Angel Sapena-Bano

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

The diagnosis of induction machines using Fourier transform relies on tracking the frequency signature of each type of fault in the currents spectrum, but this signature depends on the machines 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 currents 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.

Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current

Fault diagnosis of rotor asymmetries in induction machines working at a very low slip, through Fourier-based methods, usually requires a long acquisition time to achieve a high spectral resolution and a high sampling frequency to reduce aliasing effects. However, this approach generates a huge amount of data, which makes its implementation difficult using embedded devices with small internal memory, such as digital signal processors and field programmable gate arrays or devices with low computing power. In this paper, a new simplified diagnostic signal designated as the reduced envelope of the stator current is introduced to address this problem. The reduced envelope signal is built using only one sample of the current per cycle without any further processing, and it is demonstrated that it carries the same spectral information about the fault as the full-length current signal. Based on this approach, an embedded device has only to store and process a minimal set of samples compared with the raw current signal for a desired resolution. In this paper, the theoretical basis of the proposed method is presented, as well as its experimental validation using two different motors with broken bars: 1) a high-power induction motor working in a factory; and 2) a low-power induction motor mounted in a laboratory test bed.

Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime

Transient-based methods for fault diagnosis of induction machines (IMs) are attracting a rising interest, due to their reliability and ability to adapt to a wide range of IM’s working conditions. These methods compute the time–frequency (TF) distribution of the stator current, where the patterns of the related fault components can be detected. A significant amount of recent proposals in this field have focused on improving the resolution of the TF distributions, allowing a better discrimination and identification of fault harmonic components. Nevertheless, as the resolution improves, computational requirements (power computing and memory) greatly increase, restricting its implementation in low-cost devices for performing on-line fault diagnosis. To address these drawbacks, in this paper, the use of the short-frequency Fourier transform (SFFT) for fault diagnosis of induction machines working under transient regimes is proposed. The SFFT not only keeps the resolution of traditional techniques, such as the short-time Fourier transform, but also achieves a drastic reduction of computing time and memory resources, making this proposal suitable for on-line fault diagnosis. This method is theoretically introduced and experimentally validated using a laboratory test bench.

The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

This paper introduces a new approach for improving the fault diagnosis in induction motors under time-varying conditions. A significant amount of published approaches in this field rely on representing the stator current in the time-frequency domain, and identifying the characteristic signatures that each type of fault generates in this domain. However, time-frequency transforms produce three-dimensional (3-D) representations, very costly in terms of storage and processing resources. Moreover, the identification and evaluation of the fault components in the time-frequency plane requires a skilled staff or advanced pattern detection algorithms. The proposed methodology solves these problem by transforming the complex 3-D spectrograms supplied by time-frequency tools into simple

Harmonic order tracking analysis: A speed-sensorless method for condition monitoring of wound rotor induction generators in wind turbines

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Condition monitoring of electrical machines using low computing power devices

graphs, similar to conventional Fourier spectra. These graphs display a unique pattern for each type of fault, even under supply or load time-varying conditions, making easy and reliable the diagnostic decision even for nonskilled staff. Moreover, the resulting patterns can be condensed in a very small dataset, reducing greatly the storage or transmission requirements regarding to conventional spectrograms. The proposed method is an extension to nonstationary conditions of the harmonic order tracking approach. It is introduced theoretically and validated experimentally by using the commercial induction motors feed through electronic converters.

Harmonic order tracking analysis: A novel method for the diagnosis of induction generators

This paper introduces a speed-sensorless method for detecting rotor asymmetries in wound rotor induction machine (WRIM) working as generator in wind turbines. The method is based on time-frequency analysis of rotors currents and a new transformation, which make it suitable for every working condition of the machine: steady state, transient regime and non-stationary conditions. Besides, the results are shown in the same sort of plot regardless the working conditions, reducing the number of point to be stored and highlighting the presence or absence of faults in a simple and clear way. Simulation and experimental results show the validity of the method to detect rotor electrical faults in WRIM under any working condition.

Multilayer Park's vector approach, a method for fault detection on induction motors

The diagnosis of electrical motors through the detection of fault frequency signatures in the currents spectrum has become an established standard in the field of industrial maintenance systems. Nevertheles, its implementation on devices with low computing power remains a practical challenge. Industrial controllers, such as programmable logic controllers, or modern, low cost controller hardware, such as the Arduino or the Raspberry Pi open source hardware proposals, lack both the on-board memory and the high speed data acquisition hardware to perform an accurate spectral analysis of the machines current, in order to identify the spectral components produced by each type of fault. In this paper, a signal conditioning unit, based on a novel downsampling method of the current, is presented. This unit reduces the amount of current samples that must be processed by the diagnostic unit to a mere sample per current cycle, maintains the sub-hertz accuracy needed to resolve fault, and converts the mains component into a constant value that can be easily eliminated without using any additional filter. Besides, it is implemented using low cost devices, just resistors and operational amplifiers. The proposed method is theoretically developed in this paper, and it has been validated using induction motors with broken bars fed directly by the mains or through variable speed drives.

Automatic translation of Programmable Logic Controllers (PLC) control programs in packaging machinery

Improved fault diagnostic techniques in wind turbines is a field of growing interest, given the negative impact that unexpected breakdowns have on the profitability of wind farms. New diagnostic techniques based on generator currents monitoring have recently been developed, but their use is still irrelevant despite the advantages that current monitoring presents versus monitoring vibrations. In part, this situation can be due to the needs of relatively high computing power not available in the wind-groups and also, to the use of signals that generate volumes of data difficult to transfer to control centers, where they could be processed. This paper introduces a methodology that aims to solve these problems. In this paper a novel diagnostic method based on monitoring the generator currents is proposed. This approach is based on the tracking analysis of the fault components using the harmonic order as independent variable. This approach can be implemented in low cost digital devices; the resultant patterns are very simple, since their shape is the same no matter the changes in the fundamental frequency and thus, are easily interpretable even by non-qualified persons. Moreover these patterns are characterized by a very low number of parameters, which make easy their transmission to remote control centers. This new approach is theoretically justified and validated by laboratory tests.

Partial Inductance Model of Induction Machines for Fault Diagnosis

This paper introduces a new method of fault diagnosis for induction motors, the Multilayer Parks Vector Approach, derived from the well-known fault diagnostic method based on Parks Vector Approach analysis. This new method allows the detection of failures in steady and transient regimes operation on induction motors. The resulting vector generated provides the information about deviations in the circularity of Parks vector for each motor cycle, allowing with this information the diagnosis of failures. As advantages, this method have a lower computational cost than others methods derived from the fault analysis with Parks Vector Approach. This requires only knowledge of supply frequency and control method, without setting parameters and without motor specification. To test the reliability of this method, a test of broken rotor bars through Multilayer Parks Vector Approach has been done, obtaining relevant characteristics for the successful broken rotor bar detection.