###### Abstract  
Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a fault-diagnosis method without sensor installation, which is easier to realize in the mine. Therefore, a fault diagnosis method for local gear fault, which is based on bispectral analysis (BA) of analytical signal envelope obtained by processing a stator current under time-varying load condition, is proposed in our paper. In this method, the fault frequency component is enhanced by eliminating the interference of fundamental frequency and coal flow impact. Then, the enhanced fault frequency component is extracted by BA, and a quantitative analysis of the fault strength under time-varying load is carried out from the perspective of energy. Finally, the proposed method is verified on the number HB-kpl-75 scraper conveyor reducer, and the results show that this method can successfully diagnose the failure of the scraper conveyor gear under time-varying load conditions. scraper conveyor; gear fault diagnosis; motor current signature analysis; bispectral analysis +
Footnote †: journal: Applied Physics Letters  
the main reasons that cause the scraper conveyor to stop. Therefore, it is necessary to diagnose the gear fault under time-varying load conditions. The traditional fault diagnosis method mainly uses the vibration signal of mechanical devices [3; 4; 5; 6]. This diagnosis method is sensitive to the change of the sensors' installation position and the external environment. Besides, the sensors are voluminous and expensive [7; 8; 9]. However, the fully mechanized mining face is narrow, harsh and dusty, so it is hard to install and work the vibration signal sensor. Thus, it is not easy to accomplish the fault of the scraper conveyor gear by vibration signal. Nevertheless, motor current signature analysis (MCSA) is a non-intrusive and sensor-free fault diagnosis method [10]. Compared with traditional vibration signal analysis, it has the advantages of easy signal acquisition, small space occupation and no environmental interference [11; 12]. Therefore, MCSA is more suitable for the fault diagnosis of the scraper conveyor reducer gears in the fully mechanized coal face. Figure 1 is the fully mechanized coal face in the mine. Initially, the MCSA was used to diagnose the fault of the motor body [13; 14], and it was gradually applied to the fault diagnosis of rotating machinery [10; 15; 16]. Literature [17; 18; 19; 20] shows that when gear failure occurs, the failure position can cause the periodic impact of load torque. These shocks caused by gear failure will modulate the stator current of the motor [21], which makes a series of frequency sidebands around the fundamental frequency appear in the current signal spectrum [22]. However, the scraper conveyor adopts the variable frequency conversion input current, so there is a strong basic frequency interference component in its working current. Meanwhile, when the scraper conveyor is working, the amount of coal falling may sudden increase or the scraper chain may get stuck. The impact characteristics and basic frequency interference caused by these conditions will weaken the fault characteristics in the current, which makes it difficult to extract gear fault information from the original current. Therefore, it is necessary to preprocess the original current, so as to enhance the fault frequency component. The current signal preprocessing includes two parts: extracting the fault frequency band and eliminating the fundamental frequency interference. Aiming at the interference of coal flow impact and scraper chain impact in the current signal, the original current signal is decomposed into fault frequency band signal and impact frequency band signal by wavelet packet multi-scale decomposition (WPMD) in our method.