Comparison of hybrid-excitation fault-tolerant in-wheel motor drives for electric vehicles

Paper no. DS2-23Hybrid-excitation in-wheel motor drive receives the attractive merit for its fault-tolerant operation. This paper gives the performance comparison of three types of hybrid-excitation in-wheel motor drives in electric vehicles (EVs) for their fault-tolerant operations. By using finite-time element analysis, the torque output is utilized as the fault indicator to investigate the performances of each motor drive under normal, faulty, and remedial operations.published_or_final_versio

Electromagnetic design of a new hybrid-excited flux-switching machine for fault-tolerant operations

Paper no. YD-011541In this paper, a new hybrid-excited flux-switching (HEFS) machine is proposed with the outer-rotor configuration, which possesses the distinct feature of fault-tolerant operation. Comparing with the conventional permanent-magnet (PM) machine, it combines merits of flux control, high mechanical integrity, and low-cost. Furthermore, its fault-tolerant feature ensures its continuous operation in the event of winding faults. Hence, a new 12/10-pole HEFS machine is designed and implemented in this paper. By using time-stepping finite element method, open circuit (OC) fault and short circuit (SC) faults on the armature winding are investigated in the proposed machine for the fault-tolerant operation. The phase-current reconfiguration and flux control are applied for the remediation of the OC fault, while the SC faults is remedied by the phase-current reconfiguration merely. Both approaches demonstrate their good performances for the fault-tolerant operation. © 2015 IEEE.postprin

Real-Time Fault Diagnosis of Permanent Magnet Synchronous Motor and Drive System

Permanent Magnet Synchronous Motors (PMSMs) have gained massive popularity in industrial applications such as electric vehicles, robotic systems, and offshore industries due to their merits of efficiency, power density, and controllability. PMSMs working in such applications are constantly exposed to electrical, thermal, and mechanical stresses, resulting in different faults such as electrical, mechanical, and magnetic faults. These faults may lead to efficiency reduction, excessive heat, and even catastrophic system breakdown if not diagnosed in time. Therefore, developing methods for real-time condition monitoring and detection of faults at early stages can substantially lower maintenance costs, downtime of the system, and productivity loss. In this dissertation, condition monitoring and detection of the three most common faults in PMSMs and drive systems, namely inter-turn short circuit, demagnetization, and sensor faults are studied. First, modeling and detection of inter-turn short circuit fault is investigated by proposing one FEM-based model, and one analytical model. In these two models, efforts are made to extract either fault indicators or adjustments for being used in combination with more complex detection methods. Subsequently, a systematic fault diagnosis of PMSM and drive system containing multiple faults based on structural analysis is presented. After implementing structural analysis and obtaining the redundant part of the PMSM and drive system, several sequential residuals are designed and implemented based on the fault terms that appear in each of the redundant sets to detect and isolate the studied faults which are applied at different time intervals. Finally, real-time detection of faults in PMSMs and drive systems by using a powerful statistical signal-processing detector such as generalized likelihood ratio test is investigated. By using generalized likelihood ratio test, a threshold was obtained based on choosing the probability of a false alarm and the probability of detection for each detector based on which decision was made to indicate the presence of the studied faults. To improve the detection and recovery delay time, a recursive cumulative GLRT with an adaptive threshold algorithm is implemented. As a result, a more processed fault indicator is achieved by this recursive algorithm that is compared to an arbitrary threshold, and a decision is made in real-time performance. The experimental results show that the statistical detector is able to efficiently detect all the unexpected faults in the presence of unknown noise and without experiencing any false alarm, proving the effectiveness of this diagnostic approach.publishedVersio

Low-cost motor drive embedded fault diagnosis systems

Electric motors are used widely in industrial manufacturing plants. Bearing faults, insulation faults, and rotor faults are the major causes of electric motor failures. Based on the line current analysis, this dissertation mainly deals with the low cost incipient fault detection of inverter-fed driven motors. Basically, low order inverter harmonics contributions to fault diagnosis, a motor drive embedded condition monitoring method, analysis of motor fault signatures in noisy line current, and a few specific applications of proposed methods are studied in detail. First, the effects of inverter harmonics on motor current fault signatures are analyzed in detail. The introduced fault signatures due to harmonics provide additional information about the motor faults and enhance the reliability of fault decisions. It is theoretically and experimentally shown that the extended fault signatures caused by the inverter harmonics are similar and comparable to those generated by the fundamental harmonic on the line current. In the next chapter, the reference frame theory is proposed as a powerful toolbox to find the exact magnitude and phase quantities of specific fault signatures in real time. The faulty motors are experimentally tested both offline, using data acquisition system, and online, employing the TMS320F2812 DSP to prove the effectiveness of the proposed tool. In addition to reference frame theory, another digital signal processor (DSP)-based phasesensitive motor fault signature detection is presented in the following chapter. This method has a powerful line current noise suppression capability while detecting the fault signatures. It is experimentally shown that the proposed method can determine the normalized magnitude and phase information of the fault signatures even in the presence of significant noise. Finally, a signal processing based fault diagnosis scheme for on-board diagnosis of rotor asymmetry at start-up and idle mode is presented. It is quite challenging to obtain these regular test conditions for long enough time during daily vehicle operations. In addition, automobile vibrations cause a non-uniform air-gap motor operation which directly affects the inductances of electric motor and results quite noisy current spectrum. The proposed method overcomes the challenges like aforementioned ones simply by testing the rotor asymmetry at zero speed

