# A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection
## 1 Introduction  
Since the 2010s, Artificial Intelligence (AI) and Machine Learning (ML) have become standard ways to tackle complex problems relating to Industry, Medical, Manufacturing, or Economics. Flexibility, generality, and efficiency are only some of the qualities permitted by AI to be adopted into multiple scenarios. Furthermore, academics have recently enhanced learning programs, research programs, and collaboration projects with companies to respond to the industrial world's increasing AI and ML needs. With the upcoming Industry 5.0 paradigm, the term Predictive Maintenance (PdM) is becoming more common in the language of companies today. This type of maintenance plays an interesting role in preventing machine failures and reducing the downtime cost in a preponderant way. The cost of unrelated failure occurring in industrial machines is estimated at USD 50 billion annually as reported in the analysis by Ref. [1]. An explanation of how predictive maintenance is able to reduce the overall costs by 5-10% and the standard maintenance costs by 20-50% is reported in Ref. [2], highlighting its impact on the company's economic aspect. Throughout the years, the term \"Maintenance\" has taken different shapes and terminologies, as shown in (Figure 1) [3]:* _Reactive/Corrective Maintenance_: it intervenes after a problem has occurred and is considered the oldest one;
* _Aggressive Maintenance_: it focuses on the performance improvement of production equipment design;
* _Proactive Maintenance_: it prevents machine failures and is considered to be the most used maintenance strategy today; two sub-maintenance strategies derive from this approach:
* _Preventive Maintenance_: it is based on a periodic components' substitution, generally the most used ones;
* _Predictive Maintenance_: it is able to predict when a problem will occur in the future. Since the first industrial revolution, the predictive maintenance branch has increased companies' and scientific community's interest in an exponential way [4]. Predictive Maintenance monitors the real status of the machine by employing events, states, and sensor data. With this kind of maintenance, component substitution is done when it is truly necessary. This approach represents the future trend of maintenance thanks to its ability to estimate when a failure will occur, reducing the cost of useless substitution and harmful downtime. Its main drawback concerns the need to collect numerous data to perform an estimation with adequate accuracy. Often, the term \"Predictive Maintenance\" is used improperly by engineers, blurring it with Condition Monitoring (CM). There is a substantial difference between these two methodologies [5]: CM is based on the overcome of specific thresholds computed by a domain expert or on the datasheet basis; hence, if a warning appears, the maintainer has to intervene in solving the anomaly. With CM, there is no prediction about future issues, and an alarm is emitted when a problem has already appeared. CM is deeply explored in different application domains, as reported in Refs. [6; 7; 8]. The PdM's approach is different from CM's; using the collected data, PdM can predict when an issue will appear in the future, giving time to the maintainer to intervene before the breakdown. These two techniques are also related to two other common words: diagnostics and prognostics. Diagnostics is related to analyzing the past collected data, studying which damage occurred and the cause of that; similar to the medical environment, prognostics analyzes the data collected in the past together with the present ones to predict issues and failures in the future [9]. In detail, according to the definition of ISO 13381-1 [10], prognostics has the quirk to predict the Remaining Useful Life (RUL) of the components analyzed. Different models exist to perform a prognosis, as reported in Ref. [11], in which three different variants are well explained. Today's PdM methodologies use more complex algorithms  
Figure 1: Types of maintenance. than the statistical ones used in the past.