The SRVM: A Similarity-Based Relevance Vector Machine for Remaining Useful Lifetime Prediction in the Industrial Internet of Things

With the continuous advancement of Industry 4.0 and intelligent manufacturing, remaining useful lifetime (RUL) prediction can forecast the future degradation state of machinery and then estimate the remaining service time before it loses its safe operation ability. Accordingly, a series of predictive maintenance strategies can be regulated in advance for equipment in the Industrial Internet of Things. To tackle the challenges of insufficiency of failure data and lack of confidence in RUL prediction results, a similarity-based relevance vector machine (SRVM) is proposed in this article. Primarily, the relationship among latent variables in the SRVM is learned adaptively through similarity computations to fully utilize the limited degradation data. Furthermore, these internal variables in the SRVM are treated as time-varying variables and re-estimated dynamically to provide RUL prediction with reliable confidence. The experiment results show that the prediction accuracy of the SRVM is higher than that of other baseline methods.

Automated summary

In this article, a similarity-based relevance vector machine (SRVM) is proposed to address the challenges of insufficient failure data and low confidence in remaining useful lifetime (RUL) prediction results. The relationship among latent variables is learned adaptively through similarity computations to fully utilize the limited degradation data, and the internal variables in SRVM are treated as time-varying variables and re-estimated dynamically to provide reliable confidence for RUL prediction. Experimental results demonstrate that SRVM achieves higher prediction accuracy compared to other baseline methods.