Fault Detection for PMSM Motor Drive Systems by Monitoring Inverter Input Currents

This paper has proposed a fault detecting method for DC supplied permanent magnet synchronize motor(PMSM)drive systems by monitoring the drive DC input current.This method is based on the fault signal propagation from the torque disturbance on the motor shaft to the inverter input currents.The accuracy of this fault signal propagation is verified by the Matlab simulation and experiment tests with the emulated faulty conditions.The feasible of this approach is shown by the experimental test conducted by the Spectra test rig with the real gearbox fault.This detection scheme is also suitable for monitoring other drive components such as the power converter or the motor itself using only one set of current transducers mounted at the DC input side.

Intelligent Diagnosis of Short Hydraulic Signal Based on Improved EEMD and SVM with Few Low-dimensional Training Samples

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD（empirical mode decomposition） and EEMD（ensemble empirical mode decomposition） are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM（support vector machine）, extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD＋SVM method is compared with the EEMD＋SVM and EMD＋SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

Open-circuit Fault Diagnosis of Traction Inverter Based on Compressed Sensing Theory

This study proposes a new method of fault diagnosis based on the least squares support vector machine with gradient information(G-LS-SVM)to solve the insulated-gate bipolar transistor(IGBT)open-circuit failure problem of the traction inverter in a catenary power supply system.First,a simulation model based on traction inverter topology is built,and various voltage fault signal waveforms are simulated based on the IGBT inverter open-circuit fault classification.Second,compressive sensing theory is used to sparsely represent the voltage fault signal and make it a fault signal.The new method has a high degree of sparseness and builds an overcomplete dictionary model containing the feature vectors of voltage fault signals based on a double sparse dictionary model to match the sparse signal characteristics.Finally,the space vector transform is used to represent the three-phase voltage scalar in the traction inverter as a composite quantity to reduce the redundancy of the fault signals and data-processing capabilities.A G-LS-SVM fault diagnosis model is then built to diagnose and identify the voltage fault signal feature vector in an overcomplete dictionary.The simulation results show that the accuracy of this method for various types of IGBT tube fault diagnosis is over 98.92%.Moreover,the G-LS-SVM model is robust and not affected by Gaussian white noise.

Acoustic fault signal extraction via the line-defect phononic crystals

Rotating machine fault signal extraction becomes increasingly important in practical engineering applications.However,fault signals with low signal-to-noise ratios(SNRs)are difficult to extract,especially at the early stage of fault diagnosis.In this paper,2D line-defect phononic crystals(PCs)consisting of periodic acrylic tubes with slit are proposed for weak signal detection.The defect band,namely,the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity.The noise can be filtered by the band gap.As a result,fault signals with high SNRs can be obtained for fault feature extraction.The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies.All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals.This work will provide potential for extracting weak signals in many practical engineering applications.