Pattern Detection Model Using a Deep Learning Algorithm for Power Data Analysis in Abnormal Conditions

• Main Authors: Jeong-Hee Lee, Jongseok Kang, We Shim, Hyun-Sang Chung, Tae-Eung Sung
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Electronics
• Subjects: manufacturing big-data; fault detection; abnormal pattern; labelling; neural network; hybrid model
• Online Access: https://www.mdpi.com/2079-9292/9/7/1140

Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites.