This study discusses a failure detection algorithm that uses frequency analysis and artificial intelligence to determine whether a rotor used in an industrial setting has failed. A rotor is a standard component widely used in industrial sites, and continuous friction and corrosion frequently result in motor and bearing failures. As workers inspecting failure directly are at risk of serious accidents, an automated environment that can operate unmanned and a system for accurate failure determination are required. This study proposes an algorithm to detect faults by introducing convolutional neural networks (CNNs) after converting the fault sound from the rotor into a spectrogram through STFT analysis and visually processing it. A binary classifier for distinguishing between normal and failure states was added to the output part of the neural network structure used, which was based on the transfer learning methodology. We mounted the proposed structure on a designed embedded system to conduct performance discrimination experiments and analyze various outcome indicators using real-world fault data from various situations. The analysis revealed that failure could be detected in response to various normal and fault sounds of the field system and that both training and validation accuracy were greater than 99%. We further intend to investigate artificial intelligence algorithms that train and learn by classifying fault types into early, middle, and late stages to identify more specific faults.

WESPs (Wet Electrostatic precipitators) are mainly installed in industries and factories where PM (particulate matter) is primarily generated. Such a wet type WESPs exhibits very excellent performance by showing a PM collection efficiency of 97 to 99%, but the PM collection efficiency may decrease rapidly due to a situation in which the dust collector and the discharge electrode is corroded by water. Thus, developing technology to predict efficient PM collection in the design and operation of WESPs is critical. Previous studies have mainly developed machine learning-based models to predict atmospheric PM concentrations using data measured by meteorological agencies. However, the analysis of models for predicting the dust collection efficiency of WESPs installed in factories and industrial facilities is insufficient. In this study, a WESPs was installed, and PM collection experiments were conducted. Nonlinear data such as operating conditions and PM measurements were collected, and ensemble PM collection efficiency prediction models were developed. According to the research results, the random forest model yielded excellent performance, with the best results achieved when the target was PM 7: R2, MAE, and MSE scores of 0.956, 0.747, and 1.748, respectively.