ATM Digital Twin for Real-Time Failure Prediction
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Solution Overview
Problem
Deploying ATMs in various regions poses challenges due to unnoticed failures leading to downtime, which affects user satisfaction and operational efficiency, as existing monitoring tools are inadequate for real-time fault detection with minimal manual intervention.
Innovation Solution
Implementing a digital twin network for ATMs equipped with machine learning capabilities, utilizing a neural network that maps physical ATM data from various sources, including 5G connectivity, CCTV, and WAN, to predictively analyze and prevent system failures before they occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring tools are deployed to check ATM health, then some fault detection capability is provided, but real-time failure prediction and reduced downtime cannot be achieved
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical ATM network that mirrors its behavior and state. This digital replica allows for failure prediction and analysis without directly interfering with the physical system, enabling proactive maintenance while maintaining system reliability.
Solution Approach 2:
The system performs preliminary failure prediction by analyzing historical and real-time data from the digital twin before actual failures occur in the physical ATM network. This allows engineers to take preventive actions ahead of time, reducing downtime and improving reliability.
2Ease of operation
If engineers manually triage unnoticed failures at ATMs, then issues can be addressed, but downtime increases and user satisfaction decreases
Solution Approach 1:
The system implements continuous feedback loops where the digital twin monitors the physical ATM network state, predicts potential failures, and provides early warnings. This feedback mechanism enables proactive response before failures impact user operations, reducing downtime and maintaining ease of operation.
Solution Approach 2:
By predicting failures in advance through the digital twin, the system enables preliminary maintenance actions to be taken before actual failures occur, preventing operational disruptions and maintaining high ATM availability.
3Measurement precision
If digital twins with machine learning capabilities are implemented to predict failures, then failure prediction accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The digital twin serves as a simplified virtual representation that captures essential behaviors of the physical ATM network. By working with this copied model rather than the complex physical system directly, the patent achieves accurate failure prediction while managing complexity through abstraction.
Solution Approach 2:
The digital twin ecosystem serves multiple functions simultaneously: monitoring, prediction, analysis, and simulation. This multi-functionality consolidates what would otherwise require multiple separate complex systems into a single unified platform, managing overall system complexity while providing comprehensive failure prediction capabilities.
Data Source
AI summary
A computer performs a method for pro-actively administering a physical network of automated teller machines (ATM). The network may include ATMs. The network may be coupled, using an asynchronous transfer mode Wide Area Network (WAN) and the router, to a second plurality of ATMs via a digital subscriber line (DSL) module, a Closed Circuit Television (CCTV) system to provide CCTV television footage, and/or a 5-G cellular interface. The method receives input data using a data processing block coupled to the network of ATMs. The data may be derived from the plurality of ATMS, the WAN, the second plurality of ATMs, the CCTV and/or the 5-G cellular network interface. The method cleans and processes data and leverages a virtual digital twin to predictively provide status information of the ATMs based on the data. The method may formulate and maintain predictive data using a digital twin prediction model.