ATM Error Diagnosis via Sensor Pattern Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for self-service terminals like ATMs lack the ability to accurately predict and diagnose error states due to the absence of information about previous state changes, making it difficult to identify the causes of malfunctions such as jammed notes or multiple feeding issues, which often require manual intervention and result in inefficient operations.
Innovation Solution
A method involving a system with sensors and a control unit that analyzes error logs to identify sensor patterns, compares them with predefined detectors, and classifies error states, incorporating a learning capability to detect novel error sequences and adapt to specific environmental conditions, allowing for automated error prediction and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor information is analyzed using traditional snapshot methods, then the system structure remains simple, but the ability to accurately determine error causes is insufficient
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing sensor information in error logs before errors occur. This historical data is then analyzed using pattern detection algorithms to identify error causes, allowing the system to diagnose issues based on pre-captured sensor patterns rather than relying solely on current state snapshots.
Solution Approach 2:
The patent introduces an intermediary error log that stores sensor information over time, and pattern detection algorithms that serve as mediators between raw sensor data and error diagnosis. This intermediary layer enables complex temporal pattern analysis without requiring direct complex interactions between all system components, thus improving accuracy while managing complexity.
2Productivity
If manual intervention is used to diagnose ATM malfunctions, then error analysis can be thorough, but operational efficiency decreases and response time increases
Solution Approach 1:
The system implements self-service by automatically analyzing error logs and detecting sensor patterns to diagnose errors without requiring manual operator intervention. The control means autonomously processes sensor information, identifies error causes through pattern matching, and determines error states, enabling the ATM to self-diagnose issues and maintain high operational efficiency while preserving complete error state information.
3Reliability
If only current sensor states are monitored, then the monitoring system remains simple, but the ability to predict and diagnose errors is limited
Solution Approach 1:
The system performs preliminary data collection by continuously storing sensor information in error logs before errors occur. This pre-captured historical data enables the system to analyze temporal patterns and predict potential errors before they manifest as fatal failures, improving reliability while reducing the time needed for error analysis since the data is already collected and organized.
Solution Approach 2:
The system applies partial action by focusing pattern detection algorithms on specific sensor patterns and error conditions rather than analyzing all possible sensor combinations. This selective approach to pattern matching enables effective error prediction and diagnosis without requiring excessive computational resources or time, balancing thoroughness with efficiency.
Data Source
AI summary
A method is described for determining the cause of an error state for one or more components within an apparatus. The apparatus comprises a plurality of sensors arranged to monitor the operation of components of the apparatus and a control means arranged to receive said information from said plurality of sensors. The method comprises analysing said sensor information in the form of an error log to ascertain sensor patterns from said sensor information comparing said sensor patterns with detectors, which are predefined patterns, indicative of the condition of said one or more components within the apparatus and classifying said sensor patterns as being indicative of said error state of a component or not based upon a comparison of sensor patterns with said detectors.