Bearing fault diagnosis plays an essential role in the maintenance of rotating machines in industries. Challenges in developing an effective and robust bearing fault diagnosis method include the complexity of vibration data and the external interference caused by the data collection. This study develops an intelligent data-driven method for bearing fault diagnosis in noisy environments, consisting of the feature transformation of vibration data and fault recognition based on transformed features. First, an extension of the Ramanujan filter banks (RFBs) method, scaled-RFB, is introduced to suppress the noises and convert original sequential vibration data into representative RGB images. Next, a strip convolutional neural network (strip-CNN) is developed with strip convolution to recognize the health condition of bearings based on the obtained RGB images. Two vibration datasets collected from Soochow University and a public data source are utilized to validate the effectiveness and robustness of the proposed method individually. Six levels of Gaussian noises are separately added into the two datasets to further demonstrate the performance of the proposed method in noisy environments. Compared with six benchmarking methods, the proposed method can achieve the best performance on bearing fault diagnosis in most scenarios and shows promising performance on datasets with a higher noise level. The average Precision, Recall, and ${F}1$ scores of the proposed method on both datasets are at least 51.79%, 52.49%, and 52.47% higher than that of benchmarking methods respectively when the signal-noise ratio (SNR) is −10 dB.