Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

Han, Hyungseob; Cho, Sangjin; Kwon, Sundeok; Cho, Sang-Bock

doi:10.3390/electronics7020016

Cited by 36 publications

(21 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, wavelet transform and empirical mode decomposition (EMD) have effectively performed high resolution in both time and frequency domains, which have been successfully applied in faulty signal analysis. Continuous wavelet transform coefficients were used as features for the fault diagnosis system [11,12].…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the Effect of Alignment of the Rotor of the Emissions Exhaust Fan on Its Operating Parameters

Vojtko

Baron

Pollák

et al. 2019

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

The paper describes research examining the trends in balancing rotary machines and the application of the sequence of steps in execution of the operational balancing of an impeller of the industrial furnace emissions fan. The balancing process is based on the correction of the weight distribution of the body with the given axis of rotation. In trial operation, vibrations (with the measurement of the oscillation phase) were measured on both emission fan rotor bearings. Since the measured vibration value was higher than recommended, it was necessary to take steps to balance the fan’s impeller. The first step of the operational balancing is to identify the initial vibration vector relative to the reference mark, arbitrarily located on the rotating wheel of the fan. In trial operation, the balancing weight mass was changed in the selected balancing level, resulting in the change of the vibration vector compared to the initial state. The size and the angular position of the balancing weight were determined from the vibration values measured. 15 months into operation after the impeller was balanced, the diagnosis of its current technical condition was performed. Total vibrations of the device were measured in the low- and high-frequency range, at four measuring points on the rotor and the drive engine. The measurement confirmed a satisfactory technical condition of the engine and the fan equipment. The effect of balancing has also been demonstrated in the case observed. Reduced bearing wear, elimination of recurrent defects, foundation damage, etc., have been observed.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Examining the Effect of Alignment of the Rotor of the Emissions Exhaust Fan on Its Operating Parameters

Vojtko

Baron

Pollák

et al. 2019

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…To address these two issues, an improved CEEMDAN (ICEEMDAN) was further proposed and modes with less noise as well as more physical meaning can be obtained [13]. Although ICEEMDAN was originally proposed for biomedical signal processing [13,14], very recently few work [15] has researched its potential for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

Ran

Zhang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

show abstract

“…Generally, fault diagnosis techniques can be categorized into three main types in accordance with the diagnostic procedures: model-based, signal-based and data-based [ 6 ]. Signal processing is an indispensable part of these techniques because the purpose of signal processing is to discover fault signatures from the measured data from machinery in operation [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

Nikitin

Božek

Peterka

2020

Sensors

View full text Add to dashboard Cite

The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.

show abstract

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

Cited by 36 publications

References 23 publications

Examining the Effect of Alignment of the Rotor of the Emissions Exhaust Fan on Its Operating Parameters

Examining the Effect of Alignment of the Rotor of the Emissions Exhaust Fan on Its Operating Parameters

Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

Contact Info

Product

Resources

About