Abstract: Gear transmission systems are critical for power delivery in major equipment, yet they are susceptible to diverse and unpredictable to diverse and unpredictable faults, compromising operational safety and service reliability. In practical engineering applications, the absence of fault samples and unknown fault locations pose significant challenges. To address these issues, this paper proposes an unsupervised hypersphere-based fault localization (UHFL) method empowered by mode component energy features. This method extracts mode component energy features that are not only related to the fault mechanisms of transmission components but also possess clear fault localization interpretability. These features then enable both unsupervised anomaly detection and interpretable fault localization. Specifically, an unsupervised data description model is constructed using the proposed features. An attribution explanation method is introduced to quantify the contribution of each feature to the anomaly detection result, thereby achieving interpretable fault localization of gear transmission system components under conditions lacking fault samples. The proposed method is validated through single-fault and compound-fault localization experiments conducted on a helicopter main reducer planetary stage test bench and an armored vehicle transmission system test bench. Experimental results demonstrate that the proposed UHFL method can accurately localize faults in transmission components without requiring any fault sample training. This method offers an effective solution for fault localization in gear transmission systems under data-scarce conditions, showcasing valuable engineering promotion potential and application prospects.