This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review

The health condition of rolling bearing possesses a significant impact on the safety and efficiency of rotating machinery. Accordingly, to diagnose the faults in rolling bearings effectively and accurately, a novel hybrid approach coupling variational mode decomposition (VMD), composite multiscale fine-sorted dispersion entropy (CMFSDE) and support vector machine (SVM) optimized by mutation sine cosine algorithm and Harris hawks optimization (MSCAHHO) is proposed in the paper. Firstly, VMD is employed to decompose raw vibration signals with various fault types into different sets of intrinsic mode functions (IMFs) to weaken the non-stationarity of signals, before which the parameter $K$ of VMD is decided through central frequency observation method. Subsequently, CMFSDE is put forward in this paper to analyze the complexity of fault signals by fully considering the relationship between neighboring elements based on composite multiscale technique, with which the representative features of different fault samples are extracted to construct feature vectors. Later, an enhanced hybrid optimization approach called MSCAHHO is proposed by integrating sine cosine algorithm (SCA) and a periodic mutation strategy to improve Harris hawks optimization (HHO). Then, MSCAHHO is employed to optimize the parameters of SVM, after which the optimal SVM model is utilized for fault classification. Finally, the performance of the proposed methodology is evaluated with four validity indices through comparative experiments. The experimental results reveal that the proposed VMD-CMFSDE-MSCAHHO-SVM method achieves favorable diagnosis results comparing with other relevant methods.

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Fault Diagnosis for Rolling Bearings Based on Composite Multiscale Fine-Sorted Dispersion Entropy and SVM With Hybrid Mutation SCA-HHO Algorithm Optimization

A new denoising algorithm and feature extraction algorithm that combine a new kind of permutation entropy (NPE) and variational mode decomposition (VMD) are put forward in this paper. VMD is a new self-adaptive signal processing algorithm, which is more robust to sampling and noise, and also can overcome the problem of mode mixing in empirical mode decomposition (EMD) and ensemble EMD (EEMD). Permutation entropy (PE), as a nonlinear dynamics parameter, is a powerful tool that can describe the complexity of a time series. NPE, a new version of PE, is interpreted as distance to white noise, which shows a reverse trend to PE and has better stability than PE. In this paper, three kinds of ship-radiated noise (SN) signal are decomposed by VMD algorithm, and a series of intrinsic mode functions (IMF) are obtained. The NPEs of all the IMFs are calculated, the noise IMFs are screened out according to the value of NPE, and the process of denoising can be realized by reconstructing the rest of IMFs. Then the reconstructed SN signal is decomposed by VMD algorithm again, and one IMF containing the most dominant information is chosen to represent the original SN signal. Finally, NPE of the chosen IMF is calculated as a new complexity feature, which constitutes the input of the support vector machine (SVM) for pattern recognition of SN. Compared with the existing denoising algorithms and feature extraction algorithms, the effectiveness of proposed algorithms is validated using the numerical simulation signal and the different kinds of SN signal.

https://www.mdpi.com/2073-8994/9/11/256/pdf

Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise

This study presents the case study of a substation in-service power transformer referred to the on-site partial discharge (PD) detection and evaluation methods. An original methodology for simultaneous application of three methods is proposed: electrical, acoustic and ultra-high frequency. Transformer is powered by the power grid and no external generator is required according to the proposed methodology. Furthermore, several possibilities of applying these concurrent measurements and benefits of such solution in terms of result interpretation, interference resistance and on-site measurement applicability are indicated. The proposed methodology allows for a fast, accurate and secure PD diagnostics in a power transformer under on-site operating conditions. In the described example, a failure as a surface PD in the vicinity of the first phase of high voltage winding output is detected. The research results provide the basis for further works on a diagnostic expert system, which exploits automatic detection and evaluation of the PD phenomenon in power transformers under normal operating conditions.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-gtd.2017.1551

Measurements on partial discharge in on-site operating power transformer: a case study

Considering the advantages of contactless fault diagnosis, a novel sound based fault diagnosis method for railway point machines (RPMs) is proposed. Firstly, a denoising method based on empirical mode decomposition (EMD) is proposed. The useful intrinsic mode functions (IMFs) are selected using kurtosis and energy criteria to reconstruct the denoised signal. Then, multi-scale fractional permutation entropy (MFPE) is proposed inspired by fractional calculus, which is more powerful than the classical multi-scale permutation entropy (MPE). And a two-scale algorithm is developed to avoid neglecting the information contained at the end of the signals in the coarse-graining process. Finally, the feasibility and superiority of the proposed method (D-FMPE-T) based on denoising method and two-scale algorithm are verified using support vector machine optimized by particle swarm optimization (PSO-SVM) through comparing with some commonly used feature extraction and classification methods. Besides, CWRU is also utilized for verification of the superiority of the proposed method. Experiment results show that the proposed method performs best. The identification accuracies on normal-reverse and reverse-normal switching processes, and CWRU data sets reach 99.3%, 99%, and 99.17% respectively, demonstrating the feasibility and effectiveness of the proposed method.

Sound Based Fault Diagnosis for RPMs Based on Multi-Scale Fractional Permutation Entropy and Two-Scale Algorithm

Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy.

Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

https://www.mdpi.com/1996-1073/12/20/4017/pdf

A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Partial discharge (PD) fault analysis is an important tool for insulation condition diagnosis of electrical equipment. In order to conquer the limitations of traditional PD fault diagnosis, a novel feature extraction approach based on variational mode decomposition (VMD) and multi-scale dispersion entropy (MDE) is proposed. Besides, a hypersphere multiclass support vector machine (HMSVM) is used for PD pattern recognition with extracted PD features. Firstly, the original PD signal is decomposed with VMD to obtain intrinsic mode functions (IMFs). Secondly proper IMFs are selected according to central frequency observation and MDE values in each IMF are calculated. And then principal component analysis (PCA) is introduced to extract effective principle components in MDE. Finally, the extracted principle factors are used as PD features and sent to HMSVM classifier. Experiment results demonstrate that, PD feature extraction method based on VMD-MDE can extract effective characteristic parameters that representing dominant PD features. Recognition results verify the effectiveness and superiority of the proposed PD fault diagnosis method.

https://www.mdpi.com/1099-4300/21/1/81/pdf

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy.

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

Haikun Shang

Papers

Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy

A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy.

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy.