Signal optimization based generalized demodulation transform for rolling bearing nonstationary fault characteristic extraction

Zhao, Dongfeng; Wang, Tianyang; Gao, Robert X.; Chu, Fulei

doi:10.1016/j.ymssp.2019.106297

Cited by 49 publications

(32 citation statements)

References 27 publications

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“…Zhao et al [19] applied a number of advanced neural network architectures including firefly artificial neural network, particle swarm optimization neural network, and genetic artificial neural network, and achieved diagnosis accuracy of more than 98% on rotor fault types. Additional works explored the effectiveness of generalized demodulation transform in adapting to varying speed of the rotating element [20]. Wang et al [21] tackled the same problem by introducing multiscale analysis windows from sensor signals.…”

Section: Requiring Lessmentioning

confidence: 99%

“…First, the aforementioned methods only considered faults of one component, whereas in real applications it is possible that mixed faults in multiple components factor in the measured signals. Moreover, whereas many works introduced measures to passively reject noise and nonrelated factors such as varying rotational speeds [20], [21], none of the reviewed works actively evaluated the robustness of proposed methods by testing them against strategically introduced interference or perturbation signals. A robust model must reject a broader range of noise originated from external sources like signal acquisition process and varying operating conditions of the machinery, and it is not to be assumed that no noise or perturbation is deliberately introduced to disable the system.…”

Section: Requiring Lessmentioning

confidence: 99%

See 1 more Smart Citation

Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Chen

Meng

Tang³

et al. 2020

IEEE/ASME Trans. Mechatron.

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Fault diagnosis for rolling elements in rotating machinery persistently receives high research interest due to the said machinery's prevalence in a broad range of applications. State-of-the-art methods in such setups focus on effective identification of faults that usually involve a single component while rejecting noise from limited sources. This article studies the data-based diagnosis of mixed faults coming from multiple components with an emphasis on model robustness against a wide spectrum of external perturbation. A dataset is collected on a rotor and bearing system by varying the levels and types of faults in both the rotor and bearing, which results in 48 machine health conditions. A duplet classifier is developed by combining two 1-D convolutional neural networks (CNNs) that are responsible for the diagnosis of the rotor and bearing faults, respectively. Experimental results show that the proposed classifier can reliably identify the onset and nature of mixed faults. In addition, one-vs-all classifiers are built using the features generated by the developed 1-D CNNs as predictors to recognize previously unlearned fault types. The effectiveness of such classifiers is demonstrated using data collected from four new fault types. Finally, the robustness and ability to reject external perturbation of the duplet classification model are analyzed using kernel density estimation. The code for the proposed classifiers is available at https://github.com/siyuanc2/machine-fault-diag.

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Section: Requiring Lessmentioning

confidence: 99%

Section: Requiring Lessmentioning

confidence: 99%

Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Chen

Meng

Tang³

et al. 2020

IEEE/ASME Trans. Mechatron.

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“…Its faults are important reasons of shutdowns of machines. For reducing economic losses and avoiding disastrous accidents, novel IF estimations for rolling bearing has become an important topic [ 44 , 45 , 46 , 47 ].…”

Section: Validation Of the Proposed If Estimation Technology Via Ementioning

confidence: 99%

Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

Zhao

Gelman

Chu

et al. 2020

Sensors

Self Cite

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It is proposed a novel instantaneous frequency estimation technology, multi-generalized demodulation transform, for non-stationary signals, whose true time variations of instantaneous frequencies are unknown and difficult to extract from the time-frequency representation due to essentially noisy environment. Theoretical bases of the novel instantaneous frequency estimation technology are created. The main innovations are summarized as: (a) novel instantaneous frequency estimation technology, multi-generalized demodulation transform, is proposed, (b) novel instantaneous frequency estimation results, obtained by simulation, for four types of amplitude and frequency modulated non-stationary single and multicomponent signals under strong background noise (signal to noise ratio is −5 dB), and (c) novel experimental instantaