Method for detecting faults in FFUs using SDP based on audio signal analysis

Shirai, Takuya; Shibata, Katsuhiko; Takahashi, Atsushi; Mori, Kazue; Kasashima, Nagayoshi; Ueno, Yoshio

doi:10.1109/etfa.1996.573299

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…The SDP was used for testing audio signal feature processing [28]. This method transformed the correlated results of sequence signals into a set of points to display the amplitude and frequency changes in a comprehensible image.…”

Section: Proposed Methodsmentioning

confidence: 99%

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

Lu,

Wu,

Sian

2023

IET Science Measure & Tech

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PV fault diagnosis remains difficult due to the non‐linear characteristic of PV output, which makes PV output to be likely disturbed by the ambient environment. This study proposes a novel convolutional extension neural network (CENN) algorithm, which is a jointed architecture based on convolutional neural network (CNN) and extension neural network (ENN), takes advantage of CNN and ENN. The CENN is combined with the symmetrized dot pattern (SDP) analysis method to diagnose the common eight PV array faults. The SDP is used to transform the measured PV signals into the point coordinate feature image; then, the CENN is trained to identify the different PV faults. Experimental results show an obvious improvement in short detection times and high accuracy compared with traditional CNN and the histogram of oriented gradient (HOG) extraction method with support vector machine (SVM), K‐nearest neighbours (KNN), and back propagation neural network (BPNN) classifiers, with 95.3%, 94%, 93.5%, and 93.3% accuracy, respectively. Using the proposed CENN, the accuracy can be raised to 97.3%. Additionally, the signals measured by various sensors are collected using programmable logic controller (PLC). The human–machine interface (HMI) and the proposed algorithm are developed using LabVIEW for graphical design. Finally, the information is transmitted to a tablet PC for performing real‐time remote monitoring.

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Section: Proposed Methodsmentioning

confidence: 99%

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

Lu,

Wu,

Sian

2023

IET Science Measure & Tech

View full text Add to dashboard Cite

show abstract

“…The SDP analysis is a new signal processing method for detecting audio signal features proposed in the early stages [18–21]. The SDP analysis uses comprehensible images to describe the time sequence signal amplitude and frequency changes.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…The SDP analysis is a new signal processing method for detecting audio signal features proposed in the early stages [18][19][20][21].…”

Section: Symmetrized Dot Pattern (Sdp)mentioning

confidence: 99%

A novel fault diagnosis method of power cable based on convolutional probabilistic neural network with discrete wavelet transform and symmetrized dot pattern

Sian

Kuo

et al. 2022

IET Science Measure & Tech

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To accurately diagnose the XLPE power cable insulation fault, this research proposed a novel hybrid algorithm combined with Convolutional Probabilistic Neural Network (CPNN) based on Discrete Wavelet Transform (DWT) and Symmetrized Dot Pattern (SDP) analysis. First, it built seven different power cable insulation defect models to measure partial discharge signals of power cable insulation faults. Then, a discrete wavelet transform was used for noise filtering. The time-domain partial discharge signal was directly converted into the point coordinate feature image of visual polar coordinates by SDP analyses. Finally, the feature image was trained and recognized by CPNN. After the important feature information of the feature-image was extracted by convolution layer and pooling layer operations, it is applied to the power cable insulation fault state diagnosis system based on the rapid learning and highly parallel computing of Probabilistic Neural Network (PNN). The experimental results proved that the method proposed in this research could accurately diagnose the power cable insulation fault type and the recognition accuracy is higher than 96%. The proposed method has a short detection time and high diagnostic accuracy. This proves that it can be applied to detect the power cable insulation fault type.

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Motor fault diagnosis based on multisensor-driven visual information fusion

Long,

Guo,

et al. 2024

ISA Transactions

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Method for detecting faults in FFUs using SDP based on audio signal analysis

Cited by 6 publications

References 2 publications

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

A novel fault diagnosis method of power cable based on convolutional probabilistic neural network with discrete wavelet transform and symmetrized dot pattern

Motor fault diagnosis based on multisensor-driven visual information fusion

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