Relevant bibliographies by topics / Lempel-Ziv complexity method

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Academic literature on the topic 'Lempel-Ziv complexity method'

Author: Grafiati
Published: 5 June 2025
Last updated: 11 July 2025

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Journal articles on the topic "Lempel-Ziv complexity method"

1

Du, Jianxi, Lingli Cui, Jianyu Zhang, Jin Li, and Jinfeng Huang. "The Method of Quantitative Trend Diagnosis of Rolling Bearing Fault Based on Protrugram and Lempel–Ziv." Shock and Vibration 2018 (November 1, 2018): 1–8. http://dx.doi.org/10.1155/2018/4303109.

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This paper proposes a new method to realize the quantitative trend diagnosis of bearings based on Protrugram and Lempel–Ziv. Firstly, the fault features of original fault signals of bearing inner and outer race with different severity are extracted using Protrugram algorithm, and the optimal analysis frequency band is selected which reflects the fault characteristic. Then, the Lempel–Ziv complexity of the frequency band is calculated. Finally, the relationship between Lempel–Ziv complexity and fault size is obtained. Analysis results show that the severity of fault is proportional to the Lempel–Ziv complexity index value under different fault types. The Lempel–Ziv complexity showed different trend rules, respectively, in the inner and outer race, which realized the quantitative trend diagnosis of bearing faults.
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2

Han, Bing, Shun Wang, Qingqi Zhu, Xiaohui Yang, and Yongbo Li. "Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity." Applied Sciences 10, no. 12 (2020): 4221. http://dx.doi.org/10.3390/app10124221.

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The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.
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3

Tang, Youfu, Feng Lin, and Qian Zou. "Complexity Analysis of Time-Frequency Features for Vibration Signals of Rolling Bearings Based on Local Frequency." Shock and Vibration 2019 (July 10, 2019): 1–13. http://dx.doi.org/10.1155/2019/7190568.

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The multisource impact signal of rolling bearings often represents nonlinear and nonstationary characteristics, and quantitative description of the complexity of the signal with traditional spectrum analysis methods is difficult to be obtained. In this study, firstly, a novel concept of local frequency is defined to develop the limitation of traditional frequency. Then, an adaptive waveform decomposition method is proposed to extract the time-frequency features of nonstationary signals with multicomponents. Finally, the normalized Lempel–Ziv complexity method is applied to quantitatively measure the time-frequency features of vibration signals of rolling bearings. The results indicate that the time-frequency features extracted by the proposed method have clear physical meanings and can accurately distinguish the different fault states of rolling bearings. Furthermore, the normalized Lempel–Ziv complexity method can quantitatively measure the nonlinearity of the multisource impact signal. So, it supplies an effective basis for fault diagnosis of rolling bearings.
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4

Zhao, Huan, Gangjin Wang, Cheng Xu, and Fei Yu. "Voice activity detection method based on multivalued coarse-graining Lempel-Ziv complexity." Computer Science and Information Systems 8, no. 3 (2011): 869–88. http://dx.doi.org/10.2298/csis100906032z.

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One of the key issues in practical speech processing is to locate precisely endpoints of the input utterance to be free of nonspeech regions. Although lots of studies have been performed to solve this problem, the operation of existing voice activity detection (VAD) algorithms is still far away from ideal. This paper proposes a novel robust feature for VAD method that is based on multi-valued coarsegraining Lempel-Ziv Complexity (MLZC), which is an improved algorithm of the binary coarse-graining Lempel-Ziv Complexity (BLZC). In addition, we use fuzzy c-Means clustering algorithm and the Bayesian information criterion algorithm to estimate the thresholds of the MLZC characteristic, and adopt the dual-thresholds method for VAD. Experimental results on the TIMIT continuous speech database show that at low SNR environments, the detection performance of the proposed MLZC method is superior to the VAD in GSM ARM, G.729 and BLZC method.
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5

Wang, Haobo, Tongguang Yang, Qingkai Han, and Zhong Luo. "Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel–Ziv Complexity under Varying Conditions." Sensors 23, no. 8 (2023): 4044. http://dx.doi.org/10.3390/s23084044.

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The quantitative diagnosis of rolling bearings is essential to automating maintenance decisions. Over recent years, Lempel–Ziv complexity (LZC) has been widely used for the quantitative assessment of mechanical failures as one of the most valuable indicators for detecting dynamic changes in nonlinear signals. However, LZC focuses on the binary conversion of 0–1 code, which can easily lose some effective information about the time series and cannot fully mine the fault characteristics. Additionally, the immunity of LZC to noise cannot be insured, and it is difficult to quantitatively characterize the fault signal under strong background noise. To overcome these limitations, a quantitative bearing fault diagnosis method based on the optimized Variational Modal Decomposition Lempel–Ziv complexity (VMD-LZC) was developed to fully extract the vibration characteristics and to quantitatively characterize the bearing faults under variable operating conditions. First, to compensate for the deficiency that the main parameters of the variational modal decomposition (VMD) have to be selected by human experience, a genetic algorithm (GA) is used to optimize the parameters of the VMD and adaptively determine the optimal parameters [k, α] of the bearing fault signal. Furthermore, the IMF components that contain the maximum fault information are selected for signal reconstruction based on the Kurtosis theory. The Lempel–Ziv index of the reconstructed signal is calculated and then weighted and summed to obtain the Lempel–Ziv composite index. The experimental results show that the proposed method is of high application value for the quantitative assessment and classification of bearing faults in turbine rolling bearings under various operating conditions such as mild and severe crack faults and variable loads.
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6

Xiao, Leilei. "A New Feature Extraction Method of Marine Ambient Noise Based on Multiscale Dispersion Entropy." Mathematical Problems in Engineering 2022 (October 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/7618380.

