# Enhancing Antifragile Performance of Manufacturing Systems through Predictive Maintenance
## 3 Predictive Maintenance in Self-Improving Manufacturing Systems  
Specific to APM is the fact that it allows the integration and simultaneous exercise of control and maintenance procedures, respectively. In other words, the maintenance side expressed by PdM is proactive (PaM). PaM is a particular maintenance strategy that aims to detect the causes of failures and possibly avoid them or, if this is not possible, correct them. This strategy can be called conditional maintenance (CM) because it is performed only when required by the state of the system. As such, PaM aims at the timely diagnosis of a system that can degrade. On the other hand, this form of diagnosis is not enough because it provides only a warning (an alarm) and does not provide information about the remaining life of the system. For this reason, PdM complements the proactive side with a prognostic side, so that it can accurately predict when a system is expected to fail. In addition, the high dynamics of manufacturing processes run the risk that the old behaviors already learned become outdated. Reconfiguring them based on improved input data is not usually sufficient, so these system models need to be adapted and reconfigured to maintain and even improve their predictive performance. An important advance in capitalizing on APM technology is the association with digital twins [9]. A DT allows the real-time monitoring of machine conditions but also the anticipation of behaviour based on prediction models without invasive techniques. DT-based simulation allows us to find a virtual counterpart for each component of a manufacturing system and, therefore, we can have a clear picture of all assets throughout their entire life cycle. In this way, the DT-based approach will use both real data provided by built-in sensors and data from virtual digital models, which allow the calculation of an essential parameter of PdM, namely, remaining useful life (RUL). Thus, the global monitoring system can detect behavioral anomalies of the components that could lead to a failure, a decrease in performance, or a decrease in the quality of the finished product. Therefore, the essential role of predictive maintenance consists of recognizing a behavioral anomaly at an early stage of the manufacturing process and then in providing high quality prediction models to reach the different targets of this process. In paper [10], the authors present an original work dedicated to the development of a sensor signal-based digital twin for intelligent machine tools. The paper covers, in detail, two different systems (DTCS--digital twin construction system and DTAS--digital twin adaptation system) that are involved in the construction and adaptation of the twin. In the final section, the authors conclude that a DT is a certain benefit for the IoT; however, they also consider that any DT must gather knowledge through machine learning techniques based on historical datasets and use that knowledge while receiving real-time information from sensors. An interesting study investigating the connection between CPSs (cyber-physical systems) and smart manufacturing is presented in [11]. As the authors declare, the paper is \"a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS\". Although the paper aims to serve as a \"theoretical basis and as a comprehensive framework for emerging manufacturing integration\", one of the most important conclusions that can be drawn is that the integration of various concepts related to CPSs into manufacturing is still at the beginning. From this perspective, in the very near future, we expect to see many proposals for architectures dedicated to smart and wisdom manufacturing. Although this paper falls into the same category, we admit this raises a new challenge: the need for (some form of) standardization. This was also noticed by the authors of [12] who propose a \"unified architecture for IoT systems\".