Preventive Controller Switchover Using AI Failure Prediction
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Solution Overview
Problem
Existing controller redundancy systems in industrial plants often fail to prevent downtime due to primary controller failures, as the switchover to secondary controllers occurs after the primary controller has already failed, and there is a delay in data transfer, which can lead to operational failures.
Innovation Solution
A method and system that utilize a server to collect real-time log files from primary controllers, determine abnormal patterns using AI-based models, and predict events leading to switchover, allowing for a preventive switchover to secondary controllers before the primary controller fails, thereby reducing downtime and ensuring smooth operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If switchover is performed after primary controller failure, then redundancy is provided, but downtime occurs and operational continuity is compromised
Solution Approach 1:
The system performs preliminary actions by continuously monitoring operational parameters and predicting potential failures before they occur. The server analyzes log files and identifies abnormal patterns, enabling proactive switchover to the secondary controller before the primary controller actually fails, thus preventing downtime rather than responding to it after occurrence.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting log files from the primary controller, analyzing operational parameters, and comparing them against normal operation patterns. This real-time feedback loop enables the system to detect deviations and predict failures, triggering preventive switchover actions before critical failure occurs.
2Reliability
If data transfer is performed after primary controller failure, then failover is achieved, but data completeness is compromised due to inability to transfer all required parameters
Solution Approach 1:
The system performs preliminary data collection and validation before failure occurs. By continuously monitoring and analyzing operational parameters in real-time, the system ensures that all necessary data is captured and validated while the primary controller is still functional, enabling complete and accurate data transfer during preventive switchover.
Solution Approach 2:
The server acts as an intermediary between the primary and secondary controllers. It collects, validates, and prepares operational data from the primary controller before switchover, ensuring data completeness and integrity. This intermediary role allows for thorough data verification and preparation that would be impossible if transfer attempted after failure.
3Measurement precision
If continuous monitoring of operational parameters is implemented, then predictive capability is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically collecting, analyzing, and interpreting operational parameters without requiring external intervention. The server autonomously monitors log files, detects abnormal patterns, predicts failures, and triggers switchover actions, reducing the need for complex external monitoring infrastructure and manual analysis processes.
Solution Approach 2:
The system replaces complex mechanical or manual monitoring mechanisms with electronic and software-based solutions. By using automated log file analysis and pattern recognition algorithms, the system achieves precise parameter monitoring with simpler overall architecture compared to traditional hardware-based monitoring systems.
Data Source
AI summary
A preventive switchover from a primary controller to a secondary controller even before the primary controller fails system and method includes a server that collects log files comprising operational parameters of the primary controller from the primary controller in real-time. The server determines abnormal patterns or signatures in the operational parameters of the primary controller by comparing the operational parameters with reference patterns or signatures. The reference patterns or signatures are generated by training one or more Artificial Intelligence (AI) based models. After determining the abnormal patterns or signatures, the server predicts events that will lead to switchover from the primary controller to the secondary controller. Thereafter, the server provides a signal to the primary controller to perform preventive switchover to the secondary controller before the primary controller fails.