In equipment maintenance engineering practice, monitoring data often suffer from high missing rates and incomplete operating-condition coverage, which lead to loss of critical degradation phase information and, due to the narrow range of training conditions, weaken the cross-condition generalization capability of RUL (remaining useful life) prediction, thereby compromising the reliability of performance degradation prognostics. To address these issues, DDI (degradation diffusion imputation) method for probabilistic reconstruction of bearing performance degradation data was proposed. A dual conditioning mechanism consisting of “observation mask constraint” and “observation replacement during sampling” was employed to ensure that the reverse denoising process strictly follows the known observations. Meanwhile, a frequency-domain consistency loss was introduced to enforce spectral amplitude similarity between the generated and real sequences, thereby effectively preserving the monotonic degradation trend and key frequency-band features. On the XJTU-SY bearing dataset, under missing rates from 10% to 90% for both random missing and block missing scenarios, the imputation accuracy measured by MAE(mean absolute error) and RMSE(root mean squared error) of DDI was generally superior to that of linear interpolation, KNN(K-nearest neighbors), BRITS(bidirectional recurrent imputation for time series), SAITS(self-attention-based imputation for time series), and the current SOTA(state-of-the-art), CSDI(conditional score-based diffusion imputation) method. Ablation studies further validate the effectiveness of the above dual conditioning mechanism. Moreover, after augmenting the training set with data generated by DDI, the RMSE of multiple downstream RUL prediction models e.g., RF(random forest), GB(gradient boosting), MLP(multilayer perceptron) was reduced by about 20% on average. In summary, this results provide a data-layer solution with quantifiable uncertainty for incomplete monitoring data imputation.