Hwi Gyo Lee

Industrial noise can degrade acoustic fault diagnosis differently across input representations. This study evaluated whether classifier-independent waveform restoration could reduce this disparity in BLDC motor diagnosis. A Wave-U-Net denoiser was trained without classifier losses or outputs and applied before generating time-domain waveform and STFT magnitude inputs for six CNN, LSTM, and CNN–LSTM classifiers, which remained unchanged. The dataset comprised 13,790 one-second acoustic segments recorded from a single BLDC motor under normal operating conditions and bearing, eccentricity, and electrical fault conditions. Industrial noise environments were simulated by mixing the recorded motor signals with external noise recordings from the MIMII dataset. At 0 dB, restoration increased mean classification accuracy from 69.27% to 97.37% and reduced the mean performance gap between the two input representations from 48.61 to 1.01 percentage points. Improvements were also observed at evaluated SNRs below the training range and under noise types excluded from denoiser training. Grad-CAM and feature-space analyses indicated that restoration shifted decision patterns and class-wise feature structures toward those observed without added industrial noise. These findings support waveform restoration as a common preprocessing approach for reducing representation-dependent degradation under the evaluated conditions.

Bearing faults are the most common type of failure in induction motors, given their long operating times and mechanical loads. Because induction motors in industrial environments operate under various load conditions, effective methods for diagnosing bearing faults across these conditions have become increasingly important. Here, different load conditions were implemented with a powder clutch and a tension controller, and vibration data were acquired under both normal and faulty bearing conditions. To ensure diagnostic accuracy while improving time efficiency, a model bank-based fault diagnosis classifier is proposed, which utilizes independent classifiers trained for each load condition. For comparison, a single model-based classifier trained on all load conditions is also implemented. Both approaches are validated with three classifiers: support vector machine (SVM), multilayer neural network (MNN), and random forest (RF), with three input types: raw time-series signals, six statistical features, and three t-test–selected statistical features. Experimental results reveal that the model bank-based fault diagnosis classifier utilizing three statistical features selected by t-test maintained 98–100% accuracy while reducing operating time compared with Method 1 by 60.0, 71.2, and 60.0% for SVM, MNN, and RF, respectively. These results confirm that the proposed Method 2 utilizing time-domain analysis provides reliable and time-efficient performance for bearing fault diagnosis under variable load conditions.

본 논문에서는 저가형 압전 센서를 이용하여 주파수 영역에서 유도전동기의 고장을 진단하는 시스템을 구현하였다. 정상, 회전자 고장 및 베어링 고장 상태의 유도전동기를 가진 시뮬레이터를 활용하여 진동 데이터를 수집하였고, 수집된 데이터를 주파수 영역으로 변환하기 위해 FFT(Fast Fourier Transform)를 적용하였다. 변환된 주파수 영역의 데이터를 입력 벡터로 하여 MNN(Multilayer Neural Network)과 1D-CNN(1D-Convolutional Neural Network) 모델을 구성하고, 두 모델 간 분류 성능을 비교하는 컴퓨터 시뮬레이션을 수행하였다. 또한, 학습된 모델을 기반으로 Raspberry Pi와 GUI를 활용한 실시간 고장 진단 프로그램을 개발하였으며, 실제 환경에서 진단 성능을 분석함으로써 저가형 하드웨어 기반에서도 효과적인 고장 진단 시스템을 구현할 수 있음을 실험적으로 검증하였다.

Induction motors are the dominant choice in industrial applications due to their robustness, structural simplicity, and high reliability. However, extended operation under extreme conditions, such as high temperatures, overload, and contamination, accelerates the degradation of internal components and increases the likelihood of faults. These faults are challenging to detect, as they typically develop gradually without clear external indicators. To address this issue, the present study proposes a cost-effective fault diagnosis system utilizing low-cost MEMS acoustic sensors in conjunction with a lightweight multilayer neural network (MNN). The same MNN architecture is employed to systematically compare three types of input feature representations: raw time-domain waveforms, FFT-based statistical features, and PCA-compressed FFT features. A total of 5040 samples were used to train, validate, and test the model for classifying three conditions: normal, rotor fault, and bearing fault. The time-domain approach achieved 90.6% accuracy, misclassifying 102 samples. In comparison, FFT-based statistical features yielded 99.8% accuracy with only two misclassifications. The FFT + PCA method produced similar performance while reducing dimensionality, making it more suitable for resource-constrained environments. These results demonstrate that acoustic-based fault diagnosis provides a practical and economical solution for industrial applications.