Gearbox fault classification using dictionary sparse based representations of vibration signals

Medina, Rubén; Alvarez, Ximena; Jadan, Diana; Macancela, Jean-Carlo; Sánchez, René–Vinicio; Cerrada, Mariela

doi:10.3233/jifs-169537

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Therefore, more intrinsic features can be extracted. In recent years, a lot of gearbox fault diagnosis methods based on dictionary learning have been proposed [11,12]. Specially, Feng et al [13] combined shift invariant dictionary learning (SIDL) and traditional K-means singular value decomposition (K-SVD) to extract the latent constituent components of gearbox signal and reveal the true vibration patterns.…”

Section: Introductionmentioning

confidence: 99%

A sparse feature extraction method based on the shiftinvariable dictionary applied in the diagnosis of gearbox with multiple faults

Wang

Dong

Chen

et al. 2022

J. Phys.: Conf. Ser.

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The vibration signals of gearbox often contain complex components, such as the gear engagement, shafts rotation, bearings, and vibration of the box, and they brings a big challenge to fault diagnosis of gearbox. Furthermore, there are many fault types of gearbox, and the fault characteristics of them are possibly the same. For example, the pass frequency of the missing tooth or root crack in the gear on the input shaft are both equal to the rotation frequency of input shaft. In thus circumstance, traditional diagnosis methods are inapplicable, because we can’t determine which type of faults. In this paper, we proposed a dictionary learning with the shift-invariant dictionary to extract the fault features in a sparse way, and afterwards hidden Markov model (HMM) is utilized to identify the fault type from extractive features. We used the proposed method to diagnose the experimental data from a benchmark two-stage gearbox and there are nine different health conditions introduced to the pinion on the input shaft, and the diagnosis result verifies the good performance of the novel scheme in the fault diagnosis of gearbox.

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Section: Introductionmentioning

confidence: 99%

A sparse feature extraction method based on the shiftinvariable dictionary applied in the diagnosis of gearbox with multiple faults

Wang

Dong

Chen

et al. 2022

J. Phys.: Conf. Ser.

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“…Deng proposed a novel parametric dictionary design algorithm, that optimally matched the underlying fault impact characteristics of analyzed signals, but its anti-noise performance must be further improved [26]. Medina applied sparse representation in a dictionary learning approach to perform the accurate identification and classification of a gear fault dataset [27]. He designed two sub-dictionaries to separate the steady and impact modulations of gear compound faults.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Using Multiscale Sparse Frequency-Frequency Distributions

Zhang

Dong

et al. 2021

IEEE Access

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Gear fault related information is distributed over a broad frequency band, indicating a complex modulation mechanism. It is difficult to detect early-stage gear faults accurately by detecting fault frequencies in a limited frequency band. This paper proposes a novel method for achieving fault frequency detection more effectively. A short-frequency Fourier transform with a series of frequency-window functions is initially used to obtain the overall frequency information of a vibration signal. Subsequently, based on sparse decomposition and orthogonal matching pursuit, harmonic atoms are applied to refine modulation components from multiscale pseudo mono-components. A multiscale-sparse frequencyfrequency distribution is eventually applied to augment existing fault-related harmonic components. In addition, a synthesized sparse spectrum is acquired by determining the frequency-frequency ridge from the multiscale sparse frequency-frequency distribution. Compared with empirical-mode-decomposition and fast-kurtogram analyses, the effectiveness and superiority of the proposed method for gear fault detection have been verified via experiments.

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“…Fault classification is investigated by C. Li et al [14]; Pacheco et al [15]; L. Duan et al [16]; Medina et al [17]; X. Wang et al [18]; K. Liu et al [19]; J. Meng et al [20]; Sun et al [21]; and Y. Liao et al [22] In the work of C. Li As can be seen from the enclosed selection of papers intelligent computing techniques are playing a crucial role in system health management. It is apparent from this particular selection of papers that system health management can benefit significantly, in several of their main activities, from both intelligent and machine learning techniques.…”

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confidence: 99%

Advances in intelligent computing for diagnostics, prognostics, and system health management

Oliveira

2018

IFS

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The guest editors have accepted 41 papers with the special issue. By diagnosis, prognostics and system health management we mean a set of activities including: fault detection, fault classification, fault prognosis, and system modeling. Informally, fault detection refers to the real-time signal processing required to know whether or not a given system is in its healthy normal operating state. Fault classification refers to determination of the type of fault an unhealthy system is suffering from and is a pattern recognition task. Fault prognosis refers to the forecast of the remaining useful life of a system and is based on dynamic modeling. In general, these activities require a sequence of operations such as data acquisition and conditioning, feature extraction, feature selection, and a final

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Gearbox fault classification using dictionary sparse based representations of vibration signals

Cited by 9 publications

References 24 publications

A sparse feature extraction method based on the shiftinvariable dictionary applied in the diagnosis of gearbox with multiple faults

A sparse feature extraction method based on the shiftinvariable dictionary applied in the diagnosis of gearbox with multiple faults

Gearbox Fault Diagnosis Using Multiscale Sparse Frequency-Frequency Distributions

Advances in intelligent computing for diagnostics, prognostics, and system health management

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