Induction motors are robust and cost effective; thus, they are commonly used as power sources in various industrial applications. However, due to the characteristics of induction motors, industrial processes can stop when motor failures occur. Thus, research is required to realize the quick and accurate diagnosis of faults in induction motors. In this study, we constructed an induction motor simulator with normal, rotor failure, and bearing failure states. Using this simulator, 1240 vibration datasets comprising 1024 data samples were obtained for each state. Then, failure diagnosis was performed on the acquired data using support vector machine, multilayer neural network, convolutional neural network, gradient boosting machine, and XGBoost machine learning models. The diagnostic accuracies and calculation speeds of these models were verified via stratified K-fold cross validation. In addition, a graphical user interface was designed and implemented for the proposed fault diagnosis technique. The experimental results demonstrate that the proposed fault diagnosis technique is suitable for diagnosing faults in induction motors.

Lithium-ion batteries are commonly used in electric vehicles, mobile phones, and laptops because of their environmentally friendly nature, high energy density, and long lifespan. Despite these advantages, lithium-ion batteries may experience overcharging or discharging if they are not continuously monitored, leading to fire and explosion risks, in cases of overcharging, and decreased capacity and lifespan, in cases of overdischarging. Another factor that can decrease the capacity of these batteries is their internal resistance, which varies with temperature. This study proposes an estimation method for the state of charge (SOC) using a neural network (NN) model that is highly applicable to the external temperatures of batteries. Data from a vehicle-driving simulator were used to collect battery data at temperatures of 25 °C, 30 °C, 35 °C, and 40 °C, including voltage, current, temperature, and time data. These data were used as inputs to generate the NN models. The NNs used to generate the model included the multilayer neural network (MNN), long short-term memory (LSTM), gated recurrent unit (GRU), and gradient boosting machine (GBM). The SOC of the battery was estimated using the model generated with a suitable temperature parameter and another model generated using all the data, regardless of the temperature parameter. The performance of the proposed method was confirmed, and the SOC-estimation results demonstrated that the average absolute errors of the proposed method were superior to those of the conventional technique. In the estimation of the battery’s state of charge in real time using a Jetson Nano device, an average error of 2.26% was obtained when using the GRU-based model. This method can optimize battery performance, extend battery life, and maintain a high level of safety. It is expected to have a considerable impact on multiple environments and industries, such as electric vehicles, mobile phones, and laptops, by taking advantage of the lightweight and miniaturized form of the Jetson Nano device.

산업 시스템의 발전으로 인해 제조업, 중공업 등에서의 유도전동기 사용이 증가하고 있다. 전동기의 장기간 사용으로 결함이 발생할 수가 있으며 그러한 결함들은 기계의 성능과 수명을 단축시키고 산업적인 측면에서 물질적, 시간적 손해 및 사용자의 안전사고로 이어질 수가 있다. 그러므로 전동기의 고장을 정확하게 진단하는 것은 매우 중요한 문제이다. 본 논문에서는 유도전동기 시뮬레이터에서 정상, 회전자 고장 및 베어링 고장 데이터를 획득하였고 학습으로는 상태별 데이터를 각각 620쌍, 총 1860쌍을 사용하였고 테스트는 학습과 동일한 개수의 데이터를 사용하였다. 유도전동기의 각 상태별 특징을 학습하기 위해 결정 트리 앙상블 방법인 랜덤포레스트 방법을 사용하였다. 제안한 알고리즘인 랜덤 포레스트와 그래디언트 부스팅 및 다층신경망을 사용하여 성능을 비교하였고, 실험결과 제안한 모델의 분류 성능이 가장 우수함을 확인하였다.