Joan Pons-Llinares

Induction Motor Diagnosis Based on a Transient Current Analytic Wavelet Transform via Frequency B-Splines

In this paper, a new induction motor diagnosis methodology is proposed. The approach is based on obtaining a 2-D time-frequency plot representing the time-frequency evolution of the main components in an electrical machine transient current. The identification of characteristic patterns in the time-frequency plane caused by many of the fault-related components enables a reliable machine diagnosis. Unlike other continuous-wavelet-transform-based methods, this work uses frequency B-spline (FBS) wavelets. It is shown that these wavelets enable an efficient filtering in the region neighboring the main frequency, as well as enable a high level of detail in the time-frequency maps. As a consequence, the evolution of the most important current components is precisely traced. These characteristics make it easy to identify the patterns related to the fault components. The technique is applied to the experimental no-load start-up current of motors in a healthy state and with broken bars; the FBS capabilities are revealed. One of the novelties of this paper is the fact that the diagnosis is carried out via the identification not only of the traditional lower sideband harmonic but also of the upper sideband harmonic and four additional fault-related components.

Detection of Broken Outer-Cage Bars for Double-Cage Induction Motors Under the Startup Transient

Unlike single-cage rotor fault detection, fast Fourier transform (FFT)-based steady-state spectrum analysis techniques can fail to detect outer-cage faults in double-cage induction motors due to the small outer-cage current under running conditions. Double-cage motors are typically employed in applications that require loaded starts. This makes the outer cage vulnerable to fatigue failure since it must withstand the high starting current and long startup time frequently. However, there are only a few publications that investigate detection techniques specifically for double-cage motors. In this paper, considering that the influence of the faulty outer cage is strong at startup due to the large outer-cage current, detection of outer-cage faults under the startup transient is investigated. A discrete-wavelet-transform-based method is proposed as a viable solution to the detection of outer-cage faults for double-cage motors. An experimental study on fabricated copper double-cage induction motors shows that the proposed method provides sensitive and reliable detection of double-cage rotor faults compared to FFT.

Advanced Induction Motor Rotor Fault Diagnosis Via Continuous and Discrete Time–Frequency Tools

Transient-based fault diagnosis in induction motors has gained increasing attention over the recent years. This is due to its ability to avoid eventual wrong diagnostics of the conventional motor current signature analysis in certain industrial situations (presence of load toque oscillations, light loading conditions, and so on). However, the application of these transient methodologies requires the use of advanced signal processing tools. This paper presents a detailed comparison between the two main groups of transforms that are employed in transient analysis: discrete and continuous. This paper does not focus on trivial fault cases but on difficult real situations where the application of the conventional methods often leads to false diagnostics (outer bar breakages in double-cage motors, motors with rotor axial duct influence, and combined faults). Indeed, it is the first time that continuous tools are applied to some of these controversial situations. The results in this paper prove the special advantages of the continuous transforms, tearing down some false myths about their use.

Reliable Detection of Induction Motor Rotor Faults Under the Rotor Axial Air Duct Influence

Axial cooling air ducts in the rotor of large induction motors are known to produce magnetic asymmetry and can cause steady-state current or vibration spectrum analysis based fault detection techniques to fail. If the number of axial air ducts and that of poles are identical, frequency components that overlap with that of rotor faults can be produced for healthy motors. False positive rotor fault indication due to axial ducts is a common problem in the field that results in unnecessary maintenance cost. However, there is currently no known test method available for distinguishing rotor faults and false indications due to axial ducts other than offline rotor inspection or testing. Considering that there is no magnetic asymmetry under high slip conditions due to limited flux penetration into the rotor yoke, the detection of broken bars under the start-up transient is investigated in this paper. A wavelet-based detection method is proposed and verified on custom-built lab motors and 6.6-kV motors misdiagnosed with broken bars via steady-state spectrum analysis. It is shown that the proposed method provides the reliable detection of broken bars under the start-up transient independent of axial duct influence.

Application of the Teager–Kaiser Energy Operator to the Fault Diagnosis of Induction Motors

The diagnosis of induction motors through the spectral analysis of the stator current allows for the online identification of different types of faults. One of the major difficulties of this method is the strong influence of the mains component of the current, whose leakage can hide fault harmonics, especially when the machine is working at very low slip. In this paper, a new method for demodulating the stator current prior to its spectral analysis is proposed, using the Teager-Kaiser energy operator. This method is able to remove the mains component of the current with an extremely low usage of computer resources, because it operates just on three consecutive samples of the current. Besides, this operator is also capable of increasing the signal-to-noise ratio of the spectrum, sharpening the spectral peaks that reveal the presence of the faults. The proposed method has been deployed to a PC-based offline diagnosis system and tested on commercial induction motors with broken bars, mixed eccentricity, and single-point bearing faults. The diagnostic results are compared with those obtained through the conventional motor current signature analysis method.

