For the remaining useful life (RUL) prediction of bearings across varying operating conditions, transfer learning models have demonstrated high accuracy. To make use of the maximum amount of bearing degradation information, multi-source domain adaptation models have been developed to enable predictions across operating conditions and bearing types. These models typically make use of vibration data in the time, frequency, and time-frequency domains. However, in practice, some systems only collect data in the frequency domain, such that prediction models developed for these systems lack access to degradation features from the time or time-frequency domains. To address this, this study proposes a multi-source domain adaptation (MSDA) model for bearing RUL prediction across operating conditions and bearing types using only frequency domain data as input data. The model employs a common feature extractor to capture domain invariant features, and domain specific regressors for RUL prediction. Domain adaptation is done by minimising the maximum mean discrepancy between features from the source and target domains, while the domain specific RUL predictions are aligned by minimising the distance between the RUL predictions. Experimental results across three scenarios demonstrate that the MSDA model achieves accurate RUL predictions. Even with only frequency domain data, the RMSE and Score are comparable to those of other advanced algorithms that use both frequency and time domain data.