Arturo Garcia-Perez

Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation

Overall system performance on a production line is one of the major concerns in modern industry where induction motors are present and their condition monitoring is mandatory. Periodic offline monitoring of the motor condition is usually performed in the industry, consuming production time and increasing cost. Broken rotor bars are among the most common failures in induction motors. Reported research projects give a broken-rotor-bar-detection methodology based on personal-computer implementation that is performed offline and requires an expert technician interpretation which is not a cost-effective solution. The novelty of this paper is the development of an automatic online diagnosis algorithm for broken-rotor-bar detection, optimized for single low-cost field-programmable gate-array (FPGA) implementation, which guarantees the development of economical self-operated equipment. The proposed algorithm requires less computation load than the previously reported algorithms, and it is mainly based on the discrete-wavelet-transform application to the start-up current transient; a further single mean-square computation determines a weighting function that, according to its value, clearly points the motor condition as either healthy or damaged. In order to validate the proposed algorithm, several tests were performed, and an FPGA implementation was developed to show the algorithm feasibility for automatic online diagnosis.

The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors

Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified; however, they generally deal with a single fault only. Instead, in real induction machines, the case of multiple faults is common. When multiple faults exist, vibration and current are excited by several fault-related frequencies combined with each other, linearly or nonlinearly. Different techniques have been proposed for the diagnosis of rotating machinery in literature, where most of them are focused on detecting single faults and few works deal with the diagnosis and identification of multiple combined faults. The contribution of this paper is the development of a condition-monitoring strategy that can make accurate and reliable assessments of the presence of specific fault conditions in induction motors with single or multiple combined faults present. The proposed method combines a finite impulse response filter bank with high-resolution spectral analysis based on multiple signal classification for an accurate identification of the frequency-related fault. Results show the methodology potentiality as a deterministic detection technique that is suited for detecting multiple features where the fault-related frequencies are very close to those analytically reported in literature.

Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks

The detection and classification of power quality (PQ) disturbances have become a pressing concern due to the increasing number of disturbing loads connected to the power line and the susceptibility of certain loads to the presence of these disturbances; moreover, they can appear simultaneously since, in any real power system, there are multiple sources of different disturbances. In this paper, a new dual neural-network-based methodology to detect and classify single and combined PQ disturbances is proposed, consisting, on the one hand, of an adaptive linear network for harmonic and interharmonic estimation that allows computing the root-mean-square voltage and total harmonic distortion indices. With these indices, it is possible to detect and classify sags, swells, outages, and harmonics-interharmonics. On the other hand, a feedforward neural network for pattern recognition using the horizontal and vertical histograms of a specific voltage waveform can classify spikes, notching, flicker, and oscillatory transients. The combination of the aforementioned neural networks allows the detection and classification of all the aforementioned disturbances even when they appear simultaneously. An experiment under real operating conditions is carried out in order to test the proposed methodology.

FPGA-Based Online Detection of Multiple Combined Faults in Induction Motors Through Information Entropy and Fuzzy Inference

The development of monitoring systems for rotating machines is the ability to accurately detect different faults in an incipient state. The most popular rotating machine in industry is the squirrel-cage induction motor, and the failure on such motors may have severe consequences in costs, product quality, and safety. Most of the condition-monitoring techniques for induction motors focus on a single specific fault. The identification of two or more combined faults has been rarely considered, in spite of being a very usual situation in real rotary machines. On the other hand, information entropy is a signal processing technique that has recently proved its suitability for fault detection on induction motors, and fuzzy logic analysis has extensively been used in combination with several processing techniques in improving the diagnosis of a single isolated fault. The contribution of this paper is a novel methodology that is suitable for hardware implementation, which merges information entropy analysis with fuzzy logic inference to identify faults like bearing defects, unbalance, broken rotor bars, and combinations of faults by analyzing one phase of the induction motor steady-state current signal. The proposed methodology shows satisfactory results that prove its suitability for online detection of single and multiple combined faults in an automatic way through its hardware implementation in a field programmable gate array device.

