A system for calculating a maintenance prediction for one or more components in an on-board inert gas generating system (OBIGGS), the OBIGGS components including an ozone converter, heat exchanger, inlet filter, and Air Separation Module each fluidly coupled via a flow path, the ozone converter configured to remove ozone gas from an inlet gas received via an inlet valve, the heat exchanger configured to adjust the temperature of the inlet gas, the inlet filter configured to filter at least a portion of contaminants from the inlet gas, and the ASM comprising a hollow-fiber membrane configured to separate constituents within the inlet gas and produce a volume of inert gas for delivery to a downstream fuel tank; the system comprising: a. a prognostic health monitoring sensor network comprising a plurality of sensors (32, 36, 40, 44, 50, 52, 60, 64), wherein a respective sensor (32, 36, 40, 44, 50, 52, 60, 64) is coupled to a respective component (22, 24, 26, 58) of the OBIGGS, wherein each respective sensor (32, 36, 40, 44, 50, 52, 60, 64) is configured to output a respective data signal corresponding to a performance condition of the respective component (22, 24, 26, 58) associated with the respective sensor (32, 36, 40, 44, 50, 52, 60, 64); and b. a control unit operatively coupled to each of the components (22, 24, 26, 58) of the OBIGGS and signally coupled to the plurality of sensors (32, 36, 40, 44, 50, 52, 60, 64) of the PHM sensor network, wherein the control unit includes i) a memory configured to store a test condition algorithm associated with the respective component (22, 24, 26, 58), wherein the test condition algorithm includes an empirical curve fit corresponding to a predicted performance lifetime of the respective component (22, 24, 26, 58), and ii) a computing device configured to analyze the respective data signal to calculate the maintenance prediction for the respective component (22, 24, 26, 58), wherein the maintenance prediction is calculated by comparing the respective data signal relative to the empirical curve fit to estimate remaining component operating hours before component failure. a. a prognostic health monitoring sensor network comprising a plurality of sensors (32, 36, 40, 44, 50, 52, 60, 64), wherein a respective sensor (32, 36, 40, 44, 50, 52, 60, 64) is coupled to a respective component (22, 24, 26, 58) of the OBIGGS, wherein each respective sensor (32, 36, 40, 44, 50, 52, 60, 64) is configured to output a respective data signal corresponding to a performance condition of the respective component (22, 24, 26, 58) associated with the respective sensor (32, 36, 40, 44, 50, 52, 60, 64); and b. a control unit operatively coupled to each of the components (22, 24, 26, 58) of the OBIGGS and signally coupled to the plurality of sensors (32, 36, 40, 44, 50, 52, 60, 64) of the PHM sensor network, wherein the control unit includes i) a memory configured to store a test condition algorithm associated with the respective component (22, 24, 26, 58), wherein the test condition algorithm includes an empirical curve fit corresponding to a predicted performance lifetime of the respective component (22, 24, 26, 58), and ii) a computing device configured to analyze the respective data signal to calculate the maintenance prediction for the respective component (22, 24, 26, 58), wherein the maintenance prediction is calculated by comparing the respective data signal relative to the empirical curve fit to estimate remaining component operating hours before component failure.