Application of Cyclic Spectral Analysis in Diagnosis of Bearing Faults in Complex Machinery

Abstract

Bearing failure can lead to major damage to rotating components and its diagnosis and prognosis are therefore of paramount importance. Techniques and approaches for detecting bearing faults abound. However, application of these methods is limited for complex systems such as aircraft engines. This stems from the fact that the complex configuration of the system and inaccessibility make it difficult to place the vibration transducers close to the bearings. In most cases, available instrumentation is limited to few vibration transducers on the casing of the machine. In such cases, the vibration due to bearing faults is barely detectable using traditional methods, because it normally makes only a small contribution to the overall energy and this is to some extent dissipated by the transmission path. For bearing fault detection to be effective in such applications, the methodology must be capable of detecting faint bearing signals and also allow consistent trending and tracking. This study examines these requirements in detail and presents an experimental assessment of newly emerging cyclic spectral analysis in this field for such requirements.

KEY WORDS:

• Cyclic Spectral Analysis
• Cyclostationary
• Bearing Fault Detection
• Complex Machinery
• Condition Monitoring

FUNDING

The authors are greatly in debt to Department 9436 of Pratt & Whitney Canada for their generous support and their valuable comments. This project has been made possible by support from the collaborative research and development grant (No. CRDPJ-335472-05) of the Natural Sciences and Engineering Research Council of Canada (NSERC), Pratt & Whitney Canada, BOMBARDIER Aeronautics, and National Research Council of Canada (NRC).