Data-driven fault detection and diagnosis in automated manufacturing systems
TL;DR
Data-driven methods, in contrast to traditional reactive ones, make use of continuous sensor data streams to anticipate anomalies before they can result in critical failures using advanced analytics, thereby improving responsiveness and efficiency in the system.
Abstract
Abstract. The introduction of a data-based fault detection and diagnosis (FDD) has radically changed the concept of automated manufacturing systems as it provides the capability to monitor real-time in an intelligent manner, diagnose faults beforehand, and predictive maintenance schedules. Data-driven methods, in contrast to traditional reactive ones, make use of continuous sensor data streams to anticipate anomalies before they can result in critical failures using advanced analytics. This proactive feature has a great impact in minimizing unplanned downtime, increasing the safety of operations, and enhancing the overall efficiency of equipment. The FDD systems have been made effective with the help of the artificial intelligence (AI) and the Industrial Internet of Things (IoT). Sensors that have the ability to collect data through IoT can easily get data on various changes of the manufacturing facilities and the AI algorithms can help extract meaningful information data and support in making automated decisions. The other technologies such as cloud platform and edge computing, enable real-time processing of data and scalable analytics, thereby improving responsiveness and efficiency in the system. This implies that manufacturing systems are turning to be more adaptable, independent and robust.