J. Burriel-Valencia

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

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

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Support vector machines optimization for steady state diagnosis methods of induction motors. A comparative study

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.

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

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.

Low computational cost algorithm for detecting rotor asymmetries in IM based on the negative sequence component of the startup currents

Diagnosis of induction motors in industrial facilities is crucial to avoid unexpected outages that may lead a huge economic losses. Hence, this field has attracted a rising interest, in order to improve the accuracy of the fault diagnosis and to extend it to a growing number of different types of faults. Over the last decade, and recently, new diagnosis methods have being proposed in the technical literature which are solving some drawbacks of previous diagnostic methods and are improving the diagnostic procedure in terms of reliability, precision, and computational time, among others. In this paper, an optimization with support vector machine (SVM) of several diagnostic methods for accuracy improvement is presented, using a comparative study for finding the best and more optimized fault diagnosis system. A great amount of experimental tests have been developed to validate this comparative.

Portable network analyzer and mobile app based small wind turbine condition monitoring

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current’s spectrogram with a significant reduction of the required computational resources.

A new multilevel approach for Programmable Logical Controller (PLCs)

This paper introduces an algorithm with a very low computational complexity, which enables to detect rotor asymmetries in induction motors. The proposed approach is based on calculating the negative sequence component of the startup stator currents, and enables to visualize the characteristic pattern of the lower sideband component (LSH) during the startup transient. Unlike previous published approaches, the proposed method does not need to use either frequency domain transforms nor time-frequency transforms or complex mathematic functions. Thus it can be implemented as an embedded algorithm in preexisting electronic devices or in low cost devices such as DSPs or tablets. In this paper the method is theoretically justified and validated through simulation and laboratory tests.

Motor current signal analysis on programmable logic controller

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. Traditional and new diagnostic techniques mainly based, respectively, on vibration and generator current monitoring cannot be applied easily to small wind groups due to the very restrictive cost limitations. This paper proposes a fault diagnosis technique based on monitoring generator voltage, current and power suitable for small wind groups based on permanent magnet synchronous generators which aims to offer an affordable solution that provides easy to understand information even for users lacking technologic training, helping them decide when to ask for specialized assistance. This proposal is based on data provided by a commercial monitoring system installed between the generator and the regenerative inverter and processed and displayed in a mobile device (smartphone or tablet-PC) App running either Android or iOS, thus greatly contributing to a very cost-effective solution. The actual performance of an off-the-shelf small wind group has been measured both off-line and on-line (by the diagnosis App during the training stage) with good agreement and confirms the suitability of the method to detect demagnetization faults.

Micro Zero Padding for the Reduction of Spectral Leakage in the Diagnosis of Rotor Asymmetries Faults in Large Induction

Most of automation systems in industry are based on Programmable Logical Controllers (PLCs) due to its reliability and immunity to industrial environments. Besides, PLCss flexibility and capabilities have allowed human to automate a huge number of industrial processes. In this paper a new multilevel graphical and modular approach is proposed for programming industrial PLCss. Traditional programming drawbacks, such as impossibility of exchange code or the programmer dependence, are well-known. For solve these problems, the paradigm proposed in this paper exploits several object oriented features, improving the re-use of code, develops objects to be used in a plug and play way understandable by so many developers. Hence this framework reduce the PLC brand and programmer dependence.