## 1 Introduction
However, direct analysis of the observed weak fault signals will inevitably underperform, as the raw signals contain a large amount of noise and strong contributions from rotating shafts and gears unrelated to bearing faults. Therefore, to improve the accuracy of the diagnostics, it is key to use an appropriate algorithm for fault characteristic signal enhancement so as to highlight the relevant information. For example, [PERSON] [14] proposed wavelet packet transform as a 2D visualization tool and applied CNN to recognize bearing conditions. Wavelet packet transform time-frequency feature fusion is also an effective method for enhancing fault characteristics [15]. The sub-components of the decomposed signal can be input to CNN for extracting more fault-sensitive information to improve the results of the degradation assessment. [PERSON] et al. [16] used Variational Mode Decomposition (VMD) as a preprocessing algorithm to decompose the original signal into Intrinsic Mode Functions (IMFs), and then input them into the CNN network to extract the characteristics of each IMF. The validity of the algorithm is verified by experiment data under different environments and operating states. [PERSON] et al. [17] proposed multi-sensor information fusion technology, which can provide more accurate information for weak fault classification. However, most of the condition monitoring data collected in the actual service environment are non-faulty state data, and the amount of vibration data in the faulty state is relatively small. Even if the vibration data in the fault state are collected, the unique structure of planetary bearings leads to complex peculiar vibration patterns which are not easily highlightable. The attenuation characteristic of the signal transmission path is the main factor leading to the weak fault characteristic of the planetary bearing measuring point signal. The operating environment of mechanical equipment is harsh, and the external interference is large. Early fault patterns are often flooded by external information unrelated to the fault. In addition, the coupled movement between the different planetary gearbox parts leads to complex non-stationary, modulated signals due to variable transmission paths and variable loads. Finally, a certain fault characteristic may be covered by other fault characteristics, resulting in multiple fault signal characteristics cross coverage, which is another critical characteristic of fault characteristic signals. Taking into account the above factors, it has been found that Cyclostationary analysis can be applied to the non-stationary fault signal analysis of planetary bearings. In fact, even if the time-domain fault-characteristic waveform is not cyclical, its statistics generally change with time and show period or multi-period changes [18]. Cyclostationary analysis tools such as the spectral correlation density [19; 20], cyclic modulation spectrum [21], FFT cumulative method [22], cycle energy indicators [23], and the rapid spectral estimation algorithm [24; 25] have been proven to be effective in the field of weak fault diagnostics. In order to improve the recognition accuracy of the CNN algorithm, in the preprocessing step, wavelet transform coefficients cyclic demodulation (WSC) is proposed to obtain a 2D spectrometography able to enhance the planetary gearbox bearing fault characteristics. In order to properly test the here proposed algorithm, a high-quality, labeled training dataset was used, containing acquisitions from a damaged condition. The novel proposed algorithm is described in Section 3, based on the notions about Cyclostationary (Section 2.1) and CNN (Section 2.2). WSC is adopted to eliminate the fault irrelevant information, isolate the fault source, and extract the fault-related feature quantity. Then, a 2D feature space data argumentation strategy based on image segmentation is proposed to compensate for the influence of the wavelet scale analysis range on the results.