Supervised-Learning-Based Intelligent Fault Diagnosis for Mechanical Equipment

Recently, anomaly detection for improving the productivity of machinery in industrial environments has drawn considerable attention. As large-scale data collection and processing are becoming easier owing to technological developments, data-based deep-learning technology is being developed to detect anomalies in mechanical equipment operation. This study proposes an ensemble model that combines stacked two-dimensional and one-dimensional convolutional neural networks (CNNs), residual long short-term memory (LSTM), and LSTM based on supervised learning. The model, which is called the SCRLSTM model, can detect abnormal data generated by mechanical equipment. The proposed model can extract the spatial features of data using a CNN model and detect anomalous states in the time-series-based vibration datasets of machinery under various environments through residual LSTM. To verify this model, data augmentation was applied to the original time-series-based mechanical vibration dataset, which had unbalanced samples that lowered the performance of the abnormal anomaly detection model. In addition, an image-based analysis was performed by converting time-series-based raw-signal data to Mel-spectrogram images, thereby achieving better performance in the fault diagnosis system to which data augmentation was applied. The proposed SCRLSTM model shows better performance than other supervised-learning-based models on datasets having different lengths under various conditions. This indicates that the proposed anomaly detection model can be expected to improve the productivity of mechanical equipment in industrial settings.