The detection of anomalous behaviour is fundamental to component health analysis techniques. However, detecting anomalies is a difficult and time consuming task if their form, location, and frequency are unknown. This research introduces an innovative unsupervised predictive maintenance pipeline that requires minimal domain knowledge and time to create competitive and insightful health monitoring models. First, a Dilated Convolutional Autoencoder learns to recreate healthy sensor data. Then, a Kullback-Leibler (KL) divergence based health analysis transforms discrepancies between the reconstruction and the sensor data into a single performance metric per sensor per flight. A novel evaluation method based on the KL divergence metric allows for quantitative evaluation and hyperparameter tuning of the autoencoder. Results provide new insights and show competitive performance on analysing the fuel level measuring system. Additionally, in a generalisability study on the braking system of a different aircraft type the proposed method outperforms the currently employed health monitoring model in precision and F1 score. The main advantages of the proposed method are; the ability to rapidly create unbiased health indicators on a sensor level, the capability to generalise to other components, and a framework to quantitatively evaluate the model’s performance when no truth labels are available.