A Diagnostic System for Speed-Varying Motor Rotary Faults

Abstract

This study proposed an intelligent rotary fault diagnostic system for motors. A sensorless rotational speed detection method and an improved dynamic structural neural network are used. Moreover, to increase the convergence speed of training, a terminal attractor method and a hybrid discriminant analysis are also adopted. The proposed method can be employed to detect the rotary frequencies of motors with varying speeds and can enhance the discrimination of motor faults. To conduct the experiments, this study used wireless sensor nodes to transmit vibration data and employed MATLAB to write codes for functional modules, including the signal processing, sensorless rotational speed estimation, neural network, and stochastic process control chart. Additionally, Visual Basic software was used to create an integrated human-machine interface. The experimental results regarding the test of equipment faults indicated that the proposed novel diagnostic system can effectively estimate rotational speeds and provide superior ability of motor fault discrimination with fast training convergence.

Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

“…The time available for initiating a maintenance action before a catastrophic failure after confirming a defect will be very short if this traditional technique is used. In contrast, Tseng, et al [18] and Kamiel, et al [19] have shown that utilizing the statistical process control (or statistical control chart) techniques combined with the time-domain features have effectively improved the fault detection process, but could not diagnose it. Thus, as it was mentioned previously that the first stage is in charge of only detecting the fault, so the combination of time-domain signal analysis with SCC will be applied at this stage.…”