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Marine ambient noise (AN) is a nonlinear and unstable signal, traditional dispersion entropy can only analyze the marine AN from a single scale, which is easy to cause the loss of information. To address this problem, we introduced multiscale dispersion entropy (MDE), and then a new feature extraction method of marine ambient noise based on MDE is proposed. We used MDE, multiscale permutation entropy (MPE), multiscale permutation Lempel–Ziv complexity (MPLZC), and multi-scale dispersion Lempel–Ziv complexity (MDLZC) to carry out feature extraction and classification recognition experiments for six ANs. The experimental results show that for the feature extraction methods based on MDE, MPE, MDLZC, and MPLZC, with the increase of the number of features, the feature extraction effect becomes better, and the average recognition rate (ARR) becomes higher; compared with other three feature extraction methods, the feature extraction method based on MDE has the best feature extraction effect and the highest ARR for the six ANs under the same feature number.
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7

AHMED, SULTAN UDDIN, MD SHAHJAHAN, and KAZUYUKI MURASE. "A LEMPEL-ZIV COMPLEXITY-BASED NEURAL NETWORK PRUNING ALGORITHM." International Journal of Neural Systems 21, no. 05 (2011): 427–41. http://dx.doi.org/10.1142/s0129065711002936.

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This paper presents a pruning method for artificial neural networks (ANNs) based on the 'Lempel-Ziv complexity' (LZC) measure. We call this method the 'silent pruning algorithm' (SPA). The term 'silent' is used in the sense that SPA prunes ANNs without causing much disturbance during the network training. SPA prunes hidden units during the training process according to their ranks computed from LZC. LZC extracts the number of unique patterns in a time sequence obtained from the output of a hidden unit and a smaller value of LZC indicates higher redundancy of a hidden unit. SPA has a great resemblance to biological brains since it encourages higher complexity during the training process. SPA is similar to, yet different from, existing pruning algorithms. The algorithm has been tested on a number of challenging benchmark problems in machine learning, including cancer, diabetes, heart, card, iris, glass, thyroid, and hepatitis problems. We compared SPA with other pruning algorithms and we found that SPA is better than the 'random deletion algorithm' (RDA) which prunes hidden units randomly. Our experimental results show that SPA can simplify ANNs with good generalization ability.
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8

Şener, Somay Kübra, and Emine Doğru Bolat. "Sleep-Apnea Detection with the Lempel-Ziv Complexity Analysis of the Electrocardiogram and Respiratory Signals." Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences 9, no. 25 (2022): 109–20. https://doi.org/10.5281/zenodo.7474702.

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Sleep apnea is a common and life-threatening disease. Diagnosis of the disease is as important as its treatment. A remarkable increase is observed in the number of diagnosed patients with the increase in public awareness and the increase in the rate of being noticed by physicians. Polysomnography measurements used in the diagnosis of sleep apnea disturb the patient and require more than one physiological data collection. Due to such problems, new analysis methods are being investigated. Since Lempel-Ziv is a fast and non-linear signal processing method, it is very suitable for processing physiological data. By using the Lempel-Ziv complexity method, it is aimed to diagnose the disease with less time and less data. In line with this goal, the treatment process will also be brought forward. Disease detection studies were carried out by using ECG and respiratory data from the Physionet.org database. As a result of the analyzes, it was observed that there was a significant difference in the time intervals with apnea from the ECG, chest respiration (Resp C) and abdominal respiration (Resp A) data. With this method, sleep apnea can be diagnosed for EKG, Resp C and Resp A.
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9

Yan, Xiaoan, Daoming She, Yadong Xu, and Minping Jia. "Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults." Entropy 23, no. 11 (2021): 1372. http://dx.doi.org/10.3390/e23111372.

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Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).
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10

Amigó, José M., Janusz Szczepański, Elek Wajnryb, and Maria V. Sanchez-Vives. "Estimating the Entropy Rate of Spike Trains via Lempel-Ziv Complexity." Neural Computation 16, no. 4 (2004): 717–36. http://dx.doi.org/10.1162/089976604322860677.

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Normalized Lempel-Ziv complexity, which measures the generation rate of new patterns along a digital sequence, is closely related to such important source properties as entropy and compression ratio, but, in contrast to these, it is a property of individual sequences. In this article, we propose to exploit this concept to estimate (or, at least, to bound from below) the entropy of neural discharges (spike trains). The main advantages of this method include fast convergence of the estimator (as supported by numerical simulation) and the fact that there is no need to know the probability law of the process generating the signal. Furthermore, we present numerical and experimental comparisons of the new method against the standard method based on word frequencies, providing evidence that this new approach is an alternative entropy estimator for binned spike trains.
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Book chapters on the topic "Lempel-Ziv complexity method"

1

Xia, Deling, Yuetian Li, Qingfang Meng, and Jie He. "A Method Using the Lempel-Ziv Complexity to Detect Ventricular Tachycardia and Fibrillation." In Advances in Neural Networks - ISNN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_19.

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