# An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data
## 7 Conclusions  
In this paper, we propose a Predictive Maintenance (PdM) framework that can be applied even in the absence of run-to-failure data. In particular, if only normal data can be defined, it is a methodology that can perform PdM. Many existing studies have relied on simulation data. In addition, because it is essential to have a considerable amount of run-to-failure data, it was challenging to apply instantly to the industry despite the high experimental accuracy. However, this paper proposes a framework based on autoencoder and simple linear regression to generate and monitor models even in the absence of run-to-failure data. Since the proposed methodology is roughly divided into the construction part and prediction parts of the HI, relatively simple algorithms are used to demonstrate the framework in this paper. Other unsupervised learning that suitable for the industry can be used in addition to the autoencoder. Furthemore, it is possible to improve the accuracy of the model by using a more appropriate prediction methodology in addition to simple linear regression. Furthermore, the configuration can be updated based on the failure data. In this way, a more the proposed framework is applied, as knowledge of failure data is accumulated, the more improve the performance.  
The proposed framework was carried out in two different real cases, even though they are a completely different domain, confirming the usefulness and applicability of the proposed methodology. In case study 1, abnormal signs were detected prior to the time of failure. In addition, because the failure type has different thresholds, the possibility of failure type classification was also confirmed. If there is no run-to-failure history, the initial accuracy of the proposed methodology may be low, but we can increase the accuracy of the model by re-training the model, as shown in case study 2. More sophisticated models can be performed if there is a history of past failures or with the knowledge of the industry. In the case studies, we used summary data rather than complicated preprocessing, and the feasibility is increased. Since feature extraction generally promotes the capability of the network-based Predictive Maintenance model [31]. In addition, through a simple additional experiment, the effectiveness of the proposed method was confirmed.  
In addition, this study raised the value of research by presenting difficulties in advance that can be experienced in actual application. However, there is some limitation of the proposed method. In this paper, the normal data points are determined more or less arbitrarily as is the threshold. In future research, the threshold needs to be defined based on the advanced algorithm, normal points in the data should be determined by someone who understands the industry well.  
[PERSON]. ([PERSON]) proposed the idea and carried out all the experiments, [PERSON] validated the process and [PERSON] ([PERSON]) guided the research. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the World Class 300 Project (R&D) (S2641209, \"Improvement of manufacturing yield and productivity through the development of next generation intelligent Smart manufacturing solution based on AI & Big data\") of the MOTIE, MSS (Korea).  
**Conflicts of Interest:** The authors declare no conflict of interest.