Application of condition-based monitoring in enhancing mechanical system reliability and proactive structural damage detection

Marjan Djidrov

doi:10.31648/ts.10826

Application of condition-based monitoring in enhancing mechanical system reliability and proactive structural damage detection

Marjan Djidrov

Ss. Cyril and Methodius University in Skopje

DOI: https://doi.org/10.31648/ts.10826

Abstract

Technologies, processes, and systems are not immune to failure, which is why robust monitoring systems are crucial to ensure their continued functionality and safety. An interdisciplinary approach that combines engineering, data science, and material science allows for more comprehensive measurement and analysis, enabling better decision-making and more accurate predictions of performance. The integration of these technologies leads to increased safety, reduced human error, and significant cost savings by preventing costly repairs and downtime. Continuous monitoring helps in avoiding catastrophic failures, allowing for early detection of issues before they escalate. Additionally, it opens opportunities for improving the design of mechanical systems and structures, optimizing the organization of maintenance. By reducing human impact and enhancing safety, these monitoring systems offer a more secure and efficient operation. Furthermore, through advanced predictive analytics, the remaining service life can be estimated, facilitating more effective planning. The development of such smart, intelligent mechanical systems and structures promises a future where maintenance is proactive rather than reactive, creating a safer, more sustainable environment for both operators and systems by leveraging advanced sensors, data analytics, and adaptive technologies for real-time monitoring and damage detection.

Keywords:

Damage detection, Continuous monitoring, Smart structures, Maintenance, SHM, Intelligent systems, Internet of Things (IoT)

References

ABDELGAWAD A., YELAMARTHI K. 2017. Internet of things (IoT) platform for structure health monitoring. Wireless Communications and Mobile Computing, 1: 6560797.
Crossref Google Scholar

AONO K., LAJNEF N., FARIDAZAR F., CHAKRABARTTY S. 2016. Infrastructural health monitoring using self-powered internet-of-things. In 2016 IEEE international symposium on circuits and systems (ISCAS).
Crossref Google Scholar

ARAVANIS T.C., SAKELLARIOU J., FASSOIS S. 2021. On the functional model-based method for vibration-based robust damage detection: versions and experimental assessment. Structural Health Monitoring, 20(2): 456-474.
Crossref Google Scholar

BADIHI H., ZHANG Y., JIANG B., PILLAY P., RAKHEJA S. 2022. A comprehensive review on signal-based and model-based condition monitoring of wind turbines: Fault diagnosis and lifetime prognosis. Proceedings of the IEEE, 110(6): 754-806.
Crossref Google Scholar

BALAGEAS D., FRITZEN C.P., GÜEMES A. (Eds.). 2010. Structural health monitoring. Vol. 90. John Wiley & Sons, New York. Google Scholar

BEHESHTI AVAL S.B., AHMADIAN V., MALDAR M., DARVISHAN E. 2020. Damage detection of structures using signal processing and artificial neural networks. Advances in Structural Engineering, 23(5): 884-897.
Crossref Google Scholar

BINI CHIESA M., BODINI I., PETROGALLI C., PROVEZZA L., FACCOLI M., MAZZU A., SOLAZZI L., SANSONI G., LANCINI M. 2018. K-means clustering approach for damage evolution monitoring in RCF tests. Journal of Physics: Conference Series, 1065(10): 102018. 10.1088/1742-6596/1065/10/102018
Crossref Google Scholar

BLANKE M., KINNAERT M., LUNZE J., STAROSWIECKI M. 2006. Diagnosis and fault-tolerant control. 2nd Edition. Springer-Verlag, Berlin, Heidelberg, Google Scholar

CASOLI P., PASTORI M., SCOLARI F., RUNDO M. 2019. A vibration signal-based method for fault identification and classification in hydraulic axial piston pumps. Energies, 12(5): 953.
Crossref Google Scholar

CHEN J., PATTON R.J. 1999. Robust model-based fault diagnosis for dynamic systems. The International Series on Asian Studies in Computer and Information Science, Kluwer Academic Publishers.
Crossref Google Scholar

DE BELIE N., GRUYAERT E., AL‐TABBAA A., ANTONACI P., BAERA C., BAJARE D., JONKERS H.M. 2018. A review of self‐healing concrete for damage management of structures. Advanced Materials Interfaces, 5(17): 1800074.
Crossref Google Scholar

