Recently, interest in Prognostics and Health management (PHM) has been increasing as an advanced technology of maintenance. PHM technology is a technology that allows equipment to check its condition and predict failures in advance. To realize PHM technology, it is important to implement artificial intelligence technology that diagnoses failures based on data. Vibration data is often used to diagnose the state of the rotating machine. Additionally, there have been many efforts to convert vibration data into 2D images to apply a convolutional neural network (CNN), which is emerging as a powerful algorithm in the image processing field, to vibration data. In this study, a series of PHM processes for acquiring data from a rotary machine and using it to check the condition of the machine were applied to the rotary table. Additionally, a study was conducted to introduce and compare two methodologies for converting vibration data into 2D images. Finally, a GUI program to implement the PHM process was developed.

Due to recent development of sensor technology and IoT, research is being actively conducted on PHM (Prognostics and Health Management), a methodology that collects equipment or system status information and determines maintenance using diagnosis and prediction techniques. Among various research studies, research on anomaly detection technology that detects abnormalities in assets through data is becoming more important due to the nature of industrial sites where it is difficult to obtain failure data. Conventional machine learning-based and statistical-based models such as PCA, KNN, MD, and iForest involve human intervention in the data preprocessing process. Thus, they are not suitable for time series data. Recently, deep learning-based anomaly detection models with better performances than conventional machine learning models are being developed. In particular, several models with improved performance by fusing time series data with LSTM, AE (Autoencoder), VAE (Variational Auto Encoder), and GAN (Generative Adversarial Network) are attracting attention as anomaly detection models for time series data. In the present study, we present a method that uses Likelihood to improve the evaluation method of existing models.