Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

© 2015 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng 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] Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.Riera-Guasp, M.; Antonino-Daviu, J.; Capolino, G. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics. 62(3):1746-1759. doi:10.1109/TIE.2014.2375853S1746175962

Computationally Efficient Optimization of a Five-Phase Flux-Switching PM Machine Under Different Operating Conditions

This paper investigates the comparative design optimizations of a five-phase outer-rotor flux-switching permanent magnet (FSPM) machine for in-wheel traction applications. To improve the comprehensive performance of the motor, two kinds of large-scale design optimizations under different operating conditions are performed and compared, including the traditional optimization performed at the rated operating point and the optimization targeting the whole driving cycles. Three driving cycles are taken into account, namely, the urban dynamometer driving schedule (UDDS), the highway fuel economy driving schedule (HWFET), and the combined UDDS/HWFET, representing the city, highway, and combined city/highway driving, respectively. Meanwhile, the computationally efficient finite-element analysis (CE-FEA) method, the cyclic representative operating points extraction technique, as well as the response surface methodology (in order to minimize the number of experiments when establishing the inverse machine model), are presented to reduce the computational effort and cost. From the results and discussion, it will be found that the optimization results against different operating conditions exhibit distinct characteristics in terms of geometry, efficiency, and energy loss distributions. For the traditional optimization performed at the rated operating point, the optimal design tends to reduce copper losses but suffer from high core losses; for UDDS, the optimal design tends to minimize both copper losses and PM eddy-current losses in the low-speed region; for HWFET, the optimal design tends to minimize core losses in the high-speed region; for the combined UDDS/HWFET, the optimal design tends to balance/compromise the loss components in both the low-speed and high-speed regions. Furthermore, the advantages of the adopted optimization methodologies versus the traditional procedure are highlighted

Wavelet packet decomposition-based fault diagnosis scheme for SRM drives with a single current sensor

Power converters are a key, but vulnerable component in switched reluctance motor (SRM) drives. In this paper, a new fault diagnosis scheme for SRM converters is proposed based on the wavelet packet decomposition (WPD) with a dc-link current sensor. Open- and short-circuit faults of the power switches in an asymmetrical half-bridge converter are analyzed in details. In order to obtain the fault signature from the phase currents, two pulse-width modulation signals with phase shift are injected into the lower-switches of the converter to extract the excitation current, and the WPD algorithm is then applied to the detected currents for fault diagnosis. Moreover, a discrete degree of the wavelet packet node energy is chosen as the fault coefficient. The converter faults can be diagnosed and located directly by determining the changes in the discrete degree from the detected currents. The proposed scheme requires only one current sensor in the dc link, while conventional methods need one sensor for each phase or additional detection circuits. The experimental results on a 750-W three-phase SRM are presented to confirm the effectiveness of the proposed fault diagnosis scheme

