Fault feature extraction plays a significant role in bearing fault diagnosis, especially in incipient fault period. Recently, deep neural network has been favored by many researchers due to its excellent hierarchical feature extraction capabilities. The existing diagnosis methods based on deep neural network mostly take original condition monitoring data as input and further convert fault diagnosis into pattern recognition issues. Although it improves the level of intelligent diagnosis, it is still confronted with practical problems. Since deep neural network includes many non-linear mapping layers, the extracted fault-related features show a high degree of abstraction, which reduces the practical level of this technology. Aimed at this problem, a mining method of understandably weak fault information is proposed based on deep neural network inversion estimation. From perspective of neuron response degree, this method mines the most sensitive input pattern that maximizes the neuron’s activation value of network output layer in the original input feature space. It can intuitively mine the weak fault information from the original signal. Two bearing experiments verify the effectiveness and reliability of the proposed method.

Aiming at the problem of gear crack fault diagnosis, an intelligent diagnosis method based on side frequency feature is proposed. It enhances the fault information representation ability of the extracted features and the fault identification ability of the model. Firstly, according to the side frequency distribution characteristics of gear crack fault, the side frequency energy features are quantified, and a relatively complete feature set is constructed by combining the time domain features; Secondly, an evaluation method of feature effectiveness is designed to obtain the optimal feature subset; Finally, a three-stage training network is constructed to achieve an increase in fault diagnosis rate. The test results under different working conditions show that the proposed method can more completely represent the fault information and effectively improve the fault diagnosis rate when compared with the machine learning model of a general two-layer network and feature extraction methods based on entropy features.

The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components.