AIChE Journal · 1995 · 177 citations · 20 references
ComputingComputer SciencePerformance MeasurementProcess Quality ManagementQuality EngineeringData AnalysisError DetectionFault DetectionMeasurement ErrorPrincipal Component AnalysisStatistical Process ControlMultivariate Statistical Fault DetectionProcess Fault DetectionProcess Fault DiagnosisProcess MonitoringTest Statistics
Statistical testing is used by engineers to assess the validity of process measurements and data reconciliation, yet commonly employed tests such as university, maximum‑power, and chi‑square often perform unsatisfactorily. The study proposes a new class of principal component analysis–based test statistics for detecting and identifying gross errors, comparing them to existing tests. These statistics are constructed using principal component analysis and evaluated against other established test statistics. The new test detects small‑magnitude gross errors and accurately identifies erroneous variables, outperforming other tests when they fail.
Abstract Statistical testing provides a tool for engineers and operators to judge the valididty of process measurements and data reconciliation. Univeriate, maximum power and chisquare tests have been widely used for this purpose. Their performance, however, has not always been satisfactory. A new class of test statistics for detection and identification of gross errors is presented based on principal component analysis and is compared to the other statistics. It is shown that the new test is capable of detecting gross erros of smallmaginitudes and has substantial power to correctly identify the variables in error, when the other tests fail.
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