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

Control of a nine-phase symmetrical PMSM with reduced rare earth material

The rising demand for high-power fault-tolerant applications such as wind generators and electric vehicles, alongside the desire to achieve better performance, have directed the interests of many research centres around the world towards electric drive configurations comprising AC machines with more than three stator phases. These so-called multiphase machines have become well recognized as an attractive alternative to the conventional three-phase machines and are used when the three-phase counterpart cannot provide a drive system with the desired performance. The Thesis examines advanced control possibilities for multiphase surface-mounted permanent magnet synchronous machines (PMSMs). Although it is well-known that permanent magnet machines are today the first choice in many applications and that their market is anticipated to catch up with the induction machines market in the near future, the main drawbacks of this machine type are the relatively high capital costs, the security of magnet supply and the environmental costs associated with the rear-earth magnet materials used in the rotor construction. This has motivated researchers to investigate methods to reduce the amount of rare earth material used in the construction of these machines. If the amount of permanent magnet material is reduced, this will inevitably result in a machine which produces lower electromagnetic tor que. On the other hand, the additional degrees of freedom, present in multiphase systems, can be exploited to inject, into the stator windings, harmonic current(s) to enhance the developed torque. This work analyses a new nine-phase symmetrical PMSM with two surface mounted magnet poles on the rotor with a shortened span. This simple design produces a highly non-sinusoidal back-electromotive force (back-EMF) comprising high third and fifth harmonic components. It is shown that these harmonic components can be utilised to boost the torque to near the value obtainable with full span magnets, provided a suitable control system is developed. The developed control algorithm is based on the well-known vector space decomposition (VSD) and classic field-oriented control methods. To test the developed control algorithm, phase domain machine model is presented first, for both sinusoidal and non-sinusoidal back-EMF distributions. To transform variables from one reference frame to another, the VSD and rotational transformations are used. The optimal ratios between fundamental and other harmonic current components are derived using the maximal torque-per-Ampere (MTPA) theory. It is shown that, by using optimal current injection, the electromagnetic torque can be improved by 36% with third harmonic only, and, up to 45% with a combination of the fundamental, the third and the fifth harmonics. Simulation results are validated in finite element method software and afterwards verified experimentally using an experimental prototype. Control of the PMSM is next expanded with position sensor fault-tolerant capability. For this purpose, the same EMF spectrum is used. When harmonic current elimination is performed in x-y subspace, remaining hth harmonic order back-EMF can be efficiently used for position angle and speed estimation. For the estimation purpose, phase-locked-loop method is employed. With estimated position/speed, a new control algorithm is devised, which combines control in two auxiliary subspaces with the control of the first plane. The third harmonic is, in combination with the fifth, used for the torque boost prior to the fault, while afterwards, the fifth EMF harmonic enables position estimation for position-sensorless control. Hence, previously stated maximal torque improvement is preserved until position sensor fault is detected, while afterwards machine continues to operate in position-sensorless mode still with partial enhancement of the torque. Control is verified experimentally. Finally, operation in the flux-weakening region is investigated. Because finding sets of multiple harmonic current references which maximize torque by taking into account voltage and current limits leads to a difficult problem to formulate, which is often impossible to solve analytically, the work presented here builds on (offline) numerical optimisation procedure. To obtain best performance, harmonics up to the (and including) fifth are considered. Limitation of voltage is achieved by comparing measured phase-to-phase voltage with maximal dc-link voltage, while thermal (RMS) constraint and inverter switch (peak) current constraint are taken into account by limiting the current. In such scenario, maximal reachable speed is much higher than the base speed, while respecting at the same time both machine and inverter constraints

Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion

Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio

Advances in Rotating Electric Machines

It is difficult to imagine a modern society without rotating electric machines. Their use has been increasing not only in the traditional fields of application but also in more contemporary fields, including renewable energy conversion systems, electric aircraft, aerospace, electric vehicles, unmanned propulsion systems, robotics, etc. This has contributed to advances in the materials, design methodologies, modeling tools, and manufacturing processes of current electric machines, which are characterized by high compactness, low weight, high power density, high torque density, and high reliability. On the other hand, the growing use of electric machines and drives in more critical applications has pushed forward the research in the area of condition monitoring and fault tolerance, leading to the development of more reliable diagnostic techniques and more fault-tolerant machines. This book presents and disseminates the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of all types of rotating electric machines

Expanding Limit of Minimum Sampling Time Using Auxiliary Vectors for PMSM Drives with Single DC-Link Current Sensor

Phase current reconstruction (PCR) strategy can improve the fault tolerance of permanent magnet synchronous motor (PMSM) drives. The PCR precision is largely affected by the unmeasurable zones and time-sharing sampling errors. The upper limit (Tlimit) of PCR allowable range can reflect the requirement of different PCR methods for the minimum sampling time (Tmin). With a longer Tlimit, there is sufficient time for sampling, even if Tlimit is halved due to the symmetrical waveform. Therefore, the extension of Tlimit is the key to eliminate the unmeasurable zones and time-sharing sampling errors. In this paper, a method to increase Tlimit is proposed, which introduces the suitable auxiliary vectors (AVs) in different regions to extend the duration time of the sampling vectors. With the help of a longer Tlimit (12.5%Ts), its possible to eliminate all the unmeasurable zones and time-sharing sampling errors, relieve the pressure on the hardware of current loop, improve the sampling accuracy, and facilitate the reliable operation of the drive. Besides, the switching action times of IGBTs can be reduced by about one-third in the high modulation area. The proposed method is finally proved to accurately reconstruct the phase currents by the experimental results on the PMSM prototype