• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.8 no.3
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• pp.285-294
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• 2003

To select an appropriate sampler for the environmental monitoring survey in coastal waters of Saemangeum, Jeollabuk-do, a macrobenthic sampling was conducted in April 2002. Employed samplers were dredge (type Charcot), a semi-quantitative sampler and Smith-McIntyre (SM) and van Veen grab (VV) as quantitative ones. One haul was tried for dredge and 3 replicates (0.1 ㎡${\times}$3) for SM and W at each of 11 stations. Comparisons of sediment volume in sampler bucket and of precision of biological parameters (i.e., density, biomass, species number and diversity index, H') were made between SM and VV. Sediment volume was significantly different (SM > VV) at p-value of 0.0050 (paired t-test) and, in average, 3 replicate samples of SM and VV satisfied a precision level of 0.2 by applying 4th root transformation. Patterns of observed and expected species numbers and H' were compared. Dredge-VV samples showed higher affinity than any other pair. Several dominant species in the area were underestimated in dredge samples (e.g., polychaete Heteromastus filiformis. Aricidea assimilis etc.). Quantifying the agreement pattern of multi-species responses was accomplished by estimating correlations between similarity matrices. Correlation between dredge and VV was slightly higher, but near-per-fect matches were found in general. Different ranks and composition among principal species lists were presumably linked to the effect of penetration depth that differs among samplers. Lower level of some species' abundance in VV samples (ca. 50％ compared with those of SM) was explained in this context. It seem appropriate to regard the effect as a probable cause of relatively higher correlations in dredge-VV, Overall bio-logica1 features indicated that a better choice could be SM in situations of requiring high data quality. The others work well, however, on observing and defining faunal characteristics and their capability cannot be questionted if we do not expect a first-order quality.