MUSIC-ANN Analysis for Locating Structural Damages in a Truss-Type Structure by Means of Vibrations

This article will present a methodology for damage detection, location, and quantification based on vibration signature analysis and a comprehensive experimental study to assess the utility of the proposed structural health monitoring applied to a five-bay truss-type structure. The MUltiple SIgnal Classification (MUSIC) algorithm introduced first by Jiang and Adeli for health monitoring of structures in 2007 is fused with artificial neural networks (ANN) for an automated result. The developed methodology is based on feeding the amplitude of the natural frequencies as input of an artificial neural network, being the novelty of the proposed methodology its ability to identify, locate, and quantify the severity of damages with precision such as: external and internal corrosion and cracks in an automated monitoring process. Results show the proposed methodology is effective for detecting a healthy structure, a structure with external and internal corrosion, and a structure with crack. Therefore, the proposed fusion of MUSIC-ANN algorithms can be regarded as a simple, effective, and automated tool without requiring sophisticated equipment. The algorithms are moving toward establishing a practical and reliable structural health monitoring methodology, which will help in evaluating the condition of the structure in order to detect damages early and to make the corresponding maintenance decisions in the structures.

Reconfigurable Monitoring System for Time-Frequency Analysis on Industrial Equipment Through STFT and DWT

Nowadays industry pays much attention to prevent failures that may interrupt production with severe consequences in cost, product quality, and safety. The most-analyzed parameters for monitoring dynamic characteristics and ensuring correct functioning of systems are electric current, voltage, and vibrations. System-on-chip (SoC) design is an approach to increase performance and overcome costs during equipment monitoring. This work presents the design and implementation of a low-cost SoC design that utilizes reconfigurable hardware and a customized embedded processor for time-frequency analysis on industrial equipment through short-time Fourier transform and discrete wavelet transform. Three study cases (electric current supply to an induction motor during startup transient, voltage supply to an induction motor through a variable speed drive, and vibration signals from industrial-robot links) show the suitability of the proposed monitoring system for time-frequency analysis of different signals in distinct industrial applications, and early diagnosis and prognosis of abnormalities in monitored systems.

FPGA based embedded system for induction motor failure monitoring at the start-up transient vibrations with wavelets

Accurate monitoring on induction motors is mandatory for modern industry in order to guarantee the overall process quality. The common practice for monitoring is performed by third party enterprises that test the electrical machines with general-purpose instrumentation equipment, which do not allow on-line operation with the subsequent increase in production costs. Several methodologies have been proposed in recent years for detection of failures in induction motors; however, these methodologies perform the analysis offline. The contribution of this work is the development of an online monitoring of induction motor failures by measuring the vibration transient signals at the start-up with discrete wavelet transform, and its implementation as an embedded system with FPGA for SOC approach. Experimentation is realized to test the system functionality. From results it is demonstrated that the proposed methodology accurately determinates the motor condition in the presence of broken rotor bars.

Wavelet-based general methodology for multiple fault detection on induction motors at the startup vibration transient

Fault preventive monitoring on induction motors has risen in order to reduce maintenance costs and increase their life expectancy. There are many developments for detecting a single induction motor fault using several methodologies and techniques. Different methodologies have been developed for multiple fault detection having the disadvantage of giving a qualitative result requiring an expert technician for estimating the motor condition, with the possibility of inducing observation errors. This work proposes a quantitative general methodology for online induction motor monitoring and identification of multiple faults in an automatic way, and its hardware processing unit for real time applications, based on the startup vibration transient analysis. The proposed methodology is tested on three different cases of study: a motor with broken rotor bars, an unbalanced motor shaft, and a motor with misaligned load. The results show that the proposed methodology is highly reliable for detecting different faults in induction motors with a certainty of 99.7%. The developed approach can be extended for detecting other faults by a proper calibration, thanks to its generalized nature.

Novel Downsampling Empirical Mode Decomposition Approach for Power Quality Analysis

The development and application of techniques and methodologies for the analysis of power quality (PQ) signals that offer a more efficient and reliable analysis in terms of processing and performance are still issues for industrial and academic fields, mainly considering the quick growing of the PQ data in modern power systems. In this regard, an iterative downsampling stage fused to the empirical mode decomposition (EMD) method is proposed. It is validated and tested using synthetic and real signals. In general, the aim of the proposed method is to extract the fundamental component as the first intrinsic mode function (IMF) to simplify the remaining decomposition. The proposed method is compared to the classical EMD and the ensemble EMD (EEMD). Advantages of the proposed method include a more adequate IMF extraction than the EMD technique, and a noticeable reduction of computational burden compared to the EEMD. The results obtained from the synthetic and real signals demonstrate the reliability and efficiency of the proposed method.

Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.