This paper describes an application of an adaptive order-tracking technique for the diagnosis of faults in rotating machinery. Conventional methods of order-tracking are primarily based on Fourier analysis with reference to shaft speed. Unfortunately, in some applications of order-tracking performance is limited, such as when a smearing problem arises and also in a multiple independent shaft system. In this study, the proposed fault diagnostic system is based on a recursive least-square (RLS) filtering algorithm. The problem is treated as the tracking of various frequency bandpass signals. Order amplitudes can be calculated with high-resolution in real-time implementation. The algorithm is implemented on a digital signal processor (DSP) platform for diagnosis and evaluated by experimental investigation. An experimental investigation is implemented to evaluate the proposed system in two applications of gear-set defect diagnosis and in the diagnosis of damaged engine turbocharger blades. The results of the experiments indicate that the proposed algorithm is effective in fault diagnosis for both experimental cases. Furthermore, a characteristic analysis and experimental comparison of a vibration signal and a sound emission signal for the present algorithm are also presented in this report.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence