PurposeIn gearbox fault diagnosis, identifying the fault type and severity simultaneously, as well as the compound fault containing multiple faults, is necessary.Design/methodology/approachTo diagnose multiple faults simultaneously, this paper proposes a multichannel and multi-task convolutional neural network (MCMT-CNN) model.FindingsExperiments were conducted on a bearing dataset containing different fault types and severities and a gearbox compound fault dataset. The experimental results show that MCMT-CNN can effectively extract features of different tasks from vibration signals, with a diagnosis accuracy of more than 97%.Originality/valueVibration signals at different positions and in different directions are taken as the MC inputs to ensure the integrity of the fault features. Fault labels are established to retain and distinguish the unique features of different tasks. In MCMT-CNN, multiple task branches can connect and share all neurons in the hidden layer, thus enabling multiple tasks to share information.

A mathematical model of the creeping phenomenon based on the mechanical model of the linear feed system was established. The dynamic characteristic parameters of each fixed joint were obtained by Yoshimura’s integral. Using the method, only the dynamic characteristic parameters of the joint surface per unit area with simple structure need to be studied, and then, the dynamic characteristic parameters of the whole joint surface can be obtained by integration. Based on the principle of the half-power bandwidth method and the frequency response function identification, the dynamic parameters of each moving joint were solved by the method of experimental modal analysis. Through the parameters of the fixed and moving joints, a rigid body model of the feed system and a flexible body model including the power transmission parts (ball screw pair) and the motion guide parts (guide slide pair and rolling bearing) were, respectively, established. And then, a rigid-flexible coupling dynamic model of the feed system was obtained through the constraint relationships between joints. The influence of both the external load and the feed rate on the fluctuation of motion speed of the system was analyzed from this model. The difference between the experimental results and the simulation results on a feed system platform is not greater than 10%, which verifies the creeping phenomenon. This conclusion can provide a basis for the optimization of the dynamic performance of the ball screw linear-feeding workbench.

No abstract

No abstract