A Novel MSFED Feature for the Intelligent Fault Diagnosis of Rotating Machines

Abstract

The vibration energy distribution pattern usually changes with the rotating machine’s health state and is a good indicator for intelligent fault diagnosis (IFD). The existing initial features such as RMS are less effective in revealing the vibration energy distribution pattern, and the frequency spectrum cannot provide a rich and hierarchical description of the vibration energy distribution pattern. Addressing this issue, we proposed a multi-scale frequency energy distribution (MSFED) feature for the IFD of rotating machines. The MSFED feature can reveal the vibration energy distribution patterns in the frequency domain in a multi-scale manner, and its one-dimensional vector and two-dimensional map formats make it usable for most IFD models. Experimental validation on the gearbox and bearing datasets verified that the MSFED feature achieved the highest diagnostic accuracy among commonly used initial features, in typical fault diagnosis scenarios except for the variable-load scenario. Furthermore, the separability and transferability of the MSFED feature were evaluated by distance-based metrics, and the results were in agreement with the features’ diagnostic performance. This work provides an important reference for the IFD of rotating machines, not only proposing a novel MSFED feature but also opening a new avenue for model-independent methods of the initial quality evaluation.