Gearbox health condition identification by neuro-fuzzy ensemble

Zhang, Long; Xiong, Guoming; Liu, Leping; Qingsong, Cao

doi:10.1007/s12206-013-0112-0

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…Therefore, each network, trained on the same training set, gives different normal regions, as presented in Figure 3b. Simultaneous training of several networks and constructing an ensemble, in which their responses are averaged together, producing a more reliable representation of a normal region in feature space [34][35][36].…”

Section: Auto-associative Neural Network (Aann)mentioning

confidence: 99%

Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Górski

Jabłoński

Heesch

et al. 2021

Sensors

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Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

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Section: Auto-associative Neural Network (Aann)mentioning

confidence: 99%

Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Górski

Jabłoński

Heesch

et al. 2021

Sensors

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“…Recent development of NFSs based on fault diagnosis can be found in Refs. [17,[115][116][117][118][119][120][121][122]. Nevertheless, several issues still need to be considered.…”

Section: Nfssmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…In recent years, artificial neural networks (ANNs) have been widely used in intelligent fault diagnosis to conduct pattern classification. The performance of a single neural network is usually affected by initial parameters like weights and node number in middle layer, and thus its recognition accuracy is unstable [18]. Aimed at such a dilemma, many methods of the multiple classifier fusion have been applied in the field of pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy

Zhang

et al. 2016

Shock and Vibration

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In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.

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Gearbox health condition identification by neuro-fuzzy ensemble

Cited by 12 publications

References 16 publications

Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy

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