Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses

Chaabi, Lilia; Lemzadmi, A.; Djebala, Abderrazek; Bouhalais, Mohamed Lamine; Ouelaa, Nouredine

doi:10.1007/s00170-020-05311-z

Cited by 29 publications

(17 citation statements)

References 32 publications

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“…In practice, as the times of charging/discharging cycles increases, the data obtained by the sensor also increases. Here, choose different prediction starting points arbitrarily, that is, the known data in the experiment are different, also take CALCE CS2-37 as examples for analysis and research, use the above-mentioned signal decomposition algorithm [27][28][29][30][31][32] to decompose the battery health indicator time series data, the decomposition results are shown in Figure 3. Figure 3b shows the charging time when it has been charged 700 cycles, and Figure 3c shows the discharge time with 500 cycles; the non-trend items Figure 3b shows the charging time when it has been charged 700 cycles, and Figure 3c shows the discharge time with 500 cycles; the non-trend items (combination of other IMFs) are shown in Figure 4.…”

Section: Results Of the Signal Decompositionmentioning

confidence: 99%

“…In the EEMD method, by adding noise to the original signal multiple times and then perform EMD decomposition separately. Next, average the obtained IMF to obtain the final component, and use multiple averaging operations to eliminate white noise [27,[30][31][32]. The steps of EEMD algorithm are as follows [29]:…”

Section: Decomposition Methods Of Emd Eemd and Ceemdanmentioning

confidence: 99%

“…In the EEMD method, the signal x i (n) containing different white noise is subjected to different decompositions in each experiment, and the residuals r i k (n) = r i k−1 (n) − IMF i k (n) of the signals are also different; as the average number increases, the error can be gradually reduced. Based on the EEMD method, by adding white noise at each stage, CEEMDAN can achieve a smaller reconstruction error at a smaller average number of times and solve the defect of an incomplete decomposition of EEMD [27,[29][30][31][32]. Define EJ(•) as the k-th mode generated by EMD and the k-th mode component produced by CEEMDAN as IMF k , then the algorithm flow of CEEMDAN [29] is as follows:…”

Section: Decomposition Methods Of Emd Eemd and Ceemdanmentioning

confidence: 99%

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State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach

et al. 2020

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Generally, the State-of-Health (SOH) monitoring and Remaining Useful Life (RUL) prediction and assessment of lithium-ion (Li-ion) batteries need to use sensors to obtain the degradation test data of the same type of batteries and establish the degradation model for reference. However, when the battery type is unknown, a usable reference model cannot be obtained, so its prediction and evaluation may be relatively inconvenient. In this paper, the State of-Health prediction for lithium-ion batteries based on a novel hybrid scheme is proposed. Firstly, historical charge/discharge time series and capacity series are extracted to analyze and construct Health Indicators, then using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the Health Indicator series into the trend and non-trend terms. Among them, the relatively smooth trend item data series uses the Autoregressive Integrated Moving Average model (ARIMA) for prediction; when dealing with the data series of non-trend items which are obviously non-smooth and seemingly random, the residuals predicted by ARIMA and the non-trend items obtained by CEEMDAN decomposition are combined into new non-trend items; then the least square support vector machine (LSSVM) is introduced to build a nonlinear prediction model and make predictions. Finally, combining the prediction results of the trend item data series and the non-trend item data series as a reference for the assessment of the state of health and remaining useful life. The 13 experimental results of 3 batteries verify the effectiveness of the scheme.

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Section: Results Of the Signal Decompositionmentioning

confidence: 99%

Section: Decomposition Methods Of Emd Eemd and Ceemdanmentioning

confidence: 99%

Section: Decomposition Methods Of Emd Eemd and Ceemdanmentioning

confidence: 99%

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State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach

et al. 2020

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“…In the process of bearing and gear fault diagnosis, the vibration signals of fault bearing and gear collected by sensors under different working conditions [31] will produce different vibration frequency bands, as shown in Figure 4, (a), (b) are bearing vibration signals, (c) and (d) are gear vibration signals.According to the theory of wavelet packet energy entropy [32,33], The difference of the fault amplitude of the vibration signal of different frequency bands will lead to the difference of the entropy value, thereby distinguishing the data of the same fault type under different working conditions. Therefore, in this paper, the original vibration signal is decomposed by wavelet packet energy entropy to get the feature vector, and then the fault diagnosis of bearing and gear under variable working conditions is carried out.…”

Section: Dynamic Learning Rate Dbn Based On Pso Adaptive Structumentioning

confidence: 99%

A Novel Rotating Machinery Fault Diagnosis Method Based on Adaptive Deep Belief Network Structure and Dynamic Learning Rate Under Variable Working Conditions

et al. 2021

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With the development of modern industries, the working environment of rotating machinery has become increasingly complicated. Therefore, it is very meaningful to accurately identify the type of equipment failure under variable operating conditions. This paper presents a rotating machinery fault diagnosis method based on dynamic learning rate deep belief network (DBN) with adaptive structure (PSO-DDBN). Firstly, the wavelet packet energy entropy principle was used to obtain the characteristic matrix of the original data, and then the characteristics of the data under variable conditions were distinguished. Secondly, in order to adjust the structure of DBN, the loss function of DBN was used to construct the convergence function in particle swarm optimization (PSO) adaptive process. The dynamic learning rate strategy was applied to the training process of the network. The network gradient value in each iteration was recorded and the dynamic learning rate function was constructed to achieve the purpose of dynamically adjusting the network learning rate and making the network convergence faster and more stable. Then, the performance of PSO-DDBN was verified by the data of bearing and gearbox under variable conditions. Finally, other intelligent diagnosis algorithms were compared with this method, and the results showed that this method had better universality and fault classification ability.

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“…Therefore, after acquiring the bearing signal feature matrix, it is necessary to be further extracted to remove the irrelevant and redundant features. At present, the methods to remove redundant and irrelevant features of bearing include auto-encoder [ 23 , 24 ], neural networks [ 25 , 26 ], Principal Component Analysis (PCA) [ 27 ], kernel PCA [ 28 ], and Singular Value Decomposition (SVD). However, although intelligent algorithms, such as self-encoding and neural networks, have been applied to diagnose bearing faults, they have disadvantages, such as low generalization, slow calculation speed, and higher requirements for hardware equipment.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion

Zhu

Wei

et al. 2021

Sensors

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Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.

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Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses

Cited by 29 publications

References 32 publications

State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach

State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach

A Novel Rotating Machinery Fault Diagnosis Method Based on Adaptive Deep Belief Network Structure and Dynamic Learning Rate Under Variable Working Conditions

Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion

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