Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary

Qin, Yi; Zou, Jingqiang; Tang, Baoping; Wang, Yi; Chen, Haizhou

doi:10.1109/tii.2019.2909305

Cited by 90 publications

(34 citation statements)

References 30 publications

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“…Another alternative for obtaining an improved spectrogram is the Matching Pursuit approach [41,42]. This method is based on calculating a series of spectrograms, using a set of different windows designated as "dictionary" [43], which has to be previously built. Then, combining spectrograms corresponding to each window of the dictionary through a pre-defined algorithm, obtains the final spectrogram, which is considered the optimum one.…”

Section: Type Of Fault Fault Harmonics Frequencymentioning

confidence: 99%

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

et al. 2019

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Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain-such as a spectrogram-is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it-short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

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Section: Type Of Fault Fault Harmonics Frequencymentioning

confidence: 99%

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

et al. 2019

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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%

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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“…The research on fault detection of gear pitting has received a great deal of attention. Conventional gear pitting fault diagnosis methods are based on signal processing such as ensemble empirical mode decomposition [ 6 ], fast 1D K-SVD with adaptive dictionary [ 7 ], and autocorrelation-based time synchronous averaging [ 8 ]. In order to achieve automatic and accurate diagnoses, intelligent diagnosis methods based on machine learning are studied [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Vision Measurement of Gear Pitting Under Different Scenes by Deep Mask R-CNN

Qin

Wang

2020

Sensors

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To accurately and quantitatively detect the gear pitting of different levels on the actual site, this paper studies a new vision measurement approach based on a tunable vision detection platform and the mask region-based convolutional neural network (Mask R-CNN). The shooting angle can be properly set according to the specification of the target gear. With the obtained sample set of 1500 gear pitting images, an optimized deep Mask R-CNN was designed for the quantitative measurement of gear pitting. The effective tooth surface and pitting was firstly and simultaneously recognized, then they were segmented to calculate the pitting area ratio. Considering three situations of multi-level pitting, multi-illumination, and multi-angle, several indexes were used to evaluate detection and segmentation results of deep Mask R-CNN. Experimental results show that the proposed method has higher measurement accuracy than the traditional method based on image processing, thus it has significant practical potential.

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Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary

Cited by 90 publications

References 30 publications

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

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

Vision Measurement of Gear Pitting Under Different Scenes by Deep Mask R-CNN

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