Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis

© 2017 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] With the expansion of the use of electrical drive sys- tems to more critical applications, the issue of reliability and fault mitigation and condition-based maintenance have consequently taken an increasing importance: it has become a crucial one that cannot be neglected or dealt with in an ad-hoc way. As a result research activity has increased in this area, and new methods are used, some based on a continuation and improvement of previous accomplishments, while others are applying theory and techniques in related areas. This Special Section of the IEEE Transactions on Industrial Informatics attracted a number of papers dealing with Advanced Signal and Image Processing Techniques for Electric Machine and Drives Fault Diagnosis and Prognosis. This editorial aims to put these contributions in context, and highlight the new ideas and directions therein.Antonino-Daviu, J.; Lee, SB.; Strangas, E. (2017). Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis. IEEE Transactions on Industrial Informatics. 13(3):1257-1260. doi:10.1109/TII.2017.2690464S1257126013

Industry 4.0 in maintenance: Using condition monitoring in electric machines

Industry traditionally considered maintenance as a cost and a necessity to replace equipment and machines, but the path has changed to better focus on maintenance to prevent faults and it was designated as predictive. The ones motivated to take these advantages are faced with two of the biggest barriers: the investment it requires and the difficulty to develop algorithms. The costs of installation are still high, but the avoided costs surpass it. Also, Internet of Things (IoT) has brought a big shift, which is been known as the industry 4.0. One of the potentials in maintenance is the conditioning monitoring. Condition monitoring sensors and devices are now linked to maintenance platforms, providing real-time data. This new connectivity is both more affordable and easier to implement than predictive maintenance. Real-time data allows managers to adjust preventive maintenance plans while providing greater reliability. At the same time, artificial intelligence manages this data to recognize patterns, which is one of the most promising advances in digital reliability. So, regardless of the ability to immediately implement a preventive maintenance plan, condition monitoring is an asset itself. The present paper presents the common faults on electric machines, their effects, their impact on the industry and the main techniques on condition control to prevent them. It is also added the reflection on the use of IoT to enhance the potential of condition control maintenance. The implementation of continuous improvement actions throughout the life of the equipment allows to increase efficiency, either by overcoming weaknesses or by adapting production or operational capacities to processes, production or maintenance, avoiding under maintenance or over maintenance and minimizing operating costs.info:eu-repo/semantics/publishedVersio

Induction Machine Diagnosis using Stator Current Advanced Signal Processing

International audienceInduction machines are widely used in industrial applications. Safety, reliability, efficiency and performance are major concerns that direct the research activities in the field of electrical machines. Even though the induction machines are very reliable, many failures can occur such as bearing faults, air-gap eccentricity and broken rotor bars. Therefore, the challenge is to detect them at an early stage in order to prevent breakdowns. In particular, stator current-based condition monitoring is an extensively investigated field for cost and maintenance savings. In fact, several signal processing techniques for stator current-based induction machine faults detection have been studied. These techniques can be classified into: spectral analysis approaches, demodulation techniques and time-frequency representations. In addition, for diagnostic purposes, more sophisticated techniques are required in order to determine the faulty components. This paper intends to review the spectral analysis techniques and time-frequency representations. These techniques are demonstrated on experimental data issued from a test bed equipped with a 0.75 kW induction machine. Nomenclature O&M = Operation and Maintenance; WTG = Wind Turbine Generator; MMF = Magneto-Motive Force; MCSA = Motor Current signal Analysis; PSD = Power Spectral Density; FFT = Fast Fourier Transform; DFT = Discrete Fourier Transform; MUSIC = MUltiple SIgnal Characterization; ESPRIT = Estimation of Signal Parameters via Rotational Invariance Techniques; SNR = Signal to Noise Ratio; MLE = Maximum Likelihood Estimation; STFT = Short-Time Fourier Transform; CWT = Continuous Wavelet Transform; WVD = Wigner-Ville distribution; HHT = Hilbert-Huang Transform; DWT = Discrete Wavelet Transform; EMD = Empirical Mode Decomposition; IMF = Intrinsic Mode Function; AM = Amplitude Modulation; FM = Frequency Modulation; IA = Instantaneous Amplitude; IF = Instantaneous Frequency; í µí± ! = Supply frequency; í µí± ! = Rotational frequency; í µí± ! = Fault frequency introduced by the modified rotor MMF; í µí± ! = Characteristic vibration frequencies; í µí± !"# = Bearing defects characteristic frequency; í µí± !" = Bearing outer raceway defect characteristic frequency; í µí± !" = Bearing inner raceway defect characteristic frequency; í µí± !" = Bearing balls defect characteristic frequency; í µí± !"" = Eccentricity characteristic frequency; í µí± ! = Number of rotor bars or rotor slots; í µí± = Slip; í µí°¹ ! = Sampling frequency; í µí± = Number of samples; í µí±¤[. ] = Time-window (Hanning, Hamming, etc.); í µí¼ = Time-delay; í µí¼ ! = Variance; ℎ[. ] = Time-window

