# An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data
## 1 Introduction  
Recently, Developments in the Industrial Internet of Things (IIoT) have made it possible to collect and process large amounts of data in sensors and computer-connected machinery [1]. In addition, it is possible to analyze not only the process data generated during the production of the product but also the interest in the equipment's Prognostics and Health Management (PHM) based on the equipment data, which is the data of the equipment itself [2]. Initial PHMs, called reactive maintenance, are used until the equipment fails and the equipment is repaired after the failure. This method has a low repair cost but leads to a very expensive incident in the case of equipment failure. To overcome these shortcomings, time-based maintenance (TBM) and condition-based maintenance (CBM), called preventive maintenance, have emerged. TBM is a method of repairing equipment regardless of the failure over the known life of the equipment. The disadvantage of this is that more maintenance than necessary is performed because maintenance is performed at a predetermined time, even without an event of failure. In addition, CBM is a method to perform maintenance when an abnormal symptom of equipment occurs [3]. It is a more advanced method than TBM because it performs maintenance when an abnormal indication occurs. However, an abnormal indication does not necessarily mean that the equipment is malfunctioning because there is also the problem of false alarms. In order to overcome this drawback, Predictive Maintenance (PdM) has recently been proposed. PdM continually detects anomalies in the equipment through condition monitoring, andthen determines when the equipment is predicted to fail using predictive models [4]. This allows the engineer to maximize the uptime of the equipment and significantly reduce the cost of the incident by preparing parts for maintenance in advance [2]. These general PdM methodologies can be broadly classified into two categories: physical model-based and data-based methodologies [5]. First, the methodology based on the physical model predicts the failure of a facility by using a known failure model or by a mathematical model. These are methodologies that rely on the experience and knowledge of engineers, which are very limited and inadaptable to a variety of environments and complex real-world applications [6; 7; 8]. Conversely, the data-based methodology uses statistical or machine learning algorithms to calculate the health of the equipment based on the sensor data such as vibration, temperature, and pressure. This builds a health model of the equipment without any assumptions about the equipment, provided there are enough data to model it. This is a more flexible and applicable methodology since, in practice, the equipment is exposed to a variety of environments and is likely to behave differently from previously defined models. In addition, this methodology is essential because, for complex equipment, it is nearly impossible to produce realistic models [5; 7]. Recently, research into data-based PdM has been actively conducted due to the development of sensors and computing technologies [9]. Data-based methodologies can also be classified according to whether run-to-failure data exist or not as follows: a supervised learning method with failure data, and an unsupervised learning method without failure data [7]. The methodology using supervised learning must have sufficient target value for learning; that is, the failure history data called so-called run-to-failure data. Reliable models created using supervised methods can only be trusted if there is a sufficient history of failure [8]. However, in the real world, there are very few cases where there is sufficient failure history data. First, the equipment data that occur in real-time is rarely stored as a whole. Second, it is unlikely that data will be present when the actual failure occurred by fixing the equipment before the failure. Third, it is difficult to store the data generated at the specific time of failure.