Recent years have seen an increasing interest in the employment of Neural Networks on spacecrafts in the context of real-time data processing and decision making. However, the radiations typical of the space environment introduce potential risks of data corruption, which could compromise the reliability and usability of these systems. This thesis project aims to address this problem through a newly developed tool, able to detect faults during inference on spacecrafts. The analysis achieved with this software gives a comprehension of the reliability of the Convolutional Neural Network adopted in a specific system, under the given conditions. The case study involves the Payload Data Processor of HEO, a micro-satellite developed by Argotec for the IRIDE program.