Step-by-Step Compound Faults Diagnosis Method for Equipment Based on Majorization-Minimization and Constraint SCA

Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time–frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

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“…After that they used a classifier to distinguish the fault pattern. A step-by-step compound fault diagnosis method was reported in [4].…”

Section: Introductionmentioning

confidence: 99%

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

Guo

Zhang

et al. 2020

Sensors

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“…It has great significance in machinery condition monitoring to realize their timely fault feature extraction. Vibration analysis has been used widely in fault feature extraction of rotating machinery [1][2][3][4][5][6] in engineering due to the reasons that vibration signal is easy to collect and it also contains rich fault feature information. However, early weak fault features of rolling bearing or gear are hard to extract using the traditional signal processing method such as envelope demodulation spectral (EDS) [7] and wavelet transform [8] because the early weak fault features are often overwhelmed by strong interference.…”

Section: Introductionmentioning

confidence: 99%

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Wang

2020

Complexity

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Rolling element bearing and gear are the typical supporting or rotating parts in mechanical equipment, and it has important economy and security to realize their quick and accurate fault detection. As one kind of powerful cyclostationarity signal analyzing method, spectral correlation (SC) could identify the impulsive characteristic component buried in the vibration signals of rotating machinery effectively. However, the fault feature such as impulsive characteristic component is often interfered by other background noise, and the situation is serious especially in early weak fault stage. Besides, the traditional SC method has a drawback of low computation efficiency which hinders its wide application to some extent. To address the above problems, an impulsive feature-enhanced method which combines fast spectral correlation (FSC) with sparse representation self-learning dictionary is proposed in the paper. Firstly, the sparse representation self-learning dictionary method-K-means singular value decomposition (KSVD) is improved and the improved KSVD (IKSVD) method is used to denoise the original signal, and the periodic impulses are highlighted. Then, the FSC algorithm is applied on the denoised signal and spectral correlation image could be obtained. Finally, the calculated enhanced envelope spectrum (EES) of the denoised signal is obtained by using the spectral correlation image to identify the accurate fault position. The feasibility and superiority of the proposed method is verified through simulation, experiment, and engineering application.

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“…During the operation of rotating machinery, the vibration signal measured by the sensor is usually superimposed by the vibration of multiple components [1][2][3]. How to analyze, process, and identify these signals is very important for judging the working state of rotating machinery and fault diagnosis of equipment [4].…”

Section: Introductionmentioning

confidence: 99%

“…Qin et al [5] proposed a novel M-band flexible wavelet transform for identifying the underlying fault features in measured signals. Wang et al [2] proposed a step-by-step compound faults diagnosis method for equipment based on majorizationminimization (MM) and constraint sparse component analysis (SCA). Lu et al [15] proposes a novel approach to periodic fault signal enhancement in rotating machine vibrations with a tris table mechanical vibration amplifier (TMVA) by exploiting stochastic resonance (SR).…”

Section: Introductionmentioning

confidence: 99%

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Miao

Zhao

2020

Mathematical Problems in Engineering

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When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It is difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. In these environments, the vibration signals are first denoised with the synchronous cumulative average noise reduction (SCA) method, and the denoised signals were separated with the improved fast independent component analysis (FastICA) algorithm. The results of simulation test and rotor fault experiments demonstrate that the novel method can effectively extract fault features, certifying its superiority in comparison with previous methods. Therefore, it is likely to be useful and practical in the fault detection area, especially under the condition of strong noise and vibration interferences.

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Step-by-Step Compound Faults Diagnosis Method for Equipment Based on Majorization-Minimization and Constraint SCA

Cited by 37 publications

References 38 publications

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

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