Novel hardware processing unit for dynamic on-line entropy estimation of discrete time information

Cabal-Yépez, Eduardo; Romero-Troncoso, René de Jesús; García-Pérez, Arturo; Rodriguez-Donate, Carlos

doi:10.1016/j.dsp.2009.06.012

Cited by 15 publications

(7 citation statements)

References 16 publications

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“…where it is bounded by 0 ≤ ( ) ≤ log 2 ( ). Due to the number of applications and to the requirements of processing time, a hardware processing unit based on FPGA for entropy estimation is presented by [33], where a simplified mathematical expression is given as follows:…”

Section: Entropymentioning

confidence: 99%

“…The total number of tests for each condition is 20, giving a total of 160 tests; their entropy values are shown in Figure 5. These values are bounded by the mean ( ) and ±3 standard deviation ( ) in order to ensure an occurrence of 99.7% [33]; Table 1 presents the numerical values. The best -means clusters are obtained using and since they offer more nonoverlapped patterns.…”

Section: Design Stagementioning

confidence: 99%

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Shannon Entropy andK-Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals

Camarena-Martinez

Valtierra-Rodríguez

Amezquita-Sanchez

et al. 2016

Shock and Vibration

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For industry, the induction motors are essential elements in production chains. Despite the robustness of induction motors, they are susceptible to failures. The broken rotor bar (BRB) fault in induction motors has received special attention since one of its characteristics is that the motor can continue operating with apparent normality; however, at certain point the fault may cause severe damage to the motor. In this work, a methodology to detect BRBs using vibration signals is proposed. The methodology uses the Shannon entropy to quantify the amount of information provided by the vibration signals, which changes due to the presence of new frequency components associated with the fault. For automatic diagnosis, theK-means cluster algorithm and a decision-making unit that looks for the nearest cluster through the Euclidian distance are applied. Unlike other reported works, the proposal can diagnose the BRB condition during startup transient and steady state regimes of operation. Additionally, the proposal is also implemented into a field programmable gate array in order to offer a low-cost and low-complex online monitoring system. The obtained results demonstrate the proposal effectiveness to diagnose half, one, and two BRBs.

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Section: Entropymentioning

confidence: 99%

Section: Design Stagementioning

confidence: 99%

Shannon Entropy andK-Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals

Camarena-Martinez

Valtierra-Rodríguez

Amezquita-Sanchez

et al. 2016

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Recently, the information entropy has proved its suitability for condition monitoring, fault detection and risk assessment (Cabal et al, 2010;Zhang et al, 2010;Li et al, 2012;Ai et al, 2013;Yang et al, 2015;Nourani et al, 2015). For instance, faulted rotor bars were detected through the analysis of vibration signals based on information entropy (Cabal et al, 2010). The utility of multiscale entropy in diagnosing faulty bearings in a rotating machine was investigated (Zhang et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A Generalized Model for Wind Turbine Faulty Condition Detection Using Combination Prediction Approach and Information Entropy

Chen¹,

Chen²,

Li³

et al. 2018

J ENV INFORM

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A generalized model for detecting the incipient wind turbine (WT) faulty condition based on the data collected from wind farm supervisory control and data acquisition (SCADA) system is proposed in this paper. The linear combination prediction approach and the information entropy are integrated to develop the generalized model, in which the linear combination prediction approach improves the accuracy and generalization performance of the model, and the information entropy of prediction residual quantifies the abnormal level of the condition parameter. SCADA datasets were selected to establish the prediction models of WT condition parameters that are dependent on environmental conditions such as ambient temperature and wind speed. The combination prediction models of WT condition parameters were developed based on different data mining algorithms such as Back propagation neural network (BPNN) algorithm, radial basis function neural network (RBFNN) algorithm and least square support vector machine (LSSVM) algorithm. The information entropy was utilized to extract useful information from residuals of the prediction models for WT faulty condition detection. Finally, the proposed method has been used for real 1.5 MW WTs with doubly fed induction generators (DFIG). Through investigation of cases of actual WT faults, the effectiveness of the proposed WT imminent fault identification approach was verified.

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“…In fact, the development of new diagnosis methods and the definition of pertinent fault indicators are only the first steps toward reliable fault detection results; the next step should be the elaboration of a complete automatic algorithm that exploits these techniques and indicators. Recently, intensive research efforts are focusing on development and implementation of fault-diagnosis algorithms [3,9,[18][19][20]26], the main object of all these researches is to allow even relatively unskilled users to make an automatic, fast and reliable decision on machine health with low requirements. In this context, the present paper proposes an improved and robust algorithm for the detection of rotor faults, such as broken rotor bars, in induction machines.…”

Section: Introductionmentioning

confidence: 99%

Rotor fault detection algorithm based on a new fault indicator for induction motors fed from an industrial frequency inverter

Sahraoui

Ghoggal

Zouzou

et al. 2015

International Journal of Applied Electromagnetics and Mechanics

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This paper proposes an improved rotor fault detection algorithm. The main objects are: First, ameliorate the reliability of the rotor fault detection task especially under very low load in the presence of industrial frequency inverter. Second, allow even relatively unskilled users to make an automatic, fast and reliable decision on machine health with low requirements. Basing on the concept of multiple signature processing, the proposed algorithm uses Relative Harmonic Indexes (RHI) as a new fault indicator. The RHI are computed starting from many harmonics selected carefully according to their sensitivity to the occurrence of rotor bar faults, this represents its main advantage compared to the classical fault indicators which use only one or two fault signatures leading commonly to erroneous diagnosis especially under low load. The proposed algorithm requires only the stator current signal. For each data acquisition, the algorithm computes, normalises and classifies the RHI then it displays the obtained results in a simple manner. Extensive experimental tests, carried out on a 3 kW squirrel cage induction motor fed from an industrial frequency inverter, confirm the effectiveness of the proposed algorithm in detecting of rotor bar fault under different operation conditions even with very low loads.

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Novel hardware processing unit for dynamic on-line entropy estimation of discrete time information

Cited by 15 publications

References 16 publications

Shannon Entropy andK-Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals

Shannon Entropy andK-Means Method for Automatic Diagnosis of Broken Rotor Bars in Induction Motors Using Vibration Signals

A Generalized Model for Wind Turbine Faulty Condition Detection Using Combination Prediction Approach and Information Entropy

Rotor fault detection algorithm based on a new fault indicator for induction motors fed from an industrial frequency inverter

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