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Adaptive and Self-supervised Condition Monitoring Method for Machinery

Data-driven condition monitoring is conducive to ensuring the efficiency and reliability of machinery. However, the accuracy of condition monitoring results can be impaired by volatile operating conditions and insufficient separability of measurements. To address the problems, an adaptive and self-supervised condition monitoring method is proposed. First, operational variable regression models are established to suppress the unexpected influence of operating conditions on the measurements collected from machinery. Then, valid data representations related to health status are extracted by self-supervised learning to guarantee that the data under distinct health conditions are separable during monitoring, and reference models are obtained to characterize the normal operations. Finally, the similarity between real-time measurements and reference models is analyzed, and control chart statistics are established to evaluate the health conditions in online monitoring. Take breakage detection in wind turbine blades as an example, the effectiveness and superiority with respect to benchmark methods are demonstrated using the data acquired from actual wind farms.