Analysis and Research Based on Instrument Drift Data

Abstract

Instruments and meters are used to inspect, measure, control, analyze, calculate and display the physical quantity, chemical quantity, biomass, electrical parameters, geometric quantity, and movement status of the object under test. Its applications cover a wide range of industries, agriculture, transportation, science, and technology, etc., and can be divided into electrical instruments, optical instruments, laboratory instruments, analytical instruments, testing machines, industrial instruments, etc. It assumes the task of gatekeeper and guide in the construction of the national economy. The analysis and research techniques of this article are based on the professional data of power plant industrial instrumentation. This paper presents an overview of various analysis methods of instrument data, and in-depth analysis of time correlation and coverage analysis, outlier analysis, etc. We give the statistical analysis process of drift data, focusing on the process of the AFAL method; We also described the drift algorithm of random behavior and deviation behavior and explored and discussed the analysis tools and data analysis results of the power plant industry professional data. We have designed a set of application systems for instrument data import, export, storage, and analysis, which can store instrument calibration data onto a Hadoop database in a prescribed format. Which provides great reference values for the calibration of instrument data. Finally, it looks forward to the analysis and research direction and development trend of industrial big data.

Evaluation of the long-term drift of measuring instruments and standards using time series

“…In [9] describes the drift algorithm for random behavior of the metrological characteristics of measuring instruments. This paper also provides useful an overview of various methods for accounting for the drift of measuring instruments.…”