Multiple Gross Errors Detection in Surveying Measurements Using Statistical Quality Control

Abstract

Most of the surveying tasks involve the acquisition and analysis of measurements. Such measurements are subject to random, systematic and gross errors. In practice, redundant measurements are made to provide quality control and errors check. In qualitative analysis and statistical evaluation, it is generally assumed that the measurements contain only random errors and are regarded as random variables. In reality, the measurements may contain gross and/or systematic errors. The effects of such errors are distributed over the residuals, after an adjustment and lead to questionable results and interpretation. For high precision applications, gross and systematic errors need to be detected prior to the analysis. These errors should be tackled before the adjustment by means of screening. These few remaining gross errors in the measurements can be detected after the adjustment. These adjustment methods assume the presence of only one gross error. One of the most effective methods that can be used in detecting multiple gross errors is the statistical quality control method. Statistical quality control is a technique used to monitor a procedure with a goal of making it more efficient and ensures precise results. Statistical control charts are used to provide an operational definition of a special cause for a given set of data. It is possible to construct multiples of sigma control limits. When all the points on a control chart are within a multiple of sigma control limits and there are no gross errors in the data, the process of measurements is said to be in a state of statistical control. Otherwise, the data indicate the presence of non-random gross errors. In this research work, different methods of statistical quality control were used. Results showed that statistical quality