# An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data
## 1 Introduction
Therefore, it is not easy to build an applicable model in this way [10]. To overcome these drawbacks, some methodologies define a target Health Index (HI) curve so that failure history data can at least be built on models [6; 10; 11; 12]. This allows the model to learn data by looking at the time of failure as 0 and defining the Health Index degradation curve of the equipment [13]. However, this method also has the following problems. First, the predefined Health Index degradation curves may not fit the actual equipment. Second, even under the same conditions, different Health Index degradation curves may exist. Third, the Health Index degradation curve may vary depending on initial conditions, such as maintenance conditions [14]. Therefore, many existing studies have assumed that there is a failure history data or have generated failure data through simulation to prove the effectiveness of the proposed method [15; 16]. However, this is also not applicable to the real methodology. Also, there are some methods for the unsupervised-based model [17; 18; 19]. These studies only applicable when there are specific data sources: time waveform data or Fast Fourier Transform (FFT) data. When a device acquired a vibration signal and its amplitude plotted against time, it is called a time waveform. And FFT data are induced by applying FFT to the time waveform, and it is described by amplitude against frequency domain. In this paper, we propose a new PdM framework that applies to real situations. The proposed method is applicable even when there is no fault history data. In addition, since each model is made for each equipment, there is a possibility that the model can be elaborately updated if the failure history is accumulated later. The proposed method consists of three main elements. First, the raw data of the equipment are preprocessed to make it applicable to the model. Second, the model is made by inputting the preprocessed data into the autoencoder model as the training data, and the Health Index is made by comparing it with the training data when the new data are entered. Third, the remaining useful life of the equipment is predicted based on the HI pattern. The main contributions of this paper are summarized as follows:In order to be applicable in the field, we propose an autoencoder (AE) based methodology. This is an algorithm that can be modeled directly from the normal data without the failure data. * In order to apply various models in the future, we divided the steps of making HI and of predicting RUL in the proposed framework. This is the standard applied in recent PdM studies [20]. * The proposed framework is applied to real data cases, not simulation data, to prove the practicality and feasibility of the proposed methodology. The remainder of this paper is organized as follows: In Section 2, the basic theory of autoencoder and the prediction method is presented in detail. In Section 3, we describe the details of the proposed framework, which are applicable even in the absence of run-to-failure data. In Sections 4 and 5, we describe the application of the proposed framework to real cases to demonstrate the practicality of the proposed method. We conclude the paper in Section 6 and discuss future directions.