On the reliability of electrical drives for safety-critical applications

The aim of this work is to present some issues related to fault tolerant electric drives,which are able to overcome different types of faults occurring in the sensors, in thepower converter and in the electrical machine, without compromising the overallfunctionality of the system. These features are of utmost importance in safety-criticalapplications. In this paper, the reliability of both commercial and innovative driveconfigurations, which use redundant hardware and suitable control algorithms, will beinvestigated for the most common types of fault: besides standard three phase motordrives, also multiphase topologies, open-end winding solutions, multi-machineconfigurations will be analyzed, applied to various electric motor technologies. Thecomplexity of hardware and control strategies will also be compared in this paper, sincethis has a tremendous impact on the investment costs

Trends in Fault Diagnosis for Electrical Machines

[EN] The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years. Reducing maintenance costs and preventing unscheduled downtimes, which result in losses of production and financial incomes, are the priorities of electrical drives manufacturers and operators. In fact, both correct diagnosis and early detection of incipient faults lead to fast unscheduled maintenance and short downtime for the process under consideration. They also prevent the harmful and sometimes devastating consequences of faults and failures. This topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years. The total number of operating electrical machines in the world was around 16.1 billion in 2011, with a growth rate of about 50% in the last five years [1].Henao, H.; Capolino, G.; Fernández-Cabanas, M.; Filippetti, F.; Bruzzese, C.; Strangas, E.; Pusca, R.... (2014). Trends in Fault Diagnosis for Electrical Machines. IEEE Industrial Electronics Magazine. 8(2):31-42. doi:10.1109/MIE.2013.2287651S31428

Pattern recognition and diagnosis of short and open circuit faults inverter in induction motor drive using neural networks

Nowadays, feeding induction motors with voltage source inverters under faulty conditions is a major challenge. For this reason, electrical systems must be well thought out to provide good diagnostics for these elements. Consequently, the early detection of faults is very important to establish strategies that allow us to control the operation and take preventive measures to avoid frequent failures. Our aim in this paper is to train multilayer neural networks using features extracted from currents and voltages measurements to detect and classify open and short-circuit switch faults in source voltage inverters. Simulation results show that instead of using several types of features extracted from measurements of several signal cycles as in previous works, a two-component feature obtained from one cycle is sufficient to obtain an excellent accuracy. The normalized mean Clark currents and the power spectrum using the fast Fourier transform have been used as features for open switches and short-circuit faults respectively

Measurements, Models, Systems and Design

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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

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

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