The rotor startup vibration signals carry abundant dynamic information of the machinery and are very useful for feature extraction and potential early fault diagnosis. Due to the non-stationary and transient nature of the signals in speed up process, the traditional diagnostic methods that have been put forward based on stationary assumption are no longer satisfactory. This paper proposes a new Speed Transform based method for the fault diagnosis of rotating machinery in variable speed. Speed Transform decomposes a complicated signal over a basis of elementary oscillatory functions, whose frequencies follow the speed variation. The effectiveness of the proposed method is demonstrated by both simulated signal and startup vibration signal collected from a rotor system with early rub-impact fault. Analyzed results showed that the proposed method could effectively extract fault features of the rotor under varying speed condition.

The vibration signals of rotating machinery are frequently disturbed by background noise and external disturbances because of the equipment’s particular working environment. Thus, robustness has become one of the most important problems in identifying the unbalance of rotor systems. Based on the observation that external disturbance of the unbalance response often displays sparsity compared with measured vibration data, we present a new robust method for identifying the unbalance of rotor systems based on model residual sparsity control. The residual model is composed of two parts: one part takes regular measurements of noise, while the other part evaluates the impact of external disturbances. With the help of the sparsity of external disturbances, the unbalance identification is converted into a convex optimization problem and solved by a sparse signal reconstruction algorithm. Experiment results have shown that the proposed method is robust and effective in identifying the unbalance of rotor systems in a complex environment, improving the precision of unbalance estimation and simplifying the balancing process.