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Abstract

This paper addresses the problem of developing diagnostic systems for industrial equipment when initial failure data is incomplete or unavailable. An approach is proposed for generating synthetic data based on a pseudo-failure generator that implements stochastic modeling of degradation processes. A formalized algorithm for generating time-to-failure samples has been developed, including stages of failure physics analysis, statistical model selection, distribution parameterization, and simulation of operating conditions. The Weibull distribution is used as the base model, allowing for the consideration of various stages of the equipment’s life cycle. Additionally, factors that bring the synthetic data closer to real operating conditions are taken into account: measurement noise, data incompleteness, and the presence of outliers. The obtained samples were validated using the Kolmogorov–Smirnov test. A practical test was conducted using a rolling bearing as an example, for which realistic time-to-failure data were generated and the degradation process was simulated using a gamma process. The results confirm the feasibility of using synthetic data for the development, testing, and verification of diagnostic systems in the early stages of their creation.

First Page

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Last Page

56

References

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