Two-step method for gross error detection in process data

Three types of gross errors-measurement biases, process leaks, and abnormal variances-are discussed. A new class of test statistics for gross error detection, called the two-step method, is proposed. This method consists of two steps. The first step is to detect and eliminate the abnormal variances in measured variables. The second step is to detect measurement biases and process leaks. In the second step, a mean-value transformation is introduced to improve the performance of gross error detection. Simulations are performed, and the two-step method is compared to the existing test statistics. It is shown that the new tests have a superior overall performance, and can detect all three types of gross errors discussed in this paper, even gross errors of small magnitudes. Also, the two-step method can easily distinguish measurement biases and process leaks from abnormal variances