[Objective]The research aimed to study the influence of automatic station data on the sequence continuity of historical meteorological data.[Method]Based on the temperature data which were measured by the automatic me...[Objective]The research aimed to study the influence of automatic station data on the sequence continuity of historical meteorological data.[Method]Based on the temperature data which were measured by the automatic meteorological station and the corresponding artificial observation data during January-December in 2001,the monthly average,maximum and minimum temperatures in the automatic station were compared with the corresponding artificial observation temperature data in the parallel observation period by using the contrast difference and the standard deviation of difference value.The difference between the automatic station and the artificial data,the variation characteristics were understood.Meanwhile,the significance test and analysis of annual average value were carried out by the data sequence during 1990-2009.The influence of automatic station replacing the artificial observation on the sequence continuity of historical temperature data was discussed.[Result]Although the two temperature data in the parallel observation period had the certain difference,the difference was in the permitted range of automatic station difference value on average.The difference of individual month surpassed the permitted range of automatic station difference value.The significance test showed that the annual average temperature and the annual average minimum temperature which were observed in the automatic station had the difference with the historical data.It had the certain influence on the annual temperature sequence,but the difference wasn’t significant as a whole.When the automatic observation combined with the artificial observation to use,the sequence needed carry out the homogeneous test and correction.[Conclusion]The research played the important role on guaranteeing the monorail running of automatic station,optimizing the meteorological surface observation system,improving the climate sequence continuity of meteorological element and the reliability of climate statistics.展开更多
This paper presents an artificial intelligence(AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser.Electrolysers are hydrogen production plan...This paper presents an artificial intelligence(AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser.Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation,prevent degradation,and avoid loss of efficiency.In this sense,predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities.However,if the sensor fails,there will be an incorrect forecasting of abnormalities.Among the different types of operational faults that sensors can present are drift-related faults,which are probably the most difficult to detect due to a slow but progressive loss of accuracy in measurements.Another problem with predictive maintenance is that it often requires enormous training data,which is not available at the early stage of plant operation.The developed fuzzy system is responsible for detecting faulty readings arising from drift sensor signals,while the neural network complements the function of the fuzzy system by predicting sensor signals when enough training data are available.The AI-based observer and the fuzzy rules are validated in an experimental case study with a 1 Nm^(3)/h electrolyser.The selected variables are electrolyser temperature and efficiency.Experimental results show that the rules of the fuzzy component of the AI-based observer guarantee an accuracy of±0.25 within the range of 0 to 1,and the neural network component predicted correct sensor values with a root mean square error(RMSE)as low as 0.0016.The authors’approach helps to determine drift faults without additional sensors or components installed in the plant.展开更多
文摘[Objective]The research aimed to study the influence of automatic station data on the sequence continuity of historical meteorological data.[Method]Based on the temperature data which were measured by the automatic meteorological station and the corresponding artificial observation data during January-December in 2001,the monthly average,maximum and minimum temperatures in the automatic station were compared with the corresponding artificial observation temperature data in the parallel observation period by using the contrast difference and the standard deviation of difference value.The difference between the automatic station and the artificial data,the variation characteristics were understood.Meanwhile,the significance test and analysis of annual average value were carried out by the data sequence during 1990-2009.The influence of automatic station replacing the artificial observation on the sequence continuity of historical temperature data was discussed.[Result]Although the two temperature data in the parallel observation period had the certain difference,the difference was in the permitted range of automatic station difference value on average.The difference of individual month surpassed the permitted range of automatic station difference value.The significance test showed that the annual average temperature and the annual average minimum temperature which were observed in the automatic station had the difference with the historical data.It had the certain influence on the annual temperature sequence,but the difference wasn’t significant as a whole.When the automatic observation combined with the artificial observation to use,the sequence needed carry out the homogeneous test and correction.[Conclusion]The research played the important role on guaranteeing the monorail running of automatic station,optimizing the meteorological surface observation system,improving the climate sequence continuity of meteorological element and the reliability of climate statistics.
基金support of(1)Grant Ref.PID2023-148456OB-C41 and(2)Grant Ref.RED2022-134588-T found by MICIU/AEI/10.13039/501100011033。
文摘This paper presents an artificial intelligence(AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser.Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation,prevent degradation,and avoid loss of efficiency.In this sense,predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities.However,if the sensor fails,there will be an incorrect forecasting of abnormalities.Among the different types of operational faults that sensors can present are drift-related faults,which are probably the most difficult to detect due to a slow but progressive loss of accuracy in measurements.Another problem with predictive maintenance is that it often requires enormous training data,which is not available at the early stage of plant operation.The developed fuzzy system is responsible for detecting faulty readings arising from drift sensor signals,while the neural network complements the function of the fuzzy system by predicting sensor signals when enough training data are available.The AI-based observer and the fuzzy rules are validated in an experimental case study with a 1 Nm^(3)/h electrolyser.The selected variables are electrolyser temperature and efficiency.Experimental results show that the rules of the fuzzy component of the AI-based observer guarantee an accuracy of±0.25 within the range of 0 to 1,and the neural network component predicted correct sensor values with a root mean square error(RMSE)as low as 0.0016.The authors’approach helps to determine drift faults without additional sensors or components installed in the plant.
