# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 2 Related Works  
Unlike anomaly detection and outlier detection, novelty detection aims to find a set of points not explained from the diagnosis model, instead of one single point differing from the nominal or known data [8]. In addition, novelty detection is also included in multi-class systems, where two or more normal classes exist, and a condition is considered novel if it differs from all of the known classes [14]. In other words, the problem becomes recognizing novelties and, at the same time, classifying the known instances into two or more diverse classes. To this aim, traditional classification and clustering-based approaches are adopted for system Health Monitoring in industrial environments. In these cases, novelty detection approaches may be classified according to two criteria: the learning paradigm, which can be supervised or unsupervised, and the necessity of a training phase before the streaming application. Hence, three different approaches can be distinguished:  
1. Classification-based with training on one or more classes (e.g., COMPOSE [15; 16]);
2. Clustering-based with training on one or more classes (e.g., OLINDDA, MINAS [17; 18; 19]);
3. Clustering-based that can be applied from scratch (e.g., ADP [20]). In addition, classification-based approaches may adopt an incremental or non-incremental learning paradigm, depending on whether the classification model is retrained when a novel class is detected. On the contrary, all clustering-based approaches adopt an incremental learning paradigm. More recently, deep-learning models--such as autoencoders networks [12; 13; 21] and Generative Adversarial Networks (GAN) [22]--have been used in this field. GAN-based approaches have been proposed as anomaly detection systems, to learn a latent feature space of a generative network G so that the latent space well captures the normality underlying the given data. Some form of residual between the real instance and the generated instance is then defined as an anomaly score. Nevertheless, the training of GANscan suffer from multiple problems, such as failure to converge and mode collapse, which leads to large difficulties in training GAN-based anomaly detection models. Moreover, the generator network can be misled and generate data instances out of the manifold of normal instances, especially when the true distribution of the given dataset is complex or the training data contain unexpected outliers. Autoencoders have been experimented in the field of the novelty detection. In particular, ref. [12] proposes an unsupervised AE LSTM architecture for the novelty detection task, intending to identify novel conditions in the machinery, but without focusing on the ability to distinguish the existing operating ones. The main paper innovation consists of the introduction of a supervised neural network that analyzes the incoming time series, classifying the type of fault in a supervised manner. Instead, ref. [13] introduces an AE architecture that combines the novelty detection part with the diagnosis (classification) part of the operating state. The innovation of the proposed architecture is the combination of the two metrics in a single optimization function. The approach requires labels on the data. As highlighted in the introduction, our approach follows this line of research by introducing a template architecture based on an autoencoder implemented with different deep learning models able to recognize novel operating conditions and classify the current one among the ones learned in a unsupervised manner. It relies on online learning: once a novel status is found, a retrain is needed. It differs from the current approaches in depth and breadth. We differ in depth since we propose a technique that is able to detect novel operating conditions, classify the current conditions and discover the signal that has most contributed to the inference in an unsupervised manner.