Gear Backlash Detection and Evaluation Based on Current Characteristic Extraction and Selection

Abstract

Motor Current Signal Analysis (MCSA) is widely used in the monitoring of electric motors, but few researchers applied it in the transmission system because of the fault information loss and aliasing of current signals. The purpose of the research is to analyze the influence of the gear backlash under different size effect on the current signal from the permanent magnet synchronous motor (PMSM). And it supplies a convenient route to detect the gear backlash more economy. In most cases, the current of PMSM is usually changed with the operating states, which may relate with rotor speed, load and gear backlash. So, it is important to extract the sensitive features under different states. Here, the stable speed and variable speed states are considered at first. At the steady stage, multiple features are extracted from current signals under variable backlash. And fisher discriminant analysis (FDA) is introduced to evaluate and select the most sensitivity features for backlash are selected. Then, features at variable speed states are extracted. Because the transient change of the current frequency and phase adjustable, it is very difficult to obtain the steady state response characteristic. In this paper, the inverse of the time difference between the positive peaks of the signal at this phase is utilized as the characteristic index describing phase. Furthermore, the polynomial principle is combined to enhance the characteristics of the extracted features. Therefore, the mapping relationship between the backlash and the current signal of the servo motor is established under different speed stages. The results show that by monitoring the motor current, it is feasible and effective to distinguish the different backlash of the meshing gear.