J.A. Ortega

Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition

Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research.

Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.

Fault Detection by Means of Hilbert–Huang Transform of the Stator Current in a PMSM With Demagnetization

This paper presents a study of the permanent magnet synchronous motor (PMSM) running under demagnetization. The simulation has been carried out by means of two dimensional (2-D) finite element analysis (FEA), and simulations results were compared with experimental results. The demagnetization fault is analyzed by means of decomposition of stator currents obtained at different speeds. The Hilbert Huang transform (HHT) is used as processing tool. This transformation represents time-dependent series in a two-dimensional (2-D) time-frequency domain by extracting instantaneous frequency components within the signal through an Empirical Mode Decomposition (EMD) process.

Short-Circuit Detection by Means of Empirical Mode Decomposition and Wigner–Ville Distribution for PMSM Running Under Dynamic Condition

This paper presents and analyzes a method for short-circuit fault detection in a permanent-magnet synchronous motor (PMSM). The study includes steady-state condition and speed transients in motor operation. The stator current is decomposed by empirical mode decomposition (EMD), which generates a set of intrinsic mode functions (IMFs). Quadratic time-frequency (TF) distributions such as smoothed pseudo-Wigner-Ville and Zhao-Atlas-Marks are applied on the more significant IMFs for fault detection. Simulations and experimental laboratory tests validate the algorithms and demonstrate that this kind of TF analysis can be applied to detect and identify short-circuit failures in PMSM.

Feature Extraction of Demagnetization Faults in Permanent-Magnet Synchronous Motors Based on Box-Counting Fractal Dimension

This paper presents a methodology for feature extraction of a new fault indicator focused on detecting demagnetization faults in a surface-mounted permanent-magnet synchronous motors operating under nonstationary conditions. Preprocessing of transient-current signals is performed by applying Choi-Williams distribution to highlight the salient features of this demagnetization fault. In this paper, fractal dimension calculation based on the computation of the box-counting method is performed to extract the optimal features for diagnosis purposes. It must be noted that the applied feature-extraction process is autotuned, so it does not depend on the severity of the fault and is applicable to a wide range of operating conditions of the motor. The performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for diagnosing demagnetization faults in industrial applications.

Motor Fault Detection Using a Rogowski Sensor Without an Integrator

This paper presents a new approach for the current acquisition system in motor fault detection applications. This paper includes the study, design, and implementation of a Rogowski-coil current sensor without the integrator circuit that is typically used. The circuit includes an autotuning block able to adjust to different motor speeds. Equalizing the amplitudes of the fundamental and fault harmonics leads to higher precision current measurements. The resulting compact sensor is used as a current probe for fault detection in induction motors through motor current signal analysis. The use of a Rogowski coil without an integrator allows a better discrimination of the fault harmonics around the third and fifth main harmonics. Finally, the adaptive conditioning circuit is tested over an induction machine drive. Results are presented, and quantitative comparisons are carried out.

Demagnetization fault detection by means of Hilbert Huang transform of the stator current decomposition in PMSM

This paper presents a study of the permanent magnet synchronous motor (PMSM) running under demagnetization. The simulation has been carried out by means of two dimensional (2-D) finite element analysis (FEA), and simulations results were compared with experimental results. The demagnetization fault is analyzed by means of decomposition of stator currents obtained at different speeds for torque change.

Study on the Permanent Magnet Demagnetization Fault in Permanent Magnet Synchronous Machines

This article studies the permanent magnet demagnetization fault in permanent magnet synchronous machines (PMSM). The study is carried out by analyzing the harmonics obtained using the fast Fourier transform (FFT) of stator and zero sequence currents at nominal torque for the whole range of machine operation speeds. The appropriate combination of analyses on the different currents makes it possible to determine the faults for any speed range

On the short-circuiting Fault Detection in a PMSM by means of Stator Current Transformations

This paper examines winding faults as short circuit for permanent magnet synchronous motor (PMSM). The study is carried out by means of stator current harmonic analysis. dq0 current transformation is also used to diagnose the state of the machine, and Wavelet approach is proposed to extend the analysis to the transitory of the failure. Experimental results for the full range of speeds have been obtained, which demonstrate the claims of the paper.

Broken Bearings Fault Detection for a Permanent Magnet Synchronous Motor under non-constant working conditions by means of a Joint Time Frequency Analysis

This work is an approach to develop new and reliable tools in order to diagnose mechanical faults in PMSM motors under non constant working conditions. These kinds of faults (especially damaged bearings) are more present in the industry. The paper presents a theoretical overview of the different techniques for a joint time frequency analysis and an experimental study of detection and diagnosis of damaged bearings on a permanent magnet synchronous motor (PMSM). The experiments were performed with variable rotor speed in such a way that the conventional methods such as MCSA do not work well. The stator current is analysed by means of STFT and Gabor spectrogram. Both results are presented and discussed.