An improved data fusion technique for faults diagnosis in rotating machines

Yunusa‐Kaltungo, Akilu; Sinha, Jyoti; Elbhbah, Keri

doi:10.1016/j.measurement.2014.08.017

Cited by 43 publications

(42 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the major concern in bearing fault feature extraction is to determine which signal processing tools and algorithms to use to distinguish and diagnose early stage fault characteristics. Up to now, various fault diagnosis techniques have been proposed attempting to address the above challenges, such as wavelet/wavelet-packet transform [4], local mean decomposition (LMD) and its extension [5], minimum entropy deconvolution (MED) and its extension [6,7] and artificial intelligence (AI) algorithms such as artificial neural network (ANN) and fuzzy algorithm [8][9][10], Hilbert envelope spectrum [11], energy and entropy methods [12][13][14], higher order statistical techniques [15][16][17][18], to mention just a few. Unfortunately, some potential drawbacks and severe shortcomings related to the common techniques still remained unresolved.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Liang

2017

Entropy

View full text Add to dashboard Cite

Abstract:The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse extraction methodology is proposed based on impulse-step dictionary and re-weighted minimizing nonconvex penalty Lq regular (R-WMNPLq, q = 0.5) for the incipient fault diagnosis of rolling bearings. Prior to the sparse representation, the original vibration signal is preprocessed by the variational mode decomposition (VMD) technique. Due to the physical mechanism of periodic double impacts, including step-like and impulse-like impacts, an impulse-step impact dictionary atom could be designed to match the natural waveform structure of vibration signals. On the other hand, the traditional sparse reconstruction approaches such as orthogonal matching pursuit (OMP), L1-norm regularization treat all vibration signal values equally and thus ignore the fact that the vibration peak value may have more useful information about periodical transient impulses and should be preserved at a larger weight value. Therefore, penalty and smoothing parameters are introduced on the reconstructed model to guarantee the reasonable distribution consistence of peak vibration values. Lastly, the proposed technique is applied to accelerated lifetime testing of rolling bearings, where it achieves a more noticeable and higher diagnostic accuracy compared with OMP, L1-norm regularization and traditional spectral Kurtogram (SK) method.

show abstract

Section: Introductionmentioning

confidence: 99%

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Liang

2017

Entropy

View full text Add to dashboard Cite

show abstract

“…Now for the system composed of the augmented state equation (10) and the equivalent measurement equation (18), the updating equation of thêand error covariance can be obtained aŝ…”

Section: Journal Of Control Science and Engineeringmentioning

confidence: 99%

“…Consequently, how to effectively integrate the information from multiple sensors to reduce system uncertainties and improve the FDD accuracy is becoming an important research issue [17,18]. A multisensor fusion and fault detection approach for air traffic surveillance was introduced in [19] based on hybrid estimation.…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Fault Diagnosis for Asynchronous Multisensor Systems Based on Augmented IMM and Strong Tracking Filtering

Xue

Jin

et al. 2018

Journal of Control Science and Engineering

View full text Add to dashboard Cite

A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive Multimodel (IMM) fusion filtering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered and a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of possible fault mode. By doing this, the dilemma of predetermining the fault extent as model design parameters in traditional IMMbased approaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track abrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance of fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility and effectiveness of the proposed method.

show abstract

“…Equations (1) locations, which has already been extensively described in an earlier study by YunusaKaltungo et al [24,1]. (2)-(3) represents the poly coherent composite FT for a particular segment 'r' of the measured vibration data from 'b' bearing locations at a particular frequency, ݂ ୩ , which was also computed as [24]; In order to enhance proper understanding of the current study, a brief re-iteration of the steps involved in the computations of pCCB and pCCT VFD method are again illustrated by the flowchart shown in Figure 1.…”

Section: Computation Of Poly Coherent Composite Spectramentioning

confidence: 99%

Sensitivity analysis of higher order coherent spectra in machine faults diagnosis

Yunusa‐Kaltungo

Sinha

2016

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

Abstract:In an earlier study, the poly coherent composite higher order spectra (i.e. poly coherent composite bispectrum and trispectrum) frequency domain data fusion technique was proposed to detect different rotor related faults. All earlier vibration-based faults detection (VFD) involving the application of poly coherent composite bispectrum (pCCB) and trispectrum (pCCT) have been solely based on the notion that the measured vibration data from all measurement locations on a rotating machine are always available and intact. In reality, industrial scenarios sometimes deviate from this notion, owing to faults and/or damages associated with vibration sensors or their accessories (e.g. connecting cables). Sensitivity analysis of the method to various scenarios of measured vibration data availability (i.e. complete data from all measurement locations and missing/erroneous data from certain measurement locations) is also examined through experimental and industrial cases, so as to bring out the robustness of the method.http://mc.manuscriptcentral.com/shmij Structural Health Monitoring AbstractIn an earlier study, the poly coherent composite higher order spectra (i.e. poly coherent

show abstract

An improved data fusion technique for faults diagnosis in rotating machines

Cited by 43 publications

References 20 publications

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Time-Varying Fault Diagnosis for Asynchronous Multisensor Systems Based on Augmented IMM and Strong Tracking Filtering

Sensitivity analysis of higher order coherent spectra in machine faults diagnosis

Contact Info

Product

Resources

About