An alternative demodulation method using envelope-derivative operator for bearing fault diagnosis of the vibrating screen

Cai, Zongyan; Xu, Yuanbo; Zhang, Duan

doi:10.1177/1077546317739117

Cited by 31 publications

(16 citation statements)

References 21 publications

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“…Therefore, in fact, we have two mentioned phenomena: varying load (due to varying amount of ore to classify and unbalance of the shaft) and impulsive load considered to be impulsive background noise. As discussed in research works published already [ 11 , 12 ], for this class of machines the problem of local damage detection is challenging and may be defined as the detection of cyclic impulsive signal in presence of non-cyclic impulsive noise [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study

Gąsior

Urbańska

Grzesiek

et al. 2020

Sensors

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Condition monitoring is a well-established field of research; however, for industrial applications, one may find some challenges. They are mostly related to complex design, a specific process performed by the machine, time-varying load/speed conditions, and the presence of non-Gaussian noise. A procedure for vibration analysis from the sieving screen used in the raw material industry is proposed in the paper. It is more for pre-processing than the damage detection procedure. The idea presented here is related to identification and extraction of two main types of components: (i) deterministic (D)—related to the unbalanced shaft(s) and (ii) high amplitude, impulsive component randomly (R) appeared in the vibration due to pieces of ore falling down of moving along the deck. If we could identify these components, then we will be able to perform classical diagnostic procedures for local damage detection in rolling element bearing. As deterministic component may be AM/FM modulated and each impulse may appear with different amplitude and damping, there is a need for an automatic procedure. We propose a method for signal processing that covers two main steps: (a) related to R/D decomposition and including signal segmentation to neglect AM/FM modulations, iterative sine wave fitting using the least square method (for each segment), signal filtering technique by subtraction fitted sine from the raw signal, the definition of the criterion to stop iteration by residuals analysis, (b) impulse segmentation and description (beginning, end, max amplitude) that contains: detection of the number of impulses in a decomposed random part of the raw signal, detection of the max value of each impulse, statistical analysis (probability density function) of max value to find regime-switching), modeling of the envelope of each impulse for samples that protrude from the signal, extrapolation (forecasting) envelope shape for samples hidden in the signal. The procedure is explained using simulated and real data. Each step is very easy to implement and interpret thus the method may be used in practice in a commercial system.

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Section: Introductionmentioning

confidence: 99%

Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study

Gąsior

Urbańska

Grzesiek

et al. 2020

Sensors

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show abstract

“…Due to the complicated structure and harsh operating environment, various faults of rolling bearings (inner ring fault, outer ring fault, ball fault) is inevitable; thus, it is of great significance to study the fault detection methods and diagnostic techniques of rolling bearings [6][7][8][9][10]. In order to find out the fault location, the current frequently used fault diagnosis method is the time-frequency analysis method, such as the variable mode decomposition, local mean mode decomposition, empirical mode decomposition and so on [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis

Guo

Wang

et al. 2019

Entropy

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The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier. Its classification process is divided into an offline training stage, an online training stage, and a testing stage. Representative samples of different categories are obtained through the first two stages, and these representative samples are called prototypes. However, in the testing stage, the classification of testing samples is completely dependent on the prototype with the maximum similarity, without considering the influence of other prototypes on the classification decision of testing samples. Aiming at the testing stage, this paper proposed a new SOF classifier based on the harmonic mean difference (HMDSOF). In the testing stage of HMDSOF, firstly, each prototype was sorted in descending order according to the similarity between each prototype in the same category and the testing sample. Secondly, multiple local mean vectors of the prototypes after sorting were calculated. Finally, the testing sample was classified into the category with the smallest harmonic mean difference. Based on the above new method, in this paper, the multiscale permutation entropy (MPE) was used to extract fault features, linear discriminant analysis (LDA) was used to reduce the dimension of fault features, and the proposed HMDSOF was further used to classify the features. At the end of this paper, the proposed fault diagnosis method was applied to the diagnosis examples of two groups of different rolling bearings. The results verify the superiority and generalization of the proposed fault diagnosis method.

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“…The accuracy of this method is 99%, but the calculation requirement is higher. Cai et al [11] proposed an alternative demodulation method for bearing fault diagnosis using envelope derivative operator. In the field of feature extraction, the results show that this method is better than Teager-Kaiser energy operator.…”

Section: Introductionmentioning

confidence: 99%

Optimization-based improved kernel extreme learning machine for rolling bearing fault diagnosis

Zheng

Xiang

Cui

2019

J Braz. Soc. Mech. Sci. Eng.

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Rolling bearing is one of the key components in rotating machinery. The working condition of rolling bearing is complex and non-stationary with shock and noise. Thus, fault diagnosis of rolling bearing is of great significance in rotating machinery. In this paper, a novel method called optimization-based improved kernel extreme learning machine is proposed for fault diagnosis of rolling bearing. Firstly, different signal processing methods and data analysis methods are used as the second layer for feature extraction and vibration data dimension reduction, and the extracted data are sorted by experience pool. Secondly, kernel extreme learning machine (K-ELM) is used as the hidden layer and the output layer to enhance feature learning and classification of the extracted data. Finally, particle swarm optimization is employed to optimize the key parameters of the improved kernel extreme learning machine. The proposed method and five other methods are applied to analyze the raw vibration data of rolling bearing, and the results confirm that the proposed method is more effective than ELM, K-ELM, optimized K-ELM, support vector machine and back-propagation neural network.

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An alternative demodulation method using envelope-derivative operator for bearing fault diagnosis of the vibrating screen

Cited by 31 publications

References 21 publications

Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study

Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components—A Sieving Screen Case Study

A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis

Optimization-based improved kernel extreme learning machine for rolling bearing fault diagnosis

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