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?
