Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data  
[PERSON]  
1 Electronics and Computing Department, Mondragon Unibertsitatea, Loramendi 4, 20500 Mondragon, Spain;  
[EMAIL_ADDRESS] (E.Z. ); [EMAIL_ADDRESS] (U.Z.) 2 Koniker, San Andres Auzoa 20, 20500 Mondragon, Spain;[EMAIL_ADDRESS] (J.R.)  
3  
[PERSON]  
1 Electronics and Computing Department, Mondragon Unibertsitatea, Loramendi 4, 20500 Mondragon, Spain;  
[EMAIL_ADDRESS] (E.Z. ); [EMAIL_ADDRESS] (U.Z.) [PERSON]  
2 Koniker, San Andres Auzoa 20, 20500 Mondragon, Spain;[EMAIL_ADDRESS] (J.R.)  
3  
[PERSON]  
2 Koniker, San Andres Auzoa 20, 20500 Mondragon, Spain;[EMAIL_ADDRESS] (J.R.)  
3  
[PERSON]  
1 Electronics and Computing Department, Mondragon Unibertsitatea, Loramendi 4, 20500 Mondragon, Spain;  
[EMAIL_ADDRESS] (E.Z. ); [EMAIL_ADDRESS] (U.Z.) 3  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/11/11/11/11/11/11/11/11/11/11/11/11/11/11/111/11/11/11/111/11/111/111/11/111/11/111/11/111/11/111/111/11/111/11/111/111/1](https://www.mdpi.com/journals/pdf/11/11/11/11/11/11/11/11/11/11/11/11/11/11/111/11/11/11/111/11/111/111/11/111/11/111/11/111/11/111/111/11/111/11/111/111/1)]([https://www.mdpi.com/journals/pdf/11/11/11/11/11/11/11/11/11/11/11/11/11/11/111/11/11/11/111/11/111/111/11/111/11/111/11/111/11/111/111/11/111/11/111/111/1](https://www.mdpi.com/journals/pdf/11/11/11/11/11/11/11/11/11/11/11/11/11/11/111/11/11/11/111/11/111/111/11/111/11/111/11/111/11/111/111/11/111/11/111/111/1))  
Besides, research on deep learning applications has grown in recent years, achieving state-of-the art results in many field including industrial applications [2]. Nowadays, many factories either use predetermined maintenance, where the components are replaced periodically without observing their degradation after a certain number of cycles or execution time, or corrective maintenance, which replaces the broken parts of an asset when it stops working or when its users detect defects. In contrast, predictive maintenance monitors the condition of a machine/component to detect early signs of failure, diagnose its root cause, and predict its evolution until failure, enabling the maintenance application when assets need it by anticipating maintenance requirements. The PdM strategy extends components' working life with respect to predetermined maintenance, and avoids damages by performing interventions before failures occur, before corrective maintenance [3]. Therefore, companies adopting PdM can achieve an overall equipment effectiveness of 90% [4] and it can achieve a 10 times return on investment [5]. The work on predictive maintenance by [PERSON] et al. [6] argues that data processing for predictive maintenance is an essential step. In addition, [PERSON] [7] defines PdM stages as 1 st anomaly detection, 2 nd diagnosis, 3 rd prognosis, and last mitigation. Each industrial use case has its own maintenance requirements and characteristics, usually differing from one another and thus requiring algorithm adaptation. Industrial companies work hard to ensure their machines are working correctly, so the majority of collected data belongs to correct working conditions. There are also issues with the registration of failures: sometimes this process does not exist, but when it does, inconsistent labeling may be used. Additionally, forcing assets to have failures is expensive, and many times it is impossible to replicate desired failure types. These are the reasons why research interest on semi-supervised models for PdM is increasing. The review by [PERSON] et al. [8] gathers the main characteristics of an anomaly detection and fault diagnosis system to address industrial process challenges. According to the review, the system should be able to detect and diagnose the anomalies quickly to facilitate fast intervention and mitigation.