Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm

Vernekar, Kiran; Kumar, Hemantha; Gangadharan, K. V.

doi:10.1007/s12046-017-0678-9

Cited by 24 publications

(15 citation statements)

References 15 publications

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“…The work in [2,8] updated this type of study and included SD, P2P, SK, and other methods. Shallow learning methods such as Support Vector Machines (SVM) [25], Naive Bayes (NB) [26], and logistic regression [27] are also common methods. The time domain method has made some progress in early research as a simple basic method [28].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Zhang

Deng

et al. 2019

Sensors

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Given its importance, fault diagnosis has attracted considerable attention in the literature, and several machine learning methods have been proposed to discover the characteristics of different aspects in fault diagnosis. In this paper, we propose a Hybrid Deep Belief Network (HDBN) learning model that integrates data in different ways for intelligent fault diagnosis in motor drive systems, such as a vehicle drive system. In particular, we propose three data fusion methods: data union, data join, and data hybrid, based on detailed data fusion research. Additionally, the significance of the fusion is explained from the energy perspective of the signal. In particular, the appropriate fusion methods and data structures suitable for model training requirements can help improve the accuracy of fault diagnosis. Moreover, mixed-precision training is used as a special fusion method to further improve the performance of the model. Experiments with the datasets obtained from the simulation platform demonstrate the superiority of our proposed model over the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Zhang

Deng

et al. 2019

Sensors

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“…To solve this problem, Huang et al [9] proposed the EMD, which can efficiently decompose nonlinear and nonstationary signals without any set of basis functions. Vernekar et al [16] presented an engine gearbox fault diagnosis on the basis of EMD and naive Bayes algorithm to manage vibration signals. ey classified healthy and different simulated faulty conditions of gear and bearing, and results showed that the classification accuracy of their method can reach 98.88%.…”

Section: Introductionmentioning

confidence: 99%

Separation Method of Impulsive Fault Component for Gasoline Engine Based on Acoustic Signal Analysis

Ning

Jiang

Sun

et al. 2019

Shock and Vibration

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This paper presents a developed dislocation superimposed method (DSM) for automatically extracting the component of impulsive signals from abnormal noise signals of an engine at a single speed range on the basis of the initial DSM. This method consists of three steps: using a correlation analysis to select an appropriate starting superposition point, superimposing abnormal sound signals to improve the signal-to-noise ratio, and intercepting superimposed signals to separate the fault component. Experimental results show that the developed DSM can effectively extract the fault characteristics of cylinder knocking and connecting rod bearing knocking. The developed approach can be applied to separate the fault characteristics of other types of rotating machines.

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“…Frequently, the signal is decomposed into experimental modes (Empirical Mode Decomposition -EMD), which correspond to the characteristic vibration frequencies and which can be treated as new multidimensional signals subjected to further analysis. In the paper [4], the authors, by means of decomposition into experimental modes, identified acceleration and machine learning signals, and the degree of damage to the gear tooth and various types of damage in the bearings. In the paper [5], the authors, using the extended EMD method (Ensemble Empirical Mode Decomposition -EEMD) in combination with machine learning methods, compared different fault detection algorithms in toothed wheels and obtained the best damage classification for the nearest neighbour method.…”

Section: Introductionmentioning

confidence: 99%

Gearbox damage identification using Ensemble Empirical Decomposition method

Jonak

Syta

2019

MATEC Web Conf.

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In this article, we have conducted a comparative analysis of vibration signals from helicopter aircraft propulsion transmissions, registered on an industrial research stand. We compared acceleration vibrations in the case of gears without physical damage and gears with one tooth missing. Based on recorded signals, we determined the values of indicators based on the statistical properties of signals and compared them with each other. For a more exact comparison, the distribution of the tested signals to the empirical modes using the EEMD (Ensemble Empirical Mode Decomposition) method was performed. This allows to treat individual modes as components of a signal at specific frequencies, and also prevents mixing of modes in individual components, which may take place in the classic EMD analysis. It should be noted that individual modes may correspond to characteristic frequencies for the operation of the transmission. When comparing the values of the most frequently used indicators, modes/frequencies in which the damage was most visible were indicated.

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Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm

Cited by 24 publications

References 15 publications

Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Separation Method of Impulsive Fault Component for Gasoline Engine Based on Acoustic Signal Analysis

Gearbox damage identification using Ensemble Empirical Decomposition method

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