Recent years have seen an exponential growth in the development of efficient strategies for decision making based on data. This is particularly true in healthcare and engineering, where effective monitoring systems have been developed to track the health status of people and of critical engineering structures (eg structural health monitoring) in order to detect anomalies in the data and suggest preventive actions to restore normal conditions. Nonetheless, the majority of the research in health monitoring of systems is focusing on the development of advanced Machine Learning algorithms which extract information from the data under the assumption that the data collected is reliable but might be affected by noise. However, the electronic equipment used in the monitoring system can be faulty, and therefore the data might display patterns which do not represent the behaviour of the system being monitored. Problems in the monitoring equipment during assembly or operational stages might be unnoticed, and can lead to wrong preventive actions planning. This talk will present a strategy for detecting failures in the monitoring systems by combining information coming from recorded sensors’ data and failure reports, therefore exploiting the application of Machine Learning and Natural Language Processing techniques. Two approaches will be presented to address two case studies: failures detection of a low-cost wearable device and of a low-cost monitoring system for vehicles.