How to Evaluate the Performance of a Dual-Sensor Redundancy Comparison Algorithm

Evaluating the performance of a dual-sensor redundancy comparison algorithm revolves around four core indicators: false alarm rate, false alarm rate, adaptability, and maintenance cost. This can be accomplished through a two-step process: "laboratory testing + field verification." The specific evaluation method is as follows: I. Core Performance Indicators (Quantitative Evaluation) These four indicators are crucial for judging the quality of the algorithm and directly correspond to its practical application value: Indicator Calculation Method Pass Standard Excellent Standard False Alarm Rate Number of false alarms ÷ Total number of alarms × 100% ≤5% ≤2% False Alarm Rate Number of missed real faults ÷ Total number of real faults × 100% ≤1% 0% Response Delay Average time from the occurrence of a real fault to the alarm ≤15s ≤5s Long-Term Stability Change rate of false alarm rate after 3 months of continuous operation ≤2% ≤1% Supplementary Note: False alarm rate is a core indicator in industrial scenarios. False alarms can lead to unnecessary downtime, directly impacting production capacity. It has a higher priority than false alarm rate. II. Pre-installation Testing in the Laboratory (Early Screening of Problems) Before on-site installation, conduct simulation tests to quickly verify basic performance: Interference Test: Artificially introduce spike noise, vibration interference, and temperature fluctuations of varying intensities to see if the algorithm will issue false alarms-the passing standard is: no instantaneous interference should trigger an alarm; only sustained deviations should trigger an alarm. Fault Simulation Test: Artificially simulate different types of faults (single-point drift, zero-point offset, slow aging) and analyze the false alarm rate and response delay-all real faults must be detected promptly without false alarms. Aging Adaptability Test: Artificially simulate aging drift at different rates for two sensors to see if the algorithm can automatically update the baseline for adaptation-the passing standard is: no false alarms should be triggered when the drift is within a reasonable range; an alarm should only be triggered when the drift exceeds the safe range. III. On-site Verification (Final Confirmation) After passing laboratory testing, run the system on-site for 1-2 months to verify performance under real-world conditions: Statistical Analysis of Real-world Operational Data: Continuously record all alarms, manually verify each alarm to determine if it is a genuine fault or a false alarm, and calculate the actual false alarm rate; Comparison with Traditional Algorithms: Compare the algorithm with the original fixed threshold algorithm during the same period to observe the percentage decrease in false alarm rate-an optimization should result in at least a 60% reduction in false alarm rate; Long-term Stability Assessment: Run continuously for 3 months to check if the false alarm rate increases significantly with sensor aging-if the algorithm includes automatic baseline updates, the false alarm rate fluctuation should not exceed 2%. IV. Additional Evaluation Items In addition to core indicators, evaluate the user experience: Maintainability: Does it support one-click calibration and automatic fault location? Can ordinary maintenance personnel operate it quickly? Compatibility: Can it be integrated with existing PLC equipment without requiring additional hardware replacement? Resource Consumption: Does the algorithm require high computing power from the controller, and will it cause PLC lag?