Bearing fault diagnosis in mechanical systems is an imperative task across various industries, including manufacturing, energy, and transportation. Although recent advances in deep learning have enabled automated approaches for fault diagnosis, these approaches often fail to incorporate vital domain-specific knowledge and operating conditions into the models. To address this limitation, we propose ZERONE, a novel image embedding method that simplifies the representation of time-domain features, frequency-domain features, and operating conditions of vibration signals by integrating them into a single image. In this representation, these features are expressed as colored numbers of either zero or one, while categorical variables are represented as text in their original form. Subsequently, these images are processed by a convolutional neural network model for fault diagnosis. Experimental results demonstrate the superior performance of our approach, achieving 98.24% accuracy on the Paderborn University bearing dataset and 99.64% accuracy on the Jiangnan University dataset, surpassing other methods. Moreover, it achieved an average performance improvement of 7.24% compared to existing image embedding methods. Furthermore, the proposed method employs gradient-weighted class activation mapping to identify key frequency and statistical variables, offering interpretability and setting a new standard for diagnosing failures in mechanical systems.