Uncertainty determination and reduction through novel approach for industrial IOT

Abstract

The most fundamental and crucial processes in Data analysts is effective information processing while cleaning information from noisy sensors. Conventional outlier identification techniques should not be used directly for this type of assessment, as sensor data can contain both noise (piezoelectric microphone type) and severe abnormalities. To manage the problem of sound reduction while maintaining the anomalies in the IIoT information, this paper proposes a method of calculating the noise score which takes into account both the rate of variation and the variance. To establish the unit of evaluation of the measurement of difference which would be used in conjunction with statistical analyses, a sliding window method. Extensive testing shows that the proposed strategy surpasses existing advanced noise detection methods, producing a clean dataset that maintains anomalies to successfully use abnormal investigation techniques.