Electric machines diagnosis techniques via transient current analysis

Induction motor condition monitoring has primarily relied on the analysis of currents during steady-state operation through the Fast Fourier Transform (FFT). Nonetheless, this conventional approach has many constraints, most of them related to the usual operation of most motors under situations differing from a pure stationary regime, or directly under transient conditions. Indeed, these situations are the most common in many industrial processes, a fact that reveals the necessity of developing techniques suited for the analysis of machine quantities during non-stationary operation. A vast work has been developed during these recent years in this area, raising many transient-based techniques being able to diagnose machines under transient conditions which furthermore overcome some of the drawbacks of conventional stationary analysis. Most of these techniques are based on the application of proper signal processing tools, especially adapted to analyze transient signals (time-frequency decomposition (TFD) tools). This paper carries out a review of the most significant techniques sustained on transient-analysis, grouping them in accordance to the nature of the TFD tool used in each case. The review intends to emphasize the most relevant contributions of each work and to serve as a useful reference to all authors involved in the area.

Evaluation of the amplitudes of high-order fault related components in double bar faults

This paper addresses the problem of diagnosing cage motors with double bar breakages involving non-consecutive rotor bars. As it is stated in previous works, the conventional diagnosis approaches fail when diagnosing this fault for certain relative positions of the broken bars. This work is focused on the study of the high order harmonics of the airgap-field produced by the fault; simple mathematical expressions are deduced which enable to evaluate the influence of the relative position of the broken bars on the amplitude of the fault related components (Left Sideband Harmonic and other high order fault components) of the stator current. These expressions demonstrate to be useful for confirming the diagnostic in uncertain cases; they are validated by finite element simulations and laboratory tests, using different diagnosis methodologies based both on steady-state and on transient analysis.

Eccentricity diagnosis in Inverter - Fed Induction Motors via the Analytic Wavelet Transform of transient currents

In this paper a recently developed induction motors diagnosis methodology is applied to detect mixed eccentricity in Inverter-Fed Induction Motors (IFIMs). The classic FFT method can not be applied when the stator current captured is not in steady state (which is common in IFIMs). The approach is based on obtaining a 2D time - frequency plot representing the time - frequency evolution of the main components in a stator transient current. The time-frequency maps are generated with high detail using the Analytic Wavelet Transform. Thanks to this, the evolutions of the main Winding Harmonics, Principal Slot Harmonics and Eccentricity Related Harmonics are traced precisely. As a consequence, the time-frequency plane characteristic patterns produced by the Eccentricity Related Harmonics are easily and clearly identified enabling a reliable diagnosis. The methodology capabilities have been shown successfully diagnosing a healthy IFIM and an IFIM with mixed eccentricity. The transients analyzed consist of a startup and a decrease in the assigned frequency.

Diagnosis of eccentricity based on the Hilbert transform of the startup transient current

The Hilbert Transform (HT) can improve the resolution of motor current signature analysis (MCSA), especially at very low slip, because it converts the supply frequency into a continuous component, which can be easily removed to better detect fault harmonics. This paper proposes its application also during speed transients, with two key advantages: first, it allows an easy filtering of the transient current component corresponding to the supply frequency, and, second, the HT allows for the generation of the Hilbert Spectrum, as a replacement of the Fourier Spectrum in the case of non-stationary signals, like those that appear in a transient regime. The performance of the proposed method is compared with other methods as the Discrete Wavelet Transform (DWT), and is validated through simulation with a mathematical model and experimental analysis of a 1.1 kW three-phase squirrel-cage commercial induction motor with eccentricity.

Detection of broken outer cage bars for double cage induction motors under the startup transient

Unlike single-cage rotor fault detection, fast Fourier transform (FFT)-based steady-state spectrum analysis techniques can fail to detect outer-cage faults in double-cage induction motors due to the small outer-cage current under running conditions. Double-cage motors are typically employed in applications that require loaded starts. This makes the outer cage vulnerable to fatigue failure since it must withstand the high starting current and long startup time frequently. However, there are only a few publications that investigate detection techniques specifically for double-cage motors. In this paper, considering that the influence of the faulty outer cage is strong at startup due to the large outer-cage current, detection of outer-cage faults under the startup transient is investigated. A discrete-wavelet-transform-based method is proposed as a viable solution to the detection of outer-cage faults for double-cage motors. An experimental study on fabricated copper double-cage induction motors shows that the proposed method provides sensitive and reliable detection of double-cage rotor faults compared to FFT.