Monitoring of punch failure in micro-piercing process based on vibratory signal and logistic regression

Wu, Tsung-Liang; Sari, Delima Yanti; Lin, Bor-Tsuen; Chang, Chia‐Wei

doi:10.1007/s00170-017-0701-7

Cited by 16 publications

(4 citation statements)

References 27 publications

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“…Machines have been known to be monitored via the acquisition of certain sensor data: voltage and current [2], temperature and pressure [3], vibration [4,5,6,7,8,9,10] and sound [11,12,13,14,15,16,17]. Vibration and sound have been reported effective sensor signals to characterize a machine behavior.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

Talmoudi

Kanada²,

Hirata

2020

Preprint

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One of the main focuses of smart industry is machinery failure predictive solutions. To achieve this, IoT-based solutions have been widely deployed. However, data processing and decision making remain challenging. The absence of enough knowledge has been the primarily limitation of statistical methods and supervised learning methods. Therefore, unsupervised learning methods are gaining more popularity but still have limits to cover effectively the pre-signs of failures due to the complexity of training process and results visualization. Previously, we proposed a novel Big Data Analysis method on audio/vibration data to cover effectively the pre-signs of failures through data visualization without complex learning or processing. We validated our proposal on a demo system. In the present work, we are using part of the MIMII dataset to test our proposed analysis method on a real-world-like data and verify the validity of our proposal on a more complex system. We are showing that we can detect abnormal machine behaviors and predict failures without prior training or knowledge of the target monitored machine.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

Talmoudi

Kanada²,

Hirata

2020

Preprint

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“…Machine Learning is a commonly used methodology for process monitoring [38][39][40]. These approaches include failure detection [41], failure diagnosis [42,43], condition monitoring [44,45], failure prediction [46][47][48], remaining useful life [49][50][51] and degradation [52], among others. As explained above, this article focuses on tool condition monitoring.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

Domínguez-Monferrer

Fernández-Pérez²,

Santos³

et al. 2022

Journal of Manufacturing Systems

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“…As presented above, several studies on machine failures using various methods can be found in the literature. However, although there are many studies on machine failures using logistic regression, studies utilizing multinomial logistic regression are rather rare (Pramesti et al, 2016;Caesarendra et al, 2010;Kozlowski et al, 2019;Yan et al, 2004, Yang and Lee, 2005, Wu et al, 2017.…”

Section: Machine Failure Analysis and Literature Reviewmentioning

confidence: 99%

Machine Failure Analysis Using Multinomial Logistic Regression

Koçak¹

2022

isarder

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Purpose: The main purpose of this study is to carry out a failure analysis of a filling machine by applying multinomial logistic regression.Design/methodology/approach:For this purpose,data related to failure mode, product, scrap rate, and shift parameters were collected from the machine and analysis was conducted by establishing two multinomial logistic regression models.Findings: Statistical results suggest that a hydraulic failure must be expected while filling the mix product. Besides, it is highly probable that a final folder failure will occur while filling the cherry product. Paper failure stands out while filling the apple product compared to other products. In addition, it is likely that a final folder failure will occur while filling this product. Photocell failure is common while filling the peach product. Results of the study show that the odd for low-level scrap is high when there is any failure in the machine.Discussion: A more effective analysis can be performed by collecting parameters that may affect the position of machinery such as vibration, humidity, temperature and pressure through the sensors to be installedon various units of the filling machine and adding them into the models developed under the study.

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Monitoring of punch failure in micro-piercing process based on vibratory signal and logistic regression

Cited by 16 publications

References 27 publications

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

Machine Failure Analysis Using Multinomial Logistic Regression

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