A fault diagnosis method for electric vehicle power lithium battery based on wavelet packet decomposition

Jiang, Jiuchun; Zhang, Ruhang; Wu, Yongbo; Chang, Chun; Jiang, Yan

doi:10.1016/j.est.2022.105909

Cited by 26 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In wavelet packet transform, wavelet basis functions are very important for feature extraction. The common wavelet basis functions are Meyer wavelet, db wavelet, and Sym wavelet [45]. Meyer wavelet converges fast but does not conform to the tight support theory.…”

Section: Echo Signal Feature Extractionmentioning

confidence: 99%

“…Using the selected wavelet basis function, it is decomposed into two sub-nodes, one for the high frequency component and one for the low frequency component. In this paper all subnodes continue to be decomposed to get more detailed information about the low and high frequencies [45]. The above decomposition steps are repeated until the desired number of decomposition layers is reached.…”

Section: Echo Signal Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Liu,

Luo,

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

Real-time monitoring of wellbore status information can effectively ensure the structural safety of the wellbore and improve the drilling efficiency. It is especially important to recognize the wellbore fractures and identify their parameters, which motivates us to propose a wellbore fracture recognition and parameter identification method using piezoelectric ultrasonic and machine learning. To realize a Self-model Emission Detection (SED), we innovatively utilize a single transducer to act as both an actuator and a sensor, allowing for the efficient acquisition of ultrasonic echo signals of the wellbore. For fracture recognition, we use the wavelet packet transform to extract features from the ultrasonic echo signal, while constructing a Convolutional Neural Network (CNN) model for fracture recognition. Then, we establish the relationships between the fracture width-depth parameter and the echo signal, including the peak value as well as the arrival time difference. The experimental results show that the proposed method effectively recognizes the fractures from the ultrasonic echo signal of the wellbore. At the same time, the established function truly reflects the relationship between the fracture parameters and the echo signal. Therefore, the proposed method can provide an identification function for quantitative monitoring of wellbore fracture parameters. Moreover, the functions can be used as a reference for other structural health monitoring, which has good application prospects.

show abstract

Section: Echo Signal Feature Extractionmentioning

confidence: 99%

Section: Echo Signal Feature Extractionmentioning

confidence: 99%

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Liu,

Luo,

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…The DLA calculation process in Figure 5 is as follows: 1) Perform a four‐level WPD [27] on the collected zero‐sequence voltage and zero‐sequence current signals of the power distribution network after a single‐phase grounding fault. 2) Compute the energy of each frequency band based on the WPD coefficients of the fourth level, as shown in Equation (15).

\begin{equation}E_n^4 = \sum_{k \in Z} {{{(w_{n,k}^4)}^2}} \end{equation}

…”

Section: Transient Dielectric Loss Angle Calculation Based On Wavelet...mentioning

confidence: 99%

“…1) Perform a four-level WPD [27] on the collected zerosequence voltage and zero-sequence current signals of the power distribution network after a single-phase grounding fault. 2) Compute the energy of each frequency band based on the WPD coefficients of the fourth level, as shown in Equation ( 15).…”

Section: Transient Dielectric Loss Angle Calculationmentioning

confidence: 99%

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Lu,

Hou,

2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

When a grounding fault occurs in the distribution network with distributed generation, the network topology becomes intricate, making it challenging to extract fault characteristics, resulting in a decrease in the precision of fault discrimination. To address this issue, a graph isomorphism network (GIN) approach based on the parameterized adaptive graph learning (AdapGL) module is proposed, transforming the distribution network fault selection problem into a graph classification task. First, the adjacency matrix of the distribution network's topology graph is initialized. This matrix, combined with feature vectors from the robust local mean decomposition energy entropy and transient dielectric loss angle of line, will be input into the GIN. Then, the AdapGL module is integrated into the GIN, dynamically learning and updating the one‐way relationships between actual network nodes to complete the graph classification task. Finally, a radial distribution network model (RDNM) and an improved IEEE 34 nodes model are established, and the fault selection results of the AdapGL‐GIN method are compared with those of other methods. The results indicate that the proposed method achieves higher accuracy than other methods, demonstrating significant practical importance in engineering applications.

show abstract

“…4 Jiang et al proposed a fault diagnosis method for electric vehicle power lithium batteries based on wavelet packet decomposition. 22 Compared to traditional power spectrum analysis or autocorrelation function analysis, BA had better nonlinear analysis ability and could detect the nonlinear characteristics and secondary phase coupling frequency of signals. Zhou et al applied a horizontal slice of cyclic bispectrum in rolling element bearings fault diagnosis and obtained a relatively good effect.…”

Section: Introductionmentioning

confidence: 99%

Abnormal noise identification of engines based on wavelet packet transform and bispectrum analysis

Yang,

Zhang,

2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Abnormal noise is the most prominent problem for motorcycles and affects the consumers’ purchasing desire and driving experience and the enterprise’s competitiveness. Usually, the noise from a newly assembled engine is detected by manual auscultation (MA) to determine if the engine is operating normally. However, MA is also affected by subjective and objective factors with severe labor intensity, and its accuracy greatly fluctuates. Importantly, MA cannot be applied in a corporation with mass production and high-quality requirements. To improve the efficiency and accuracy of motorcycle engine quality inspection and achieve intelligent production, an online engine abnormal noise detection method was proposed based on wavelet packet transform (WPT) and bispectrum analysis (BA); this method improved the accuracy and stability of the identification of the abnormal noise engine and reduced the cost of the check. First, the acoustic signal of the engine of the motorcycle was acquired by using a free-field microphone. Second, the background noise of signals was eliminated by using the wavelet correlation coefficient (WCC) theory, and the signal features were extracted by applying WPT and BA. Third, the feature vectors were normalized before being used as support vector machine (SVM) samples. Fourth, an appropriate kernel function and parameters were selected to train the vector machine using the training sets. Finally, the testing sets were used to inspect the accuracy of the vector machines. The result showed that the training accuracy is 95% and the testing accuracy is 97.5 of the samples were suitable by using the method of wavelet packet transform-bispectrum analysis-support vector machines (WPT-BA-SVM). WPT-BA-SVM effectively identified engine fault types and provided the theoretical foundation for the establishment of an engine abnormal noise online detection system.

show abstract

A fault diagnosis method for electric vehicle power lithium battery based on wavelet packet decomposition

Cited by 26 publications

References 26 publications

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Abnormal noise identification of engines based on wavelet packet transform and bispectrum analysis

Contact Info

Product

Resources

About