Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data
Open accessAbstract
Often, manufacturing equipment is utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost. The emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In this paper, the AI-based algorithms for predictive maintenance are presented, and are applied to monitor two critical machine tool system elements: the cutting tool and the spindle motor. A data-driven modeling approach will be described, and it will be utilized to investigate the tool wear and the bearing failures.
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Predictive maintenance is a maintenance approach that uses AI and ML to detect potential issues before they occur.
“Predictive maintenance is a maintenance approach that uses AI and ML to detect potential issues before they occur.”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [8]
By analyzing data from sensors and other sources, predictive maintenance algorithms can predict when a robot's components may fail, allowing for proactive repairs or replacements.
“By analyzing data from sensors and other sources, predictive maintenance algorithms can predict when a robot's components may fail, allowing for proactive repairs or replacements [8] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [8]
Cites
Bearing fault detection by one-dimensional convolutional neural networks
Eren, L. (2017) · Mathematical Problems in Engineering · doi:10.1155/2017/8617315 · Cited by 250 · see citing works
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
- Authors
- Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M. B. G., & Sutherland, J. W.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Lee, Wo Jae
- Venue
- Procedia CIRP
- Publisher
- Elsevier BV
- Published
- 2019
- Volume
- 80
- Pages
- 506-511
- ISSN
- 2212-8271 (ISSN-L)
- OpenAlex
- W2944342107
- Cited by
- 263
- References
- 1
- Citations by year
- 2026: 31 · 2025: 50 · 2024: 64 · 2023: 44 · 2022: 23 · 2021: 28 · 2020: 18 · 2019: 3 · 2012: 2
- FWCI
- 17.89
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia CIRP (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Engineering Technology and Methodologies
- Keywords
- predictive maintenance, machine tools, tool wear, spindle motor, bearing failure, machine condition monitoring, data-driven modeling, Industry 4.0, unplanned downtime, equipment availability
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