DI NUZZO F., BRUNELLI D., POLONELLI T., BENINI L. 2021. Structural health monitoring system with narrowband IoT and MEMS sensors. IEEE Sensors Journal, 21(14): 16371-16380.
Crossref Google Scholar

DJIDROV M., GAVRILOSKI V., JOVANOVA J. 2014. Vibration analysis of cantilever beam for damage detection. FME Transactions, 42(4): 311-316.
Crossref Google Scholar

DJIDROV M., GAVRILOSKI V., JOVANOVA J. 2017. Dynamic analysis of cantilever beam with bonded piezoelectric transducers by finite element method. Mechanical Engineering – Scientific Journal, 35(2): 121-127. Google Scholar

ETXANIZ J., ARANGUREN G., GIL-GARCÍA J.M., SÁNCHEZ J., VIVAS G., GONZÁLEZ J. 2023. Ultrasound-based structural health monitoring methodology employing active and passive techniques. Engineering Failure Analysis, 146: 107077.
Crossref Google Scholar

FANG L., ZHOU Y., JIANG Y., PEI Y., YI W. 2020. Vibration‐based damage detection of a steel‐concrete composite slab using non‐model‐based and model‐based methods. Advances in Civil Engineering, 1: 8889277.
Crossref Google Scholar

FENG C., LIU C., JIANG D., KONG D., ZHANG W. 2023. Multivariate Anomaly Detection and Early Warning Framework for Wind Turbine Condition Monitoring Using SCADA Data. Journal of Energy Engineering, 149(6): 04023040.
Crossref Google Scholar

FENG D., FENG M.Q. 2021. Computer vision for structural dynamics and health monitoring. John Wiley & Sons, New York.
Crossref Google Scholar

GOPALAKRISHNAN S., RUZZENE M., HANAGUD S. 2011. Computational techniques for structural health monitoring. Springer, London.
Crossref Google Scholar

GULGEC N.S., TAKÁČ M., PAKZAD S.N. 2019. Convolutional neural network approach for robust structural damage detection and localization. Journal of Computing in Civil Engineering, 33(3): 04019005.
Crossref Google Scholar

HO L.V., NGUYEN D.H., MOUSAVI M., DE ROECK G., BUI-TIEN T., GANDOMI A.H., WAHAB M.A. 2021. A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks. Computers & Structures, 252: 106568.
Crossref Google Scholar

ISERMANN R. 2005. Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer-Verlag, Berlin, Heidelberg,
Crossref Google Scholar

JAN-HWANG LOH K. 2011. Development of multifunctional carbon nanotube nanocomposite sensors for structural health monitoring. ProQuest, UMI Dissertation Publishing. Google Scholar

JAROSZEWICZ J., ŁUKASZEWICZ K. 2018. Analysis of natural frequency of flexural vibrations of a single-span beam with the consideration of Timoshenko effect. Technical Sciences, 21(3): 215-232. https://doi.org/10.31648/ts.2890
Crossref Google Scholar

JO B.W., KHAN R.M.A., LEE Y.S., JO J.H., SALEEM N. 2018. A fiber bragg grating‐based condition monitoring and early damage detection system for the structural safety of underground coal mines using the internet of things. Journal of Sensors, 1: 9301873.
Crossref Google Scholar

KESSLER S.S., SPEARING S.M., ATALLA M.J., CESNIK C.E., SOUTIS C. 2002. Damage detection in composite materials using frequency response methods. Composites Part B: Engineering, 33(1): 87-95.
Crossref Google Scholar

KOTHAMASU R., HUANG S.H., VERDUIN W.H. 2006. System health monitoring and prognostics – A review of current paradigms and practices. The International Journal of Advanced Manufacturing Technology, 28(9-10): 1012-1024.
Crossref Google Scholar

MORADI POUR P., CHAN T., GALLAGE C. 2015. An improved modal strain energy method for structural damage detection, 2D simulation. Structural Engineering and Mechanics, 54(1): 105-119.
Crossref Google Scholar

RAMANAMURTHY E.V.V., CHANDRASEKARAN K. 2011. Vibration analysis on a composite beam to identify damage and damage severity using finite element method. IJEST – International Journal of Engineering Science and Technology, 3(7): 5865-5888. Google Scholar

RYCHLIK A., LIGIER K. 2017. Fatigue crack detection method using analysis of vibration signal. Technical Sciences, 20(1): 63-74. https://doi.org/10.31648/ts.2909
Crossref Google Scholar

