Fault diagnosis is an important area in the chemical process industry and has attracted considerable attention from researchers in the recent past. All approaches for fault diagnosis depend critically on the sensors measuring the important process variables in the system. In this paper, a reliability maximization based optimization framework for sensor location from a fault diagnosis perspective is presented. The formulation is aimed toward maximizing the reliability of the fault monitoring system while satisfying the constraints imposed on the system. A minimum-cost model which minimizes the cost of the fault monitoring system while ensuring that the solution provides a minimum threshold reliability is also presented. A one-step optimization formulation which maximizes reliability and, among the various solutions with the same reliability, chooses the one with minimum cost is discussed in this paper. A methodology for obtaining the "best" sensor location irrespective of the single/multiple fault assumption is also presented. In the first part of this two-part series of papers, the sensor location framework is discussed. In the second part, the sensor location procedure is applied to a large flowsheet, the Tennessee-Eastman flowsheet. Various issues involved in the application of the reliability maximization based optimization procedure are explained using this case study.