Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery

Brkovic, Aleksandar; Gajić, Dragoljub; Gligorijevic, Jovan; Gajić, Ivana; Georgieva, Olga; Gennaro, S. Di

doi:10.1016/j.energy.2016.08.039

Cited by 76 publications

(69 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Li et al [173] used the non-dimensional symptom parameters after wavelet transformation to extract fault features, and then colony optimization was used to classify fault types at an early stage. Brkovic et al [174] used the wavelet transform to decompose the vibration signals, then the standard deviation of average energy and the logarithmic energy entropy were used as the fault features. Finally, the quadratic classifier was used for early fault detection and diagnosis in bearings.…”

Section: Other Methodsmentioning

confidence: 99%

“…Martin-del-Campo et al [171] Dictionary learning Almeida et al [172] Time-domain features + generic multi-layer perceptron Li et al [173] Wavelet transformation + ant colony optimization Brkovic et al [174] Wavelet transformation + quadratic classifier Li et al [175] Fuzzy lattice neurocomputing Cruz-Vega et al [176] Discrete wavelet + binary classification tree Martínez-Rego et al [177] Time domain features + one-class classifier…”

Section: Authors Methodologiesmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

170

View full text Add to dashboard Cite

Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

show abstract

Section: Other Methodsmentioning

confidence: 99%

Section: Authors Methodologiesmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

170

View full text Add to dashboard Cite

show abstract

“…The wavelet threshold method is to transform the signal to the wavelet domain; then obtain the wavelet coefficients and filter out noise; and finally, reconstruct the signal. The wavelet threshold function mainly includes the soft threshold as shown in Equation (5) and the hard threshold as shown in Equation (6). η(w) = (w − sgn(w)T)I(|w| > T) (5) η(w) = wI(|w| > T) (6) As shown in Equation (6), the treatment method of the hard threshold is to keep the wavelet coefficients above the threshold unchanged and change the wavelet coefficients below the threshold to 0.…”

Section: Signal Reconstruction Based On Eemd and Wsstmentioning

confidence: 99%

“…The wavelet threshold function mainly includes the soft threshold as shown in Equation (5) and the hard threshold as shown in Equation (6). η(w) = (w − sgn(w)T)I(|w| > T) (5) η(w) = wI(|w| > T) (6) As shown in Equation (6), the treatment method of the hard threshold is to keep the wavelet coefficients above the threshold unchanged and change the wavelet coefficients below the threshold to 0. However, this "guillotine" method will cause changes in the wavelet domain and lead to sudden local changes in the noise reduction results.…”

Section: Signal Reconstruction Based On Eemd and Wsstmentioning

confidence: 99%

“…In addition to the frequently-used time domain [2] and frequency domain analysis [3], current signal denoising methods have also developed some very advanced time-frequency analysis methods, such as empirical mode decomposition (EMD) [4], blind source separation (BSS) [5], energy entropy [6], variational mode decomposition (VMD) [7], wavelet transformation (WT) [8], approximate entropy (ApEn) [9], etc. Nonlinear and nonstationary signals can be decomposed into multiple intrinsic mode of the sampled signal, and PCA cannot well retain the real information of the original signal.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

View full text Add to dashboard Cite

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.Entropy 2020, 22, 290 2 of 28 functions (IMFs) by EMD [10]. Aiming at the shortcomings of the EMD, such as modal mixture and end effect, which affect the accuracy of signal decomposition [11], Wu et al. [12] improved the EMD method with auxiliary noise and proposed the ensemble empirical mode decomposition (EEMD) method.The EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis [13]. However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al. [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise. However, there is no uniform standard for the operation of the wavelet threshold method, which has a greater impact on the final result. The wavelet hard threshold denoising method generates discontinuous points and loses some vital information [17]. The wavelet soft threshold denoising method causes distortion phenomena, such as edge blur effect [18].Although the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but al...

show abstract

Integration of wavelet decomposition and artificial neural network for failure prognosis of reciprocating compressors

Wang

2021

Process Safety Progress

View full text Add to dashboard Cite

Compressors in petrochemical plants are often crucial to process operations, and when a failure occurs, the outcome can be catastrophic. Many researches have been attempting to detect failure modes as early as possible to plan upfront repair and conceivably reduce maintenance time. A reciprocating compressor was selected as the target of this study, and a few years of historical records of maintenance parameters and maintenance work orders were gathered for analysis. The time history was divided into 13 events, and each event started with a normal operation and ended with a repair work order. Time‐domain features and wavelet decomposition features of the parameters were extracted, and the patterns stored within each event were identified using the artificial neural network and support vector machine. Moreover, a set of reasoning algorithms were developed to detect anomalies, and responsible failure modes were identified. For a specific type of compressor, the vibration signal was found to be related to most of the anomalies and thus used for evaluation. Results showed a >90% detection rate for failure mode diagnosis based on historical test data.

show abstract

Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery

Cited by 76 publications

References 23 publications

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Integration of wavelet decomposition and artificial neural network for failure prognosis of reciprocating compressors

Contact Info

Product

Resources

About