On-line automatic early fault detection of rotating machinery

Machinery suffers from deterioration no matter how high its reliability is. Maintenance is an appropriate measure to ensure machinery normal condition. So performance degradation assessment is very important for maintenance decision-making. There are two very interesting aspects when degradation assessment is performed. One is to detect early fault of machinery as early as possible. Another is to estimate remaining useful life (RUL) once early fault of machinery is detected. In this paper, wavelet lifting scheme (WLS) and hidden Markov model (HMM) are used to describe current condition of gearbox and detect early gearbox faults with a dynamic threshold. After that, another model based on final failure data is proposed to predict how much time is left before a failure occurs given the current machine condition. At last, the proposed method is validated by a set of whole life gearbox data.