Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations

Ali, Jaouher Ben; Saïdi, Lotfi; Mouelhi, Aymen; Chebel-Morello, Brigitte; Fnaiech, Farhat

doi:10.1016/j.engappai.2015.03.013

Cited by 76 publications

(23 citation statements)

References 49 publications

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“…In the same year, the same author proposed automated nearly online bearing fault diagnosis to classify vibration signals' parameter using a combination of PNN and Simplified Fuzzy Adaptive Resonance Theory Map (SFAM). The application of ANN method able to recognise the different type of bearing defect [28]. Rao et al utilised feed forward four layers ANN to analysed bearing defect size by using load, revolution per minutes (RPM) and RMS velocity as input to ANN [29].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An intelligent bearing fault diagnosis system: A review

et al. 2019

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Rolling element bearing (REB) is a well-known component that most extensively used in the industry. They operate in extreme condition (high temperature, dirty environment) which may lead to unexpected failure after the certain operation. Faulty on bearing cause severe equipment damage, financial loss and threaten people's life. Development of proper fault diagnosis system of REB capable of preventing unexpected failure from occurs and maintain the machine work in the healthy state. Over a few decades, machine learning is introduced to provide a consistent fault diagnosis result. Hence, this paper reviewed the development of bearing diagnosis method using machine learning models.

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Section: Artificial Neural Networkmentioning

confidence: 99%

An intelligent bearing fault diagnosis system: A review

et al. 2019

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“…Probabilistic neural network has the good classification performance and global optimization, and thus it can be widely used in the field of detection and classification [16][17][18]. The structural model of PNN is shown in…”

Section: The Basic Principle and Model Of The Probability Neural Networkmentioning

confidence: 99%

Research on the dynamic networking of smart meters based on characteristics of the collected data

Huang¹,

Sun²,

Zhang³

2019

Turk J Elec Eng & Comp Sci

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In order to accurately collect the electricity usage information from the smart meter which uses the power line for communication, this paper proposes the method of dynamic networking to enhance the reliability of the smart meter communication. We shall firstly establish a logical topology between the smart meter and the concentrator with reference to their communication paths within the power supply range of the same transformer, and then grade smart meters, and choose the relay for each level network based on the selection methods of relay, and finally use the improved ant colony algorithm to choose the optimal communication path for smart meters in the communicref1ref1ation range of multiple relays. Although the optimal path can be discovered quickly in this way, it cannot establish other communication paths in time when the collected data are abnormal. Therefore, on the basis of the characteristics of data, this paper uses the probabilistic neural network to determine the integrity of the data collected. If the collected data is abnormal, we will reconstruct communication network by choosing a new communication path between concentrator and smart meters. By means of the MATLAB simulation, we can automatically organize the network to improve the reliability of communication between the concentrator and smart meters, which is of great significance for the concentrator to collect the users' electricity usage information.

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“…Therefore, it is necessary to compare it with some usual identification methods, such as support vector machine based schemes [32], particle swarm optimization and support vector machine based schemes [12], artificial neural networks and intelligent filter based schemes [33], PNN and simplified fuzzy adaptive resonance theory map neural networks based schemes [15], and fuzzy c-means based schemes [34]. When the class label of each signal is known, the performance of the above methods can be compared by calculating the classification accuracy and the identification accuracy.…”

Section: Comparative Experimentsmentioning

confidence: 99%

“…In [11–13], the support vector machine was introduced to identify emitter signals. In [14, 15], the fuzzy c-means and probabilistic neural networks (PNN) were used to identify emitter signals. However, the determination of the optimal number of clusters was a major challenge for these methods, and they cannot effectively improve the identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis

Liao

Yang

2018

Computational Intelligence and Neuroscience

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The rapid development of modern communication technology makes the identification of emitter signals more complicated. Based on Ward's clustering and probabilistic neural networks method with correlation analysis, an ensemble identification algorithm for mixed emitter signals is proposed in this paper. The algorithm mainly consists of two parts, one is the classification of signals and the other is the identification of signals. First, self-adaptive filtering and Fourier transform are used to obtain the frequency spectrum of the signals. Then, the Ward clustering method and some clustering validity indexes are used to determine the range of the optimal number of clusters. In order to narrow this scope and find the optimal number of classifications, a sufficient number of samples are selected in the vicinity of each class center to train probabilistic neural networks, which correspond to different number of classifications. Then, the classifier of the optimal probabilistic neural network is obtained by calculating the maximum value of classification validity index. Finally, the identification accuracy of the classifier is improved effectively by using the method of Bivariable correlation analysis. Simulation results also illustrate that the proposed algorithms can accurately identify the pulse emitter signals.

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Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations

Cited by 76 publications

References 49 publications

An intelligent bearing fault diagnosis system: A review

An intelligent bearing fault diagnosis system: A review

Research on the dynamic networking of smart meters based on characteristics of the collected data

A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis

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