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

Design and Development of a Next Generation Energy Storage Flywheel

Energy storage is crucial for both smart grids and renewable energy sources such as wind or solar, which are intermittent in nature. Compared to electrochemical batteries, flywheel energy storage systems (FESSs) offer many unique benefits such as low environmental impact, high power quality, and larger life cycles. This dissertation presents the design and development of a novel utility-scale FESS that features a shaftless, hubless rotor. The unique shaftless design gives it the potential of a doubled energy density and a compact form factor. Its energy and power capacities are 100 kWh and 100 kW, respectively. The flywheel is made of high-strength steel, which makes it much easier to manufacture, assemble, and recycle. Steels also cost much less than composite materials. In addition, the system incorporates a new combination active magnetic bearing. Its working principle and the levitation control for the flywheel are presented. The development of an integrated, coreless, permanent-magnet (PM) motor/generator for the flywheel is briefly discussed as well. Initial test results show that the magnetic bearing provides stable levitation for the 5443-kg flywheel with small current consumptions. Furthermore, this dissertation formulates and synthesizes a detailed model for designing and simulating a closed-loop control system for the proposed flywheel system at high speed. To this end, the magnetic bearing supporting structure is considered flexible and modeled by finite element modeling. The magnetic bearing is characterized experimentally by static and frequency-dependent coefficients, the latter of which are caused by eddy current effects and presents challenges to the levitation control. Sensor- runout disturbances are measured and included in the model. System nonlinearities in power amplifiers and the controller are considered as well. Even though the flywheel has a large ratio of the primary-to-transversal moment of inertias, Multi-Input-Multi-Output (MIMO) feedback control demonstrates its effectiveness in canceling gyroscopic torques and stabilize the system. Various stages of PD controllers, lead/lag compensators, and notch filters are also implemented to suppress the high-frequency sensor disturbances and structural vibrations

Design and fabrication of a long-life Stirling cycle cooler for space application. Phase 3: Prototype model

A second-generation, Stirling-cycle cryocooler (cryogenic refrigerator) for space applications, with a cooling capacity of 5 watts at 65 K, was recently completed. The refrigerator, called the Prototype Model, was designed with a goal of 5 year life with no degradation in cooling performance. The free displacer and free piston of the refrigerator are driven directly by moving-magnet linear motors with the moving elements supported by active magnetic bearings. The use of clearance seals and the absence of outgassing material in the working volume of the refrigerator enable long-life operation with no deterioration in performance. Fiber-optic sensors detect the radial position of the shafts and provide a control signal for the magnetic bearings. The frequency, phase, stroke, and offset of the compressor and expander are controlled by signals from precision linear position sensors (LVDTs). The vibration generated by the compressor and expander is cancelled by an active counter balance which also uses a moving-magnet linear motor and magnetic bearings. The driving signal for the counter balance is derived from the compressor and expander position sensors which have wide bandwidth for suppression of harmonic vibrations. The efficiency of the three active members, which operate in a resonant mode, is enhanced by a magnetic spring in the expander and by gas springs in the compressor and counterbalance. The cooling was achieved with a total motor input power of 139 watts. The magnetic-bearing stiffness was significantly increased from the first-generation cooler to accommodate shuttle launch vibrations