In this study, we developed a deep learning-based real-time fault diagnosis system to automate the weaving preparation process in textile manufacturing. By analyzing typical faults such as shaft eccentricity and rotational imbalance, we designed a data-driven fault diagnosis algorithm. We utilized tension data from both normal and faulty states to implement AI-based diagnostic models, including 1D CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and LSTM-AE (Long Short-Term Memory Autoencoder). These models enable real-time fault classification, followed by a comparative performance analysis. The LSTM-AE model achieved the best performance, with a classification accuracy of 99-100% for severe faults, such as 1.5 mm eccentricity and 100 or 150 g rotation imbalance, and 92.2% for minor faults like 1 mm eccentricity. This accuracy was optimized through threshold adjustments based on ROC curve analysis to select an optimal threshold. Building on these findings, we developed a GUI (Graphical User Interface) system capable of real- time fault diagnosis using TCP/IP (Transmission Control Protocol/Internet Protocol) communication between Python and LabVIEW. The results of this study are expected to accelerate the smartization of the weaving preparation process, contributing to improved textile quality and reduced defect rates, while also serving as a model for automation in other sectors.

This paper proposes a high-fidelity finite element model of a permanent synchronous motor (PMSM) to predict electromagnetic responses. The proposed method aims to generate electromagnetic responses from the PMSM under various operational conditions-including normal and faulty conditions-by coupling several partial differential equations governing the electromagnetics of a PMSM. The rotor eccentricity is considered to be a representative fault of a PMSM, which has electromagnetic characteristics that differ from the healthy state of a PMSM. Note that eccentricity is the most frequent fault during PMSM operation. Therefore, the proposed model could replicate the defected torque responses of an actual motor system. The effectiveness of the proposed model is validated using measurements from a PMSM test bench. Quantitative comparison reveals that the proposed model could replicate both the transient- and steady-state torque responses of the PMSM of interest at a variety of operational conditions, including a faulty status. The proposed model could be used to generate virtual electromagnetic responses of a PMSM, which could be used for data-driven fault detection methods of electric motor systems.

Lithium-ion batteries are one of the main parts of electrical devices and are widely used in various applications. To safely use lithium-ion batteries, fault diagnosis and prognosis are significant. This paper analyzes resistance parameters from electrochemical impedance spectroscopy (EIS) to detect the fault of lithium-ion batteries. The internal fault mechanisms of batteries are so complex; it is difficult to detect abnormalities by direct current-based methods. However, by using alternating-current-based impedance by EIS, the internal degradation processes of the batteries can be detected. Impedance variation from EIS is verified under accelerated degradation test conditions and normal cycling test conditions. The results showed a significant relationship between fault and increase in resistance.

A smart factory with Big Data analytics is getting attention because of its ability to automate and make the manufacturing environment more intelligent. At the same time, higher reliability is required with a drastic increase in complexity and uncertainty within the current system of manufacturing fields. The pump is considered as one of the most crucial equipment as it can affect the overall manufacturing performance of the manufacturing processes and it needs to be timely diagnosed of its mechanical condition as a top priority. In this research, we propose an operation system of centrifugal pumps and a data-driven fault diagnostic model that is developed by collecting relevant multivariate data from several natures. Proposed machine learning models can be used for detecting and diagnosing pump faults via analytical processes containing signal preprocessing and feature engineering procedures. Simulation and case studies from rotating machinery have demonstrated the effectiveness of the proposed analytical framework not only for attaining quantitative reliability but practical usages in actual manufacturing fields as well.

Recently, improvement of productivity of the paper cup forming machine has being conducted by increasing manufacturing speed. However, rapid manufacturing speed imposes high load on cams and cam followers. It accelerates wear and cracking, and increases paper cup failure. In this study, a failure diagnosis algorithm was suggested using vibration data measured from cam driving parts. Among various paper cup forming processes, a test bed imitating the bottom paper attaching process was manufactured. Accelerometers were installed on the test bed to collect data. To diagnose failure from measured data, the K-NN (K-Nearest Neighbor) classifier was used. To find a decision boundary between normal and abnormal state, learning data were collected from normal and abnormal state, and normal and abnormal cams. A few representative features such as mean and variance were selected and transformed to the relevant form for the classifier. Classification experiments were performed with the developed classifier and data gathered from the test bed. According to assigned K values, a successful classification result was obtained which means appropriate failure recognition.