# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 1 Introduction  
Malfunctions in manufacturing plant equipment may cause unexpected production stops, which are associated with huge costs, including the loss of production and time, the loss of effort to the identification of the failure's cause and repair, the waste of those products produced right after bringing back the system before normal operations due to low quality, costs of repairs, and deterioration of equipment [1]. In other words, maintenance directly impacts productivity since the failure of a component may cause unplanned production downtimes, the duration of which may vary depending on the type of needed action and the availability of spare parts. Besides, an item not working correctly may compromise the output quality of a production system and the safety of the working environment. The digitalization of current manufacturing industries offers a remarkable opportunity for system health management [2]. Machinery is equipped with sensors able to collect data at high frequencies; machinery in the same shop floor communicate and are connected to a central server, where the data of machinery installed in other plants can converge. In other words, Industry 4.0 technologies, like Industrial IoT, cloud computing, edge computing, and Big Data Analytics, enable the collection of a large amount of data from online condition monitoring systems. The knowledge of the exact assets' health condition in a given time instant has become an essential driver in maintenance management, since it provides the opportunity of setting efficient, just-in-time, and just-right maintenance strategies, resultingin the maximization of the production profits and the minimization of all cost and losses, including asset ones [3]. Smart factories implementing evolute maintenance strategies show a 25-30% decrease in maintenance activities, a 35-45% breakdown, a 20-25% increase in production, and an investment return [4]. The massive amount of collected data contains valuable information and knowledge supporting condition-based maintenance and health monitoring [5]. In this context, the transformation of raw data into knowledge is usually referred to as Prognostics and Health Management (PHM), which uses Big Data Analytics and Machine Learning (ML) algorithms to perform fault diagnostics and prognostics. In real scenarios, an Industrial PHM system should [6]:  
1. Include fault detection algorithms that can detect anomalies in the streaming data;
2. Include fault diagnosis algorithms that can classify the detected changes;
3. Consider incremental learning to deal with unlabeled datasets and any novel operating conditions of the machinery. In the last few years, a large number of approaches (partially reviewed in the related work section) have been proposed to deal with fault detection and diagnosis via the experimentation of supervised techniques for anomaly detection and classification. Only recently have aspects related to the concept drift and the concept evolution that may occur in streaming data acquired a paramount importance in PHM. Indeed, there are two typical problems to address in real-world scenarios. The first is that of industrial secrecy, for which the real operating conditions in which the machine operates, i.e., the recipes, are not intended to be explicit in the data analysis. The second is that of the change in the environmental conditions in which the machinery operates. The machines are tested in laboratories with conditions different to those of production. The plants are dissolved in different areas with different environmental conditions. In all these cases, the problem to be faced is that of \"novelty detection\". Novelty detection is the task of recognizing that test data differ in some respect from the data that are available during training [7]. The functionalities for a novelty detection system can be, in principle, provided by a classification model built on the datasets describing the operation conditions of the machinery under analysis.