A Novel Curve Clustering Method in Modal Decomposition for Amplitude Estimation of Period-Decreasing Signals in Brake Rotors

In response to rotor imbalance during vehicle braking, which leads to vehicle vibrations, a method utilizing rotor speed and signal-processing technology has been proposed. This method processes lateral vibration signals collected by a single-axis sensor during the vehicle braking process. The approach involves modal decomposition of the vibration signals, followed by improved multiscale fuzzy entropy curve trend feature clustering. This helps determine the decomposition layers $K$ in the variational mode decomposition (VMD) and performs secondary denoising. Additionally, it combines an estimation of rotor speed to eliminate residual vibration noise. Compared to traditional offline balancing methods, this approach achieves online dynamic balancing detection of brake disks during vehicle braking. Offline validation using a vertical balancing machine indicates that the average amplitude error remains within 10% when the rotor speed is below 300 r/min, thus validating the effectiveness of this method.

I. Introduction

Uneven mass distribution in rotors is a common fault in rotating machinery [1]. This condition causes the rotor mass to deviate from the geometric center, resulting in the classical eccentric effect, namely, imbalance. Imbalance becomes a primary source of vibrations leading to malfunctions in rotating mechanical systems. In the field of mechanical manufacturing, achieving high performance and reliability of precision rotors requires excellent balance as an essential prerequisite [2]. Rotor balancing is widely applied in various rotating machinery, such as wind turbines, automotive engines, aerospace equipment, industrial compressors, and centrifugal pumps, among others. The quality of rotor balance directly impacts the performance, lifespan, and safety of these devices [3], [4], [5]. The motion of an imbalanced rotor can lead to noise, vibration, and potential system failures. Therefore, extracting imbalance information from raw vibration signals is of paramount importance for rotor dynamic balancing detection [6].