Fault diagnosis based on the quantification of the fault features in a rotary machine

Self Cite

Gravure printing, which is a roll-to-roll printed electronics system suitable for high-speed patterning of functional layers have advantages of being applied to flexible webs in large areas. As each of the printing procedure from inking to doctoring followed by ink transferring and setting influences the quality of the pattern geometry, it is necessary to detect and diagnose factors causing the printing defects beforehand. Data acquisition with three triaxial acceleration sensors for fault diagnosis of four major defects such as doctor blade tilting fault was obtained. To improve the diagnosis performances, optimal sensor selection with Sensor Data Efficiency Evaluation, sensitivity evaluation for axis selection with Directional Nature of Fault and feature variable optimization with Feature Combination Matrix method was applied on the raw data to form a Smart Data. Each phase carried out on the raw data progressively enhanced the diagnosis results in contents of accuracy, positive predictive value, diagnosis processing time, and data capacity. In the case of doctor blade tilting fault, the diagnosis accuracy increased from 48% to 97% with decreasing processing time of 3640 s to 16 s and the data capacity of 100 Mb to 5 Mb depending on the input data between raw data and Smart Data.

Section: Introductionmentioning

confidence: 97%

Impact of Sensor Data Characterization with Directional Nature of Fault and Statistical Feature Combination for Defect Detection on Roll-to-Roll Printed Electronics

Lee

Cho

et al. 2021

Self Cite

“…Machine learning methods and techniques generally follow a sequence of sensor-data collection, data-quality assessment, feature extraction, feature selection, and model training [ 29 , 30 ]. Data are collected from sensors attached to the machine, and vibration sensors have proven to be effective in diagnosing faults in rotating components in several studies [ 31 , 32 , 33 , 34 ]. The collected data are quantitatively evaluated for suitability in fault classification, and fault characteristics are quantified while selecting the optimal sensor [ 29 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

“…Data are collected from sensors attached to the machine, and vibration sensors have proven to be effective in diagnosing faults in rotating components in several studies [ 31 , 32 , 33 , 34 ]. The collected data are quantitatively evaluated for suitability in fault classification, and fault characteristics are quantified while selecting the optimal sensor [ 29 , 34 ]. As the measured signal contains noise, feature extraction is performed to extract only the information that reflects the state of the diagnosis target, excluding noise [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

Lee,

et al. 2023

Self Cite

Roll-to-roll manufacturing systems have been widely adopted for their cost-effectiveness, eco-friendliness, and mass-production capabilities, utilizing thin and flexible substrates. However, in these systems, defects in the rotating components such as the rollers and bearings can result in severe defects in the functional layers. Therefore, the development of an intelligent diagnostic model is crucial for effectively identifying these rotating component defects. In this study, a quantitative feature-selection method, feature partial density, to develop high-efficiency diagnostic models was proposed. The feature combinations extracted from the measured signals were evaluated based on the partial density, which is the density of the remaining data excluding the highest class in overlapping regions and the Mahalanobis distance by class to assess the classification performance of the models. The validity of the proposed algorithm was verified through the construction of ranked model groups and comparison with existing feature-selection methods. The high-ranking group selected by the algorithm outperformed the other groups in terms of training time, accuracy, and positive predictive value. Moreover, the top feature combination demonstrated superior performance across all indicators compared to existing methods.

“…A fault diagnosis system aims to improve the productivity of a process and reduce the costs by monitoring the manufacturing process and notifying it of any failures in advance [1][2][3][4][5][6]. Machining is one of the processing methods used in product manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process

Park

Lee

Yeo

et al. 2022

Self Cite

Fault diagnosis systems are used to improve the productivity and reduce the costs of the manufacturing process. However, the feature variables in existing systems are extracted based on the classification performance of the final model, thereby limiting their applications to models with different conditions. This paper proposes an algorithm to improve the characteristics of feature variables by considering the cutting conditions. Regardless of the frequency band, the noise of the measurement data was reduced through an oversampling method, setting a window length through a cutter sampling frequency, and improving its sensitivity to shock signal. An experiment was subsequently performed to confirm the performance of the model. Using normal and wear tools on AI7075 and SM45C, the diagnosis accuracies were 97.1% and 95.6%, respectively, with a reduction of 85% and 83%, respectively, in the time required to develop a diagnosis model. Therefore, the proposed algorithm reduced the model computation time and developed a model with high accuracy by enhancing the characteristics of the feature variable. The results of this study can contribute significantly to the establishment of a high-precision monitoring system for various processing processes.