RYTTER A. 1993. Vibration based inspection of civil engineering structures. Ph.D. Dissertation, Department of Building Technology and Structural Engineering, Aalborg University, Denmark. Google Scholar

SAEEDIFAR M., MANSVELDER J., MOHAMMADI R., ZAROUCHAS D. 2019. Using passive and active acoustic methods for impact damage assessment of composite structures. Composite Structures, 226: 111252.
Crossref Google Scholar

SAKARIS C.S., SAKELLARIOU J.S., FASSOIS S.D. 2017. Random-vibration-based damage detection and precise localization on a lab-scale aircraft stabilizer structure via the Generalized Functional Model Based Method. Structural Health Monitoring, 16(5): 594-610.
Crossref Google Scholar

SIMANI S., FANTUZZI C., PATTON R.J. 2002. Model-based fault diagnosis in dynamic systems using identification techniques (Advances in Industrial Control). Springer-Verlag, Berlin Heidelberg.
Crossref Google Scholar

SOFI A., REGITA J.J., RANE B., LAU H.H. 2022. Structural health monitoring using wireless smart sensor network. An overview. Mechanical Systems and Signal Processing, 163: 108113.
Crossref Google Scholar

STASZEWSKI W.J., MAHZAN S., TRAYNOR R. 2009. Health monitoring of aerospace composite structures – Active and passive approach. Composites Science And Technology, 69(11-12): 1678-1685.
Crossref Google Scholar

STEPINSKI T., UHL T., STASZEWSKI W. 2013. Advanced structural damage detection – From theory to engineering applications. John Wiley and Sons, New York.
Crossref Google Scholar

TAVARES A., DI LORENZO E., PEETERS B., COPPOTELLI G., SILVESTRE N. 2021. Damage detection in lightweight structures using artificial intelligence techniques. Experimental Techniques, 45(3): 389-410.
Crossref Google Scholar

TOKOGNON C.A., GAO B., TIAN G.Y., YAN Y. 2017. Structural health monitoring framework based on Internet of Things: A survey. IEEE Internet of Things Journal, 4(3): 619-635.
Crossref Google Scholar

TORRES‐ARREDONDO M.A., SIERRA‐PÉREZ J., TIBADUIZA D.A., MCGUGAN M., RODELLAR J., FRITZEN C.P. 2015. Signal‐based nonlinear modelling for damage assessment under variable temperature conditions by means of acousto‐ultrasonics. Structural Control and Health Monitoring, 22(8): 1103-1118.
Crossref Google Scholar

TOWSYFYAN H., BIGURI A., BOARDMAN R., BLUMENSATH T. 2020. Successes and challenges in non-destructive testing of aircraft composite structures. Chinese Journal of Aeronautics, 33(3): 771-791.
Crossref Google Scholar

VENKAT V., RENGASWAMY R., YIN K., KAVURI S.N. 2003. A review of process fault detection and diagnosis. Part I. Quantitative model-based methods. Computers & Chemical Engineering, 27(3): 293-311.
Crossref Google Scholar

WANG C.S., CHANG F.-K. 1999. “Built-in diagnostics for impact damage identification of composite structures”, Structural Health Monitoring 2000. Proceedings of the Second International Workshop on Structural Health Monitoring, Stanford, CA, September 8-10. Technomic Publishing, Lancaster–Basel. Google Scholar

ZAKIROV R., GIYASOVA F. 2022. Application of fiber-optic sensors for the aircraft structure monitoring. Safety in Aviation and Space Technologies: Select Proceedings of the 9th World Congress “Aviation in the XXI Century”. Springer International Publishing, Berlin.
Crossref Google Scholar

ZHANG W., ZHENG Q., ASHOUR A., HAN B. 2020. Self-healing cement concrete composites for resilient infrastructures: A review. Composites. Part B: Engineering, 189: 107892.
Crossref Google Scholar

ZHONG H., YANG M. 2016. Damage detection for plate-like structures using generalized curvature mode shape method. Journal of Civil Structural Health Monitoring, 6: 141-152.
Crossref Google Scholar

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Published

2024-12-03

Cited by

Djidrov, M. (2024). Application of condition-based monitoring in enhancing mechanical system reliability and proactive structural damage detection. Technical Sciences, 27(27), 377–393. https://doi.org/10.31648/ts.10826