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State-of-the-Art Predictive Maintenance Techniques*
Abstract
This paper discusses the limitations of time-based equipment maintenance methods and the advantages of predictive or online maintenance techniques in identifying the onset of equipment failure. The three major predictive maintenance techniques, defined in terms of their source of data, are described as follows: 1) the existing sensor-based technique; 2) the test-sensor-based technique (including wireless sensors); and 3) the test-signal-based technique (including the loop current step response method, the time-domain reflectrometry test, and the inductance-capacitance-resistance test). Examples of detecting blockages in pressure sensing lines using existing sensor-based techniques and of verifying calibration using existing-sensor direct current output are given. Three Department of Energy (DOE)-sponsored projects, whose aim is to develop online and wireless hardware and software systems for performing predictive maintenance on critical equipment in nuclear power plants, DOE research reactors, and general industrial applications, are described.
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Cited by
A Manufacturing Big Data Solution for Active Preventive Maintenance
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2670505 · Cited by 441
Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study
Canizo, M., Triguero, I., Conde, A., & Onieva, E. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.034 · Cited by 420
Machine Learning approach for Predictive Maintenance in Industry 4.0
Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., & Loncarski, J. (2018) · 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) · doi:10.1109/mesa.2018.8449150 · Cited by 296
End-to-End Industrial IoT Platform for Actionable Predictive Maintenance
Christou, I. T., Kefalakis, N., Zalonis, A., Soldatos, J., & Bröchler, R. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.11.028 · Cited by 41
Requirements for an Intelligent Maintenance System for Industry 4.0
Garcia, E., Costa, A., Palanca, J., Giret, A., Julian, V., & Botti, V. (2020) · Studies in Computational Intelligence · doi:10.1007/978-3-030-27477-1_26 · Cited by 10
Metadata
- Authors
- Hashemian, H. M., & Bean, W. C.
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2011-10
- Volume
- 60
- Issue
- 10
- Pages
- 3480-3492
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2168949990
- Cited by
- 348
- References
- 0
- FWCI
- 21.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hashemian, H. M., & Bean, W. C. (2011). State-of-the-Art Predictive Maintenance Techniques*. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2009.2036347
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
