Fault identification of rolling bearings under linear varying speed based on the slope features of time–frequency ridges

•Two types of slope characteristic indicators are proposed for time frequency images under linear varying speed.•The superiority in intra-class state-aware stability and inter-class state-aware sensitivity is verified.•The influences of time-varying, noise and image cropping are considered in robustness verification process.•Fault recognition rate using one single indicator is discussed.•An feature fusion strategy by numerical addition of a slope feature and a Tamura feature is proposed to achieve high-precision intelligent diagnosis. Under the condition of linear varying speed regulation, the vibration signal of the rolling bearing is non-stationary, and its fault frequency is time-varying, which makes it difficult to extract the bearing fault characteristics. In order to better improve the fault identification accuracy of rolling bearing, two slope characteristic indicators of time–frequency ridges (TFRs) based on time–frequency images (TFIs) are proposed, which are called pseudo-slope and pseudo-angle. The intra-class state-aware stability and inter-class state-aware sensitivity of the characteristic indicators are verified by simulation and experiment. At the same time, the robustness of the slope features of TFRs and the Tamura features under the influence of time-varying, noise and image cropping are compared, and it is proved that the stability of the slope features of TFRs are better than that of the Tamura features. In the case of one single-index identification, the fault recognition rate of pseudo-slope and pseudo-angle features is higher than that of Tamura single-index features. On the basis of single-index identification, a new feature fusion by numerical summation of pseudo-slope feature and Tamura contrast feature is proposed, and the fault identification accuracy of the fused feature indicator is 98.61%, which realizes high-precision identification of rolling bearing faults.