Implementation of Spectrogram-based Learning Algorithm for Failure Prediction of RTO Rotary Motor : An Algorithm Study through Vibration Analysis

The purpose of this study is to develop an AI-based diagnostic system capable of accurately predicting failures of the rotary motor used in regenerative thermal oxidizer (RTO) equipment. The system utilizes vibration data, which is critical for machinery condition monitoring. To achieve this, vibration and phase current data of the motor were collected using an RC low-pass filter with a cutoff frequency of 1 kHz, and the data were sampled at a rate of 1 kHz. The collected data were transformed into spectrograms via Fast Fourier Transform (FFT) and used as input for the model. A deep learning model based on a ResNet architecture was designed and trained to predict motor conditions using the spectrograms. The model’s performance was evaluated using key metrics such as accuracy and loss. Experimental results demonstrate that the proposed algorithm effectively analyzes vibration signals and accurately predicts the rotary motor’s condition. This outcome suggests the potential application of AI-based diagnostic systems in real-time monitoring and preventive maintenance of RTO equipment. In particular, by enabling early fault detection of rotary motors—which control the inflow and outflow of gases in environments exceeding 800°C—the proposed system significantly improves safety compared to conventional field operations.