An integral method is proposed that performs dynamic data reconciliation on linear systems, in contrast with recent methods that utilize differential algebraic equations. The differential equations representing this system are first rearranged to obtain a system of equations containing only redundant measurements. These equations are formally integrated using polynomial approximations, and the reconciliation is then performed using analytical solutions. A new statistic to detect gross errors is proposed and a procedure to detect biased measurement is presented.