Support Vector Machine for Misalignment Fault Classification Under Different Loading Conditions Using Vibro-Acoustic Sensor Data Fusion

Patil, Shital; Jalan, A. K.; Marathe, Amol

doi:10.1007/s40799-021-00533-6

Cited by 13 publications

(3 citation statements)

References 25 publications

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“…A transmission line fault classification technique based on support vector machines (SVM) with different types of training models was proposed by [22]. This method performs effectively in fault classification when examined on a transmission line.…”

Section: B Literature Reviewmentioning

confidence: 99%

Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM

Alhanaf,

Farsadi,

Balik

2024

IEEE Access

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A modern electric power system integrated with advanced technologies such as sensors and smart meters is referred to as a "smart grid," aimed at enhancing electrical power delivery efficiency and reliability. However, fault location and prediction can become challenging when dynamic fault currents from renewable energy sources are present.To address these challenges, three unique deep learning models that make use of Deep Neural Networks (DNN) have been proposed. CNN, LSTM, and Hybrid CNN-LSTM are deep learning models. Line faulty identification (LF), fault classification (FC), and fault location estimate (FL) are the subjects on which they concentrate. These models analyze data gathered both pre and post faults occur in order to enhance decision making. Signals including the voltage and current were fed into these models from many different locations across the test networks. Once the 1D CNN has extracted characteristics from the gathered signals, LSTM uses these features to make accurate estimations and identify faults. Complex data are compatible with this method in terms of optimal outcomes. Using training and testing data from transmission line failure simulations, the proposed approaches were evaluated on the IEEE 6-bus and IEEE 9-bus systems. The tests encompassed a range of fault classes, locations, and ground fault resistances at various locations. Distributed Generator (DG) resources were additionally included in the system architecture and changes in the topology of the networks were considered in terms of location and number of DG resources .The results demonstrated that the proposed algorithms outperformed contemporary technologies in terms of detection, classification, and location accuracy. They demonstrated high accuracy and robustness in their performance.

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Section: B Literature Reviewmentioning

confidence: 99%

Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM

Alhanaf,

Farsadi,

Balik

2024

IEEE Access

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“…Since the sign of parallel and angular misalignment in a rotor system is the same, that is, 2X frequency harmonic, Patil et al [47] used the SVM model to discriminate between these two conditions. Time domain features were extracted from the vibroacoustic signals, e.g., the fusion form of the vibration and acoustic signals.…”

Section: Introductionmentioning

confidence: 99%

Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM

et al. 2023

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A growing interest in intelligent fault detection may sometimes lead to practical issues when existing malfunctions reveal analogous indications and the number of observations is limited. This article addresses the classification problem of two identical malfunctions, i.e., unbalancing and shaft bow in rotary machines, where only 56 observations were utilized for the training. The faulty systems are modeled in ABAQUS/CAE; a data set for each fault is created by simulation under various physical and operational conditions employing the uncertainty concept. The wavelet time scattering (WTS) technique extracts low-variance presentations from signals. With respect to the classification procedure of the faulted rotor systems, two models are examined with the extracted features from WTS as the input. Initially, a long short-term memory (LSTM) network is trained and tested, and then, the capability of a support vector machine (SVM) model is inquired. Ultimately, the classification models are trained and tested using the raw time series data and the extracted features to compare the effectiveness of the suggested methods, i.e., WTS. The employed approach for feature extraction demonstrated remarkable effectiveness in addressing a potential hurdle in identifying faults in rotating systems: the ability to differentiate between unbalanced and bowed rotors, irrespective of the classification model utilized.

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“…In the diagnostic field of other rotating equipment, Gunerkar 10,11 proposed a high-level sensor fusion to gain useful vibro-acoustic data for fault classification of bearings. Patil 12,13 adopted a vibro-acoustic sensor data fusion technique to classify various forms of misalignment under different operating conditions. Vibro-acoustic fusion under multisource heterogeneous information can effectively solve the problem of incomplete feature information.…”

Section: Introductionmentioning

confidence: 99%

Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion

Shen

Song

et al. 2023

Structural Health Monitoring

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On the condition of variable speeds, the blade crack signal generated by the single-mode sensor is an inherent risk of being misled by noise. Besides, it is challenging to reach the required integrity of information through feature extraction in single analysis domain, leading to the loss of detection accuracy for blade cracks. Therefore, a blade crack detection method based on domain adaptation and autoencoder (AE) of multidimensional vibro-acoustic feature fusion is proposed to extract more complete fault information from multi-sensor and multi-analysis domains. This method extracts one-dimensional (1D) and two-dimensional (2D) features from the frequency domain and smoothed pseudo-wigner-ville distribution (SPWVD) spectrum of the vibro-acoustic signal. Firstly, the 1D and 2D features are extracted without supervision through Stacked Denoising Autoencoder (SDAE) and Convolution Denoising Autoencoder (CDAE), respectively. Then, the extracted multidimensional features are fused and classified through the fully connected network. Finally, for insufficient diagnosis accuracy and data imbalance under certain working condition models with variable speed and combined with inductive transfer mechanism and domain adaptation, the stable diagnosis model under specific speed is applied for transferring and learning the unsupervised networks of other models compensating the weak generalization ability and robustness of the model. The experimental results show that this method is characterized by higher accuracy and robustness compared with the diagnosis method of single-domain feature for the single-mode sensor. Moreover, the proposed migration mechanism can well solve the data imbalance under different working conditions and improve the generalization ability of the few-shot model.

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Support Vector Machine for Misalignment Fault Classification Under Different Loading Conditions Using Vibro-Acoustic Sensor Data Fusion

Cited by 13 publications

References 25 publications

Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM

Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM

Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM

Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion

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