comprises storing the faults in a computer memory, and assigning a classification value to the machine based on the frequency and number of the faults. Based on the classification value of the cash handing device it is determined whether the cash handing device needs to be serviced. The faults can be related to one of the hardware, the cash reject rate, image handling, crash rate, user claim rate, and check handling accuracy of the machine.