Fault Signature Identification for BLDC motor Drive System -A Statistical Signal Fusion Approach

A hybrid approach based on multirate signal processing and sensory data fusion is proposed for the condition monitoring and identification of fault signal signatures used in the Flight ECS (Engine Control System) unit. Though motor current signature analysis (MCSA) is widely used for fault detection now-a-days, the proposed hybrid method qualifies as one of the most powerful online/offline techniques for diagnosing the process faults. Existing approaches have some drawbacks that can degrade the performance and accuracy of a process-diagnosis system. In particular, it is very difficult to detect random stochastic noise due to the nonlinear behavior of valve controller. Using only Short Time Fourier Transform (STFT), frequency leakage and the small amplitude of the current components related to the fault can be observed, but the fault due to the controller behavior cannot be observed. Therefore, a framework of advanced multirate signal and data-processing aided with sensor fusion algorithms is proposed in this article and satisfactory results are obtained. For implementing the system, a DSP-based BLDC motor controller with three-phase inverter module (TMS 320F2812) is used and the performance of the proposed method is validated on real time data.Comment: 7 Pages, 7 figure

Harmonic Order Tracking Analysis: A Novel Method for Fault Diagnosis in Induction Machines

(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] The diagnosis of induction machines using Fourier transform relies on tracking the frequency signature of each type of fault in the current's spectrum, but this signature depends on the machine's slip and the supply frequency, so it must be recomputed for each working condition by trained personnel or by diagnostic software. Besides, sampling the current at high rates during long times is needed to achieve a good spectral resolution, which requires large memory space to store and process the current spectra. In this paper, a novel approach is proposed to solve both problems. It is based on the fact that each type of fault generates a series of harmonics in the current's spectrum, whose frequencies are multiples of a characteristic main fault frequency. The tracking analysis of the fault components using the harmonic order (defined as the frequency in per unit of the main fault frequency) as independent variable instead of the frequency generates a unique fault signature, which is the same for any working condition. Besides, this signature can be concentrated in just a very small set of values, the amplitudes of the components with integer harmonic order. This new approach is introduced theoretically and validated experimentally.This work was supported by the Spanish "Ministerio de Ciencia e Innovacion" in the framework of the "Programa Nacional de proyectos de Investigacion Fundamental" under Project Reference DPI2011-23740. Paper no. TEC-00644-2013.Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R.; Pérez-Cruz, J.; Roger-Folch, J.; Riera-Guasp, M.; Martinez-Roman, J. (2015). Harmonic Order Tracking Analysis: A Novel Method for Fault Diagnosis in Induction Machines. IEEE Transactions on Energy Conversion. 30(3):833-841. https://doi.org/10.1109/TEC.2015.241697383384130

Real-Time Detection of Incipient Inter-Turn Short Circuit and Sensor Faults in Permanent Magnet Synchronous Motor Drives Based on Generalized Likelihood Ratio Test and Structural Analysis

This paper presents a robust model-based technique to detect multiple faults in permanent magnet synchronous motors (PMSMs), namely inter-turn short circuit (ITSC) and encoder faults. The proposed model is based on a structural analysis, which uses the dynamic mathematical model of a PMSM in an abc frame to evaluate the system’s structural model in matrix form. The just-determined and over-determined parts of the system are separated by a Dulmage–Mendelsohn decomposition tool. Subsequently, the analytical redundant relations obtained using the over-determined part of the system are used to form smaller redundant testable sub-models based on the number of defined fault terms. Furthermore, four structured residuals are designed based on the acquired redundant sub-models to detect measurement faults in the encoder and ITSC faults, which are applied in different levels of each phase winding. The effectiveness of the proposed detection method is validated by an in-house test setup of an inverter-fed PMSM, where ITSC and encoder faults are applied to the system in different time intervals using controllable relays. Finally, a statistical detector, namely a generalized likelihood ratio test algorithm, is implemented in the decision-making diagnostic system resulting in the ability to detect ITSC faults as small as one single short-circuited turn out of 102, i.e., when less than 1% of the PMSM phase winding is short-circuited.publishedVersio

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

[EN] Induction machines drive many industrial processes and their unexpected failure can cause heavy producti on losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain¿such as a spectrogram¿is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it¿short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.This research was funded by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i - Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Riera-Guasp, M.; Sapena-Bano, A.; Pineda-Sanchez, M. (2019). Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines. Energies. 12(17):1-18. https://doi.org/10.3390/en12173361S118121