In this paper we present a novel statistical approach to the estimation of the time in which an operating gear will achieve the critical stage. The approach relies on measured vibration signals. From these signals features are extracted first and then their evolution over time is predicted. This is done owing to the dynamic model that relates hidden degradation phenomena with measured outputs. The Expectation-Maximization algorithm is used to estimate the parameters of the underlying state-space model on-line. Time to reach safety alarm threshold is determined by estimating the distribution of remaining useful life using the estimated linear model.
COBISS.SI-ID: 23786791
The paper presents a fault detection algorithm applicable for mechanical drives. Traditionally, fault detection process is done by comparing the observed machine state with a set of historical data represent-ing the fault–free state. However, such historical data are rarely available. We resolved this problem by applying highly sensitive signal processing methods. The obtained results confirmed that the proposed approach was quite effective in for detection of damaged bearings.
COBISS.SI-ID: 25505063