Squirrel cage induction motor fault diagnosis using lissajous curve of an auxiliary winding voltage: case of broken bars

The detection of incipient faults has attracted industrials and researchers specific attention in order to prevent the motor breakdown, improve its reability and increase its lifetime. This paper presents a squirrel cage induction machine broken bar and rings diagnosis approach. This technic uses a new monitored signal as an auxiliary winding voltage related to a small coil inserted between two stator phases. Monitoring behaviors of the Lissajous curve of this auxiliary winding voltage park components under different load levels is the main key of this study. For this purpose, the squirrel cage induction machine modeling and the explicit expressions developed for the inserted winding voltage and its Park components will be presented. Then, an induction machine with different broken cases: one broken bar, two broken bars, broken end ring and broken bars with end ring are investigated. The simulation results confirm the validity of the proposed approach.

Hybrid Diagnosis Method Applied to Switched Mechatronic Systems

This article proposes a new diagnosis approach extended to switched mechatronic systems. The best tools of modeling and supervision, notably bond graph and observer, are used to move towards a high reliable fault detection and isolation approach. Researchers have always divided the hybrid observer into two blocks: a location observer that identifies the current mode and a continuous observer that detects faults. Applying the same logic to a system with a higher number of parameters from different energy domains increases the number of calculations and leads to a combinatorial explosion. The innovative interest of the present paper is the optimization of the observer's number using only one block to detect and, at the same time, locate faults. As a second objective, this paper presents an extension of the method to include complex industrial devices, which are in most cases switched mechatronic systems.

A New Transferable Fault Diagnosis Approach of Rotating Machinery Based on Deep Autoencoder and Dominant Features Selection under Different Operating Conditions

In the actual industrial scenarios, most existing fault diagnosis approaches are faced with two challenges, insufficient labeled training data and distribution divergences between training and testing datasets. For the above issues, a new transferable fault diagnosis approach of rotating machinery based on deep autoencoder and dominant features selection is proposed in this article. First, maximal overlap discrete wavelet packet transform is applied for signals processing and mix-domains statistical feature extraction. Second, dominant features selection by importance score and differences between domains is proposed to select dominant features with high fault-discriminative ability and domain invariance. Then, selected dominant features are used for pretraining deep autoencoder (source model), which helps in enhancing the fault representative ability of deep features. The parameters of the source model are transferred to the target model, and normal state features from target domain are adopted for fine-tuning the target model. Finally, the target model is applied for fault patterns classification. Motor and bearing fault datasets are used for a series of experiments, and the results verify that the proposed methods have better cross-domain diagnosis performance than comparative models.