Research on a Rotating Machinery Fault Prognosis Method Using Three-Dimensional Spatial Representations

Abstract

Process models and parameters are two critical steps for fault prognosis in the operation of rotating machinery. Due to the requirement for a short and rapid response, it is important to study robust sensor data representation schemes. However, the conventional holospectrum defined by one-dimensional or two-dimensional methods does not sufficiently present this information in both the frequency and time domains. To supply a complete holospectrum model, a new three-dimensional spatial representation method is proposed. This method integrates improved three-dimensional (3D) holospectra and 3D filtered orbits, leading to the integration of radial and axial vibration features in one bearing section. The results from simulation and experimental analysis on a complex compressor show that the proposed method can present the real operational status and clearly reveal early faults, thus demonstrating great potential for condition-based maintenance prediction in industrial machinery.