Process for training a self-learning machine in a defect monitoring system for a remote surveillance of a monitored structure; the defect monitoring system comprising a non-destructive defect measurement unit comprising an eddy current sensor and/or an ultrasound sensor for sensing a defect of the monitored structure, and the defect monitoring system being configured to collect auxiliary data from an auxiliary detector; the auxiliary detector being configured to: measure and/or predict a force acting on a portion of the monitored structure; and/or measure and/or predict a temperature, an acceleration and/or a linear displacement of a portion of the monitored structure; and/or detect or predict a mobile load acting on the monitored structure; and/or detect or predict a terrain displacement and/or instability near the monitored structure; and/or detect or predict an erosion or scour around a support of the monitored structure; the process comprising steps of: a) Collecting an input dataset including: reference defect data of a reference defect of the monitored structure or of a reference structure; and reference auxiliary data of other parameters of the same monitored or reference structure; b) Building a training dataset by pairing said reference defect data and/or said reference auxiliary data as input set with a corresponding training target dataset; c) Training the self-learning machine of the defect monitoring system with said training dataset; wherein the training target dataset comprises: decision data to trigger a measure from the non-destructive defect measurement unit of the defect monitoring system, and/or a classification of the reference defect of the monitored or reference structure and/or decision data to generate and/or trigger an alarm of the defect monitoring system; and/or decision data to trigger a transmission of defect data provided by the non-destructive defect measurement unit of the defect monitoring system for further examination; and/or decision data to trigger a transmission of auxiliary data provided by the auxiliary detector for further examination. measure and/or predict a force acting on a portion of the monitored structure; and/or measure and/or predict a temperature, an acceleration and/or a linear displacement of a portion of the monitored structure; and/or detect or predict a mobile load acting on the monitored structure; and/or detect or predict a terrain displacement and/or instability near the monitored structure; and/or detect or predict an erosion or scour around a support of the monitored structure; a) Collecting an input dataset including: reference defect data of a reference defect of the monitored structure or of a reference structure; and reference auxiliary data of other parameters of the same monitored or reference structure; reference defect data of a reference defect of the monitored structure or of a reference structure; and reference auxiliary data of other parameters of the same monitored or reference structure; b) Building a training dataset by pairing said reference defect data and/or said reference auxiliary data as input set with a corresponding training target dataset; c) Training the self-learning machine of the defect monitoring system with said training dataset; decision data to trigger a measure from the non-destructive defect measurement unit of the defect monitoring system, and/or a classification of the reference defect of the monitored or reference structure and/or decision data to generate and/or trigger an alarm of the defect monitoring system; and/or decision data to trigger a transmission of defect data provided by the non-destructive defect measurement unit of the defect monitoring system for further examination; and/or decision data to trigger a transmission of auxiliary data provided by the auxiliary detector for further examination.