The Industrial Internet-of-Things (IIoT) has disrupted the way of collecting physical data for predictive maintenance purposes. At present, networks of intelligent wireless sensors are pervasive, finding success in many environments and industries, including the railways. However, when it comes to data-intensive applications like vibration monitoring that require the delivery of large amounts of records, the limitations of these devices arise. The shortfalls are mainly driven by the low-bandwidth transmission capacity of their radio interfaces, and the low-power features of their battery-operated (and/or energy-harvested) electronics. In sight of these limited resources, this article explores a vibration data compression strategy for diagnosis purposes. To maximise the amount of transferred information with the least amount of bytes this method works in three stages: first, it extracts the most useful features for vibration-based analytics. Then, it compresses the raw signal waveform using an Autoencoder neural network with an undercomplete representation, assessing its optimum regularisation approach: the denoising, sparse, and contractive configurations. Finally, it reduces the resolution of the compressed data by quantising all the resulting real values into single-byte unsigned integers. The proposed strategy is evaluated with a dataset of railway axle bearings with different levels of degradation. The results of the analysis show that with compression rates up to 10 the vibration signals are practically unaffected by this procedure, and once the signals are reconstructed with a minimum quality standard, many diagnosis goals like anomaly detection, fault location, and severity appraisal can be performed. This approach yields a wide range of business opportunities for on-board predictive maintenance with IIoT technology.