Rack and pinion drives (RPDs) are key components of battery-swapping systems (BSSs) used in electric heavy trucks; the faults occurring in these drives reduce the efficiency, accuracy, quality of battery swapping, and even pose potential safety risks. The operating conditions of RPD driving gear in BSSs are characterized by speed fluctuations, relatively low speeds, and reciprocating motion. To assess the driving gear fault characteristics under these conditions, based on the solution of image recognition under complex and low illumination conditions, this study proposes a fault diagnosis framework that includes adaptive down-sampling, three-dimensional acceleration data fusion, multi-scale local binary pattern (MS-LBP) extraction, and sparse representation. First, adaptive down-sampling is used to smooth out the speed fluctuation. Subsequently, MS-LBP extraction is employed to obtain efficient fault features at low speed. Finally, dictionary learning and sparse representations are conducted on the MS-LBP features. The effectiveness and advantages of the proposed diagnosis approach are demonstrated using monitoring data acquired from a BSS. Moreover, comparative studies demonstrate that the proposed fault diagnosis method yields improved performance.