HealthMon: An approach for monitoring machines degradation using time-series decomposition, clustering, and metaheuristics

HealthMon: An approach for monitoring machines degradation using time-series decomposition, clustering, and metaheuristics. de Lima, M. J., Paredes Crovato, C. D., Goytia Mejia, R. I., da Rosa Righi, R., de Oliveira Ramos, G., André da Costa, C., & Pesenti, G. Computers & Industrial Engineering, 162:107709, December, 2021.

Paper doi abstract bibtex

Monitoring the degradation of machines to anticipate potential failures represents a significant challenge. In Industry 4.0, this task is critical when the costs associated with maintenance and stoppages on the productive processes are high. Nowadays, many preventive maintenance techniques employ supervised or unsupervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms combined with time-series decomposition can enhance the estimate of a machine’s health. This article proposes HealthMon as a novel approach to compute a health index of machines based on sensor measurements. HealthMon extracts time-series from such sensors, which are decomposed in an unsupervised way to present the health state along time. The health index is related to the degradation of the considered machine, thus optimizing the machine maintenance schedule. This work advances the state-of-the-art in the following ways: (i) it proposes a novel index of machines health, which yields a more direct and intuitive view of machine degradation; (ii) it devises the first approach capable of estimating the health index of a machine in a completely unsupervised way; (iii) it generalizes vibrating and rotating machines, thus being able to monitor a wide range of industrial equipment. We evaluated our method using both simulated and real data. The results show that the evolution of vibrating machines’ failures can be effectively detected under various input workloads. Finally, through HealthMon, industry decision-makers benefit from the guidelines for preventive actions at appropriate times, thus meeting Industry 4.0.

@article{de_lima_healthmon_2021,
	title = {{HealthMon}: {An} approach for monitoring machines degradation using time-series decomposition, clustering, and metaheuristics},
	volume = {162},
	issn = {0360-8352},
	shorttitle = {{HealthMon}},
	url = {https://www.sciencedirect.com/science/article/pii/S0360835221006136},
	doi = {10.1016/j.cie.2021.107709},
	abstract = {Monitoring the degradation of machines to anticipate potential failures represents a significant challenge. In Industry 4.0, this task is critical when the costs associated with maintenance and stoppages on the productive processes are high. Nowadays, many preventive maintenance techniques employ supervised or unsupervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms combined with time-series decomposition can enhance the estimate of a machine’s health. This article proposes HealthMon as a novel approach to compute a health index of machines based on sensor measurements. HealthMon extracts time-series from such sensors, which are decomposed in an unsupervised way to present the health state along time. The health index is related to the degradation of the considered machine, thus optimizing the machine maintenance schedule. This work advances the state-of-the-art in the following ways: (i) it proposes a novel index of machines health, which yields a more direct and intuitive view of machine degradation; (ii) it devises the first approach capable of estimating the health index of a machine in a completely unsupervised way; (iii) it generalizes vibrating and rotating machines, thus being able to monitor a wide range of industrial equipment. We evaluated our method using both simulated and real data. The results show that the evolution of vibrating machines’ failures can be effectively detected under various input workloads. Finally, through HealthMon, industry decision-makers benefit from the guidelines for preventive actions at appropriate times, thus meeting Industry 4.0.},
	language = {en},
	urldate = {2021-10-04},
	journal = {Computers \& Industrial Engineering},
	author = {de Lima, Miromar Jose and Paredes Crovato, Cesar David and Goytia Mejia, Rodrigo Ivan and da Rosa Righi, Rodrigo and de Oliveira Ramos, Gabriel and André da Costa, Cristiano and Pesenti, Giovani},
	month = dec,
	year = {2021},
	keywords = {Health index, Machine learning, Monitoring, Prediction, Preventive maintenance, Time-series, Unsupervised learning, sigkdd-rw},
	pages = {107709},
}

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