Experimental investigation and comparative study of signal processing techniques dedicated for the diagnosis of rotor failures in the induction motors

Purpose The purpose of this paper is to propose applications of advanced signal-processing techniques for the diagnosis and detection of rotor fault in an induction machine. Two techniques are used: spectral analysis techniques and time frequency techniques for the diagnosis of an electrical machine. One is based on the power spectral density estimation techniques, such as periodogram and Welch periodogram. The second method is based on Hilbert transform (HT) to extract the envelope for the stator current. Then, this signal is processed via discrete wavelet transform (DWT) for determining the faulty components in the spectrum of the stator current envelope and identifying the eigenvalues of energies (HDWT). Design/methodology/approach First, this paper focused on theoretical development and a comparative study of these signal-processing techniques, which are based on the periodogram, Welch periodogram, HT and the DWT to extract the envelope for the stator current; it is used to compute the energy stored in each decomposition level obtained by the stator current envelope (HDWT). Moreover, the Welch periodogram is applied to obtain the envelope spectrum. Findings The simulation obtained and the experimental validation results of the proposed methods through MATLAB environment show the effectiveness of the proposed approaches with a good accuracy by power spectral density estimation techniques (periodogram and Welch periodogram). Moreover, the faults are manifested through the appearance of new frequencies components, as well as the envelope for the stator current (HT and DWT). This approach is effective for non-stationary and stationary signal to extract useful information for the detection of broken bar fault. Originality/value The current paper proposes a new diagnosis method for the detection and characterization of broken rotor bars defects early; it is founded primarily on theoretical development, and the comparison is based on the power spectral density technique (periodogram and Welch periodogram) and the computation of the energy stored in each decomposition level (precisely the HT and DWT). Moreover, the Welch periodogram is applied to obtain the envelope spectrum. The main advantages of the proposed techniques increase their reliability and availability.

Adaptive Scheme for Detecting Induction Motor Incipient Broken Bar Faults at Various Load and Inertia Conditions

This paper introduces a novel online adaptive protection scheme to detect and diagnose broken bar faults (BBFs) in induction motors during steady-state conditions based on an analytical approach. The proposed scheme can detect precisely adjacent and non-adjacent BBFs in their incipient phases under different inertia, variable loading conditions, and noisy environments. The main idea of the proposed scheme is monitoring the variation in the phase angle of the main sideband frequency components by applying Fast Fourier Transform to only one phase of the stator current. The scheme does not need any predetermined settings but only one of the stator current signals during the commissioning phase. The threshold value is calculated adaptively to discriminate between healthy and faulty cases. Besides, an index is proposed to designate the fault severity. The performance of this scheme is verified using two simulated motors with different designs by applying the finite element method in addition to a real experimental dataset. The results show that the proposed scheme can effectively detect half, one, two, or three broken bars in adjacent/non-adjacent versions and also estimate their severity under different operating conditions and in a noisy environment, with accuracy reaching 100% independently from motor parameters.

Research on stator current characteristics of centrifugal pumps under different mechanical seal failures

Mechanical seal failure has a great negative impact on the operation of a centrifugal pump system. A method to analyze the stator current characteristics of the motor in a centrifugal pump system is proposed to monitor the internal flow of the centrifugal pump and to identify the failure status of the mechanical seal. Experiments were conducted under different mechanical seal states. Based on sensorless technology, the stator current signal of the motor is collected, processed by windowing function, anti-aliasing filter, singular value decomposition, Hilbert–Huang transform, and the marginal spectrum of correlation quantity is drawn. The results show that according to the external characteristic curve of the centrifugal pump, after the failure of the mechanical seal, the head and efficiency of the centrifugal pump decrease, and the head is greatly affected by the degree of failure, while the degree of mechanical seal failure has little effect on the shaft power of the centrifugal pump; the centrifugal pump has good operation stability under design conditions or near slightly large flow; the stability of centrifugal pump operation decreases with the aggravation of mechanical seal failure; the corresponding maximum amplitude in the marginal spectrum can be used as an index to diagnose the damage degree of the mechanical seal.

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

Design and Analysis of a Dual Rotor Multiphase Brushless DC Motor for its Application in Electric Vehicles

The main objective of this paper is to study the effect of phase numbers in the dual rotor Brushless DC (BLDC) motor for its application in Electric Vehicles (EVs). The performance of two novel 5-, and 7-phase dual rotor BLDC motors is compared against the standard 3-phase dual rotor BLDC motor. The proposed motors combine the positive characteristics of multiphase BLDC motor and the dual rotor BLDC motor thus achieving better fault tolerance capability, high power density, and less per phase stator current. Finite Element Method (FEM) was used to design the 3-, 5-, and 7-phase dual-rotor BLDC motors. The design parameters and operating conditions are kept the same for a fair comparison. The stator current and torque performance of the proposed motors were obtained with FEM simulation and were compared with the standard 3-phase dual rotor BLDC motor. It is possible to use low power rating power electronics switches for the proposed motor. The simulation results also validate low torque ripples and high-power density in the proposed motors. Finally, the fault analysis of the designed motors shows that the fault tolerance capability increases as the phase number increases.

Improving the energy efficiency of electric drives for auxiliary units of traction rolling stock

The review of technical solutions and schematic characteristics of auxiliary drives for traction vehicles has shown that the most rational variant is an electric drive with an induction machine. Given the operating modes of the auxiliary drives and the share of their power consumption in the total locomotive power, the task of using scalar control systems for induction machines becomes relevant. Based on a mathematical model describing the dynamic energy conversion processes in the T-shape substitution circuit of an induction motor, taking into account stator steel losses and current displacement effects in the rotor winding and saturation along the main magnetic path, possibilities for reducing stator current have been investigated. In order to improve the energy efficiency of electric drives two variants of control system have been proposed. One based on search method of self-tuning to the stator current minimum and the other - on maintaining the power factor of induction motor at the level that ensures equality of active and reactive components of stator current. The hardware and software requirements for implementing control systems have been analysed. Modelling using Matlab has shown that both control systems work - power loss reduction can be as low as 50% and as high as 60% in certain modes.

Combined RBFN based MPPT and d-axis stator current control for permanent magnet synchronous generators

This paper proposes a new radial basis function neural network maximum power point tracking controller based on a differential evolution algorithm for machine side converter of permanent magnet synchronous generator wind turbine under variable wind speed. Direct axis stator current control methods of permanent magnet synchronous machine are reviewed shortly. A combined radial basis function neural network-based network maximum power point tracking method and d axis stator current control techniques including zero d axis stator current, unity power factor, and constant stator flux-linkage have been implemented to control the machine side converter of permanent magnet synchronous generator wind turbine. The dynamic performance of the proposed approach is assessed under different operating conditions through a simulation model based on MATLAB. It has been seen that the radial basis function neural network controller can not only track well the maximum power point but also can be reduced costly.

Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox

Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial to develop effective current signal processing methods. Some researchers have developed mathematical models to study the characteristics of current signals. However, no one has considered the coupling of rotor eccentricity and gear failures, resulting in an inaccurate analysis of the current signals. This study considers the sun gear failure of a planetary gearbox and the eccentricity of the motor rotor. An improved induction motor model is proposed based on the magnetomotive force (MMF) to simulate the stator current. By analyzing the current, the modulation relationships of gearbox meshing frequency, fault frequency, power supply frequency, and gear rotating frequency are obtained. The proposed model is validated to some extent using experimental data.