Deep learning has revolutionized many fields in recent years by replacing expert-designed, handcrafted features with learned representations. Gear health monitoring is a field where expert-designed features are heavily used for predictive modeling. This paper investigates how unsupervised deep learning can be applied to gear health monitoring to make predictions on low frequency scales using high frequency data given small, sparsely labeled data sets. Deep convolutional autoencoders are trained and used to generate learned features. The learned features are compared with relevant handcrafted features via their performance in training machine learning models to predict discrete gear fatigue states. The learned features performed poorly against the handcrafted features, however models trained on both sets tended to outperform those exclusively trained on handcrafted features. The top performing model was a multi-layer perceptron trained on both feature sets that leveraged the ability of the condition indicator to represent healthy and failure states and the ability of the learned features to represent the intermediate worn state. This work shows that unsupervised deep learning techniques can be used to bolster the performance of handcrafted features in small, sparsely labeled data sets in gear health monitoring.