FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors

Cabal-Yépez, Eduardo; Valtierra-Rodríguez, Martín; Romero-Troncoso, René de Jesús; García-Pérez, Arturo; Osornio-Ríos, Roque Alfredo; Miranda-Vidales, Homero; Álvarez-Salas, R.

doi:10.1016/j.ymssp.2012.01.021

Cited by 47 publications

(28 citation statements)

References 27 publications

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“…It relates the uncertainty of the signal or event associated with a given probability distribution. The concept of entropy has found broad applications in engineering, including, for instance, fault diagnoses [28][29][30][31]. A survey of recent methods for the fault diagnosis of rotating machinery using entropy techniques was discussed in [32].…”

Section: Background and Methodologymentioning

confidence: 99%

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

et al. 2017

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High-efficiency motors are being gradually introduced in many industrial applications because of their positive impacts on the environment by reducing energy consumption and CO 2 emission. In this respect, line start permanent magnet synchronous motors (LS-PMSMs) have been introduced recently. Due to their unique configuration, LS-PMSMs allow the obtaining of super premium efficiency levels, accompanied with a high torque and power factor. However, since the use of LS-PMSMs in the industry is in its infancy, no efficient scheme has yet been reported for broken rotor bar (BRB) fault detection in this type of motor. Accordingly, the main aim of this research is to investigate the fault-related feature for BRB faults on LS-PMSMs. In this regard, a simulation model and experimental setup for the investigation of BRB in LS-PMSM are implemented. The detection strategy for BRB in LS-PMSM proposed here is based on the monitoring of the start-up current signal and discrete wavelet transform. The entropy features are used as fault-related features for BRB faults. Finally, the ability of these features is validated for the detection of BRB in LS-PMSM through statistical analysis. In this research, the importance of the starting load is also considered for BRB detection in LS-PMSMs.

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Section: Background and Methodologymentioning

confidence: 99%

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

et al. 2017

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“…The blocks can work in parallel and can also be interconnected to build more complex operation blocks. These distinct features have made FPGA-based hardware systems very attractive for real-time signal processing applications [14][15][16] . The FPGA in this system employs three DMA channels to which data can be written concurrently, thereby allowing signal processing tasks to be carried out in real time.…”

Section: Configurable Data Acquisition Systemmentioning

confidence: 99%

“…Many of the most popular FPGAs are in-system programmable, allowing modification of the operation of the device for dedicated applications [20] . The target FPGA in this system is a Virtex-5 LX30 FPGA from Xilinx, working at an operating clock frequency of 90 MHz.…”

Section: Fpga Implementationmentioning

confidence: 99%

A condition monitoring system for an early warning of developing faults in wind turbine electrical systems

Ma¹,

Cross²,

Qian³

2016

insight

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“…With that, the error between the desired and calculated outputs is minimized. Finally, the entire training data is repeatedly presented to the FFNN until the overall error is acceptable [27]. On the other hand, the mathematical function that describes to each neuron shown in Figure 2(b) is given in Equation (6); it consists on the summation Σ(·) of the multiplications between the inputs x i and the associated multipliers commonly called weights ω i to each input plus a bias b ; then, this result is evaluated with a nonlinear function f (·) to provide the FFNN with the ability to model nonlinear relationships [27].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Hilbert Transform-Based Smart Sensor for Detection, Classification, and Quantification of Power Quality Disturbances

Granados-Lieberman

Valtierra-Rodríguez

Morales-Hernandez

et al. 2013

Sensors

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Power quality disturbance (PQD) monitoring has become an important issue due to the growing number of disturbing loads connected to the power line and to the susceptibility of certain loads to their presence. In any real power system, there are multiple sources of several disturbances which can have different magnitudes and appear at different times. In order to avoid equipment damage and estimate the damage severity, they have to be detected, classified, and quantified. In this work, a smart sensor for detection, classification, and quantification of PQD is proposed. First, the Hilbert transform (HT) is used as detection technique; then, the classification of the envelope of a PQD obtained through HT is carried out by a feed forward neural network (FFNN). Finally, the root mean square voltage (Vrms), peak voltage (Vpeak), crest factor (CF), and total harmonic distortion (THD) indices calculated through HT and Parseval's theorem as well as an instantaneous exponential time constant quantify the PQD according to the disturbance presented. The aforementioned methodology is processed online using digital hardware signal processing based on field programmable gate array (FPGA). Besides, the proposed smart sensor performance is validated and tested through synthetic signals and under real operating conditions, respectively.

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FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors

Cited by 47 publications

References 27 publications

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

A condition monitoring system for an early warning of developing faults in wind turbine electrical systems

A Hilbert Transform-Based Smart Sensor for Detection, Classification, and Quantification of Power Quality Disturbances

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