This paper presents a data-driven methodology for monitoring and quantifying the degradation of plastic chain conveyors, a relevant asset in industrial automation. The proposed approach leverages system vibrations and employs the synchrosqueezing Short-Time Fourier Transform and convolutional autoencoders to generate a compact representation space for the data. This space enables the construction of control charts to monitor the extracted metrics. The method is intended to provide a comprehensive assessment of system degradation. Applied to a plastic conveyor chain on a dataset collected over a four-month period, the methodology seeks to identify and quantify the two most significant degradation mechanisms: the chain's elongation due to joint wear and the wear and tear of the slide rail. This research intends to address a significant gap in the literature, offering a practical and automatic solution for condition monitoring based on vibration for industrial equipment. Two aspects will be considered for the evaluation of the method: the capability to "reduce" the storage usage (dimensionality reduction) and the anomaly detection capabilities of the system.