Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples

Feng, Zhipeng; Liang, Ming; Chu, Fulei

doi:10.1016/j.ymssp.2013.01.017

Cited by 800 publications

(388 citation statements)

References 94 publications

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“…the research community [28]. For the purposes of this paper, the Morlet wavelet, Ψ ( ), is chosen because of its similarity to the impulse component of symptomatic faults of many mechanical systems [29] and is defined as…”

Section: Scalogramsmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

301

198

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Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Timefrequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.

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Section: Scalogramsmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

301

198

View full text Add to dashboard Cite

show abstract

“…When considering data acquisition, this study performs a nonstationary signal analysis for bearing fault diagnosis. Feng et al [37] classified the various time-frequency analysis methods applied to machinery fault diagnosis into four categories: linear and bilinear timefrequency representations (e.g., wavelet transform); adaptive parametric time-frequency analysis; adaptive nonparametric time-frequency analysis (e.g., Hilbert-Huang transform) [38]; and time varying higher order spectra. Various methods adapted to specific conditions have been used since then.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Pino

Ribas

Guimarães

2018

Shock and Vibration

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The contribution of a medium-sized hydro power plant to the power grid can be either at base load or at peak load. When the latter is the most common operation mode, it increases the start and stop frequency, intensifying the hydro turbine components' degradation, such as the guide bearings. This happens due to more frequent operation in transient states, which means being outside the service point of the machines' nominal condition, consisting of speed, flow, and gross head. Such transient state operation increases the runner bearings' mechanical vibration. The readings are acquired during the runner start-ups and filtered by a DC component mean value and a wavelet packet transform. The filtered series are used to estimate the relationship between the maximum orbit curve displacement and the accumulated operating hours. The estimated equation associated with the ISO 7919-5 vibration standards establishes the sojourn times of the degradation states, sufficient to obtain the transition probability distribution. Thereafter, a triangular probability function is used to determine the observation probability distribution in each state. Both matrices are inputs required by a hidden Markov model aiming to simulate the equipment deterioration process, given a sequence of maximum orbit curve displacements.

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“…In [16], Peng Z. K. et al compared the HHT and WT, and then verified that the HHT has better resolution and computational efficiency in time-frequency domain. EMD and Hilbert spectrum were used to diagnose rotating machines faults [17,18]. Several adaptive methods based on EMD have been developed such as the LMD [19], intrinsic time-scale decomposition (ITD) [20].…”

Section: Introductionmentioning

confidence: 99%

Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Rahmoune¹,

Merainani²,

Benazzouz³

et al. 2018

J. vibroeng.

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Abstract. Vibration signal of gearbox systems carries the important dynamic information for fault diagnosis. However, vibration signals always show non stationary behavior and overwhelmed by a large amount of noise make this task challenging in many cases. Thus, a new fault diagnosis method combining the Hilbert empirical wavelet transform (HEWT), the singular value decomposition (SVD) and Elman neural network is proposed in this paper. Vibration signals of normal gear, gear with tooth root crack, gear with chipped tooth in width, gear with chipped tooth in length, gear with missing tooth and gear with general surface wear are collected in different speed and load conditions. HEWT, a new self-adaptive time-frequency analysis, was applied to the vibration signals to obtain the instantaneous amplitude matrices. Singular value vectors, as the fault feature vectors were then acquired by applying the SVD. Last, the Elman neural network was used for automatic gearbox fault identification and classification. Through experimental results, it was concluded that the proposed method can accurately extract and classify the gear fault features under variable conditions. Moreover, the performance of the proposed HEWT-SVD method has an advantage over that of Hilbert-Huang transform (HHT)-SVD, local mean decomposition (LMD)-SVD or wavelet packet transform (WPT)-PCA for feature extraction.

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Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples

Cited by 800 publications

References 94 publications

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

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