A reduced-order machine-learning-based method for fault recognition in tool condition monitoring

Isavand, Javad; Kasaei, Afshar; Peplow, Andrew; Wang, Xiaofeng; Yan, Jihong

doi:10.1016/j.measurement.2023.113906

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…In the work [15], a novel machine-learning-based approach for identifying failure symptoms of cutting tools in both frequency and time-frequency domains is presented. The study utilizes five cutting tools as case studies in a 200 min machining operation.…”

Section: Introductionmentioning

confidence: 99%

Integrating Sensor Systems and Signal Processing for Sustainable Production: Analysis of Cutting Tool Condition

Kozłowski,

Antosz,

Sęp

et al. 2023

Electronics

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This research focuses on the crucial role of monitoring tool conditions in milling to improve workpiece quality, increase production efficiency, and reduce costs and environmental impact. The goal was to develop predictive models for detecting tool condition changes. Data from a sensor-equipped research setup were used for signal analysis during different machining stages. The study applied logistic regression and a gradient boosting classifier for material layer identification, with the latter achieving an impressive 97.46% accuracy. Additionally, the effectiveness of the classifiers was further confirmed through the analysis of ROC (Receiver Operating Characteristic) curves and AUC (Area Under the Curve) values, demonstrating their high quality and precise identification capabilities. These findings support the classifiers’ utility in predicting the condition of cutting tools, potentially reducing raw material consumption and environmental impact, thus promoting sustainable production practices.

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Section: Introductionmentioning

confidence: 99%

Integrating Sensor Systems and Signal Processing for Sustainable Production: Analysis of Cutting Tool Condition

Kozłowski,

Antosz,

Sęp

et al. 2023

Electronics

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“…Early Fault Detection: It allows for the early detection of any potential defects within equipment or structures, which allows for preventive maintenance before a breakdown occurs [69]. This helps to avoid costly repairs and downtime.…”

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confidence: 99%

“…Improved Safety: Vibration monitoring allows for the detection of machine faults, which increases the safety of employees by avoiding accidents carried by machinery that fails [69]. 4.…”

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confidence: 99%

An In-Depth Study of Vibration Sensors for Condition Monitoring

Hassan,

Panduru,

Walsh

2024

Sensors

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Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration.

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