Data preprocessing-based bearing fault diagnosis in PHM: A systematic review

Preprocessing, a component of Prognostics Health Management, involves data collection, cleaning, etc., from sensor measurements to accurately assess bearing health. This step is critical for detecting incipient faults and performing effective diagnostics. By systematically preprocessing data from various sensors, engineers can extract meaningful features that reflect bearing degradation, enabling the generation of an accurate Health Index. Prognostics Health Management utilizes these indicators to predict the Remaining Useful Life of bearings, allowing for proactive maintenance and minimizing downtime. This paper outlines multiple methods for analyzing bearing faults. It is divided into two main sections. The first section addresses preprocessing methods, while the second section focuses on dataset collection, which helps researchers compare the results obtained using similar preprocessing methods. In summary, we create a preprocessing graph of the methods used.