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.
To develop a technology to diagnose the fault of dampers applied to railway vehicles and to set criteria, test runs were performed to measure damping force and displacement acting on a lateral damper during vehicle operation. Normal damper and fault damper were installed on a test train. Damper force and velocity of the lateral damper during test running were measured. Distributions of damper force and velocity representing the state of the damper had the same distribution in repeated tests. Distribution of the damper force and velocity was consistently uniform regardless of the train driving direction. Thus, the effect of train driving direction on damper force and velocity distribution was insignificant. The fault of the damper appeared to have a direct effect on the distribution of the damper force, suggesting that the fault of the damper could be sufficiently diagnosed just by monitoring the force of the damper. Especially, when comparing the velocity-force distribution, the fault damper showed a clear difference from a normal damper. Results of this paper could be used for developing a technology for diagnosing damper fault for railway vehicles in the future.
The Steer-by-Wire (SbW) system is a system that eliminates the physical connection structure of the steering system. Instead, it steers the tires through electrical communication that transmits the driver’s intention to the motor. However, the SbW system poses a significant risk in the event of a system failure. This highlights the need for effective failure backup strategies.In our study, we propose a new estimation technique. This technique accurately predicts the magnitude of the front wheel steering angle, which is determined by the vehicle motion. This prediction is particularly useful when rear wheel steering and differential braking are applied to facilitate vehicle steering in the event of a fatal SbW system failure. The estimation model is derived based on the single track model. To construct the prediction model using only measurable states, we replaced the side slip angle with the lateral acceleration signal. Additionally, we incorporated compensation for changes in cornering stiffness due to differential braking. This enhances the accuracy of the model. We validated the proposed steer angle estimation model in a virtual environment using CarSim SW and MATLAB/Simulink.
The Technical Specification for Interoperability (TSI) legally mandates the prediction and verification process of the Reliability, Availability, Maintainability and Safety (RAMS) in signaling and communication systems. Recently, domestic regulations, including the Railroad Safety Act, have been strengthened in order to better meet the requirements for participating in international projects. To comply with these regulatory requirements, manufacturers and development organizations must prepare verification data pertaining to the reliability and safety of railway components and related systems. This paper aims to analyze the requirements of Failure Mode, Effects and Criticality Analysis (FMECA) through international laws and standards, and subsequently propose a compliant FMECA system for the domestic railway industry. The proposed FMECA system is then compared with the analysis results of actual failure data to determine its suitability for establishing a Reliability, Availability, Maintainability (RAM) verification standard for railway products in relation to conformity assessment.
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.
In this study, the production process of eccentric head bolts that fasten flanges for water supply pipe connections, which can only be achieved through the cold forging process, was improved. For axial forging, forming analysis was performed for a 200-ton header machine to check the raw material specifications, forming load, and metal flow improvements suitable for forming. The analysis found that the forging of high-strength bolts of M14×65 ㎜ with eccentric heads was possible under the maximum load condition of 137.2 tons with low carbon boron steel of ø13.8×89.7 ㎜ and 105.2 g. By mounting the prototype mold on the header machine, it was possible to prevent metal flow breakage, as shown by the trial mass production test. It was possible to improve the strength of the eccentric head bolt and reduce the weight of the input material through the cutting process. Therefore, manufacturing costs could be reduced.
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.
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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.
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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.
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Commercial air-conditioning systems are widely used for buildings of various sizes. Design and installation of these systems follow a certain guideline developed by the manufacturer. The guideline also includes the adequate amount of refrigerant to be charged into the system. However, the guideline is often insufficient to reflect all the characteristics of installation, which results in too little or too much refrigerant. Inadequate amount of refrigerant usually causes more power consumption and reduced air-conditioning / heating capacity. This paper focuses on identifying the relationship between adequate refrigerant amount and various state variables such as condensation temperature of the air-conditioning system. This is based on regression analysis of data obtained through the experiments under controlled temperature and humidity.
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As emission regulation of vehicles is being reinforced globally, the current requirement of the automobile industry are innovative green technologies that reduce the weight of the vehicle, thereby improving fuel consumption and the amount of exhaust gas emission. The application of ultra-high strength steel (UHSS) for vehicles has specifically been studied for light weight of vehicles. UHSS withstands greater loads than a general steel sheet of the same thickness. The spring-back and formability of the UHSS are also worse than general steel sheet due to their high elasticity and high yield strength. Various methods applied for processing UHSS include roll-forming and hot-press forming. However, these processes have not only high installation cost but also low productivity. This study therefore developed the cold-press forming method to overcome these disadvantages. The objective of this study is to determine the optimum conditions of the cold press required to form the upper seat track using UHSS. Forming analysis predicted the spring-back at each stage of the press forming. The prediction of spring-back was compared with the manufactured upper seat track by try-out, thereby reducing trial and error in the pressing process.
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A paper cup forming machine performs the entire process to produce paper cups. Recently, as the demand for paper cups in various fields increases, the need for rapid and timely paper cup forming also increases. However, the more rapid the manufacturing speed is, the higher the possibility of forming failure. Frequent fault occurrences cause a time-consuming and costly repair process and reduces manufacturing efficiency. Among various fault factors in this research, position deviation of the paper from the original position, which induces a jamming and process stop, was selected and a novel deviation detecting system using multiple photo sensors was suggested. Before operating the position detecting system, the performance of the photo sensors was evaluated with respect to response speed and photo beam precision. A deviation detecting mechanism was designed. The developed deviation detecting system was integrated with the paper cup forming machine and experimented with using base papers. It was conformed that the suggested system could be used to diagnose paper deviation failure.
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The Development of a Failure Diagnosis System for High-Speed Manufacturing of a Paper Cup-Forming Machine Seolha Kim, Jaeho Jang, Baeksuk Chu Journal of the Korean Society of Manufacturing Process Engineers.2019; 18(5): 37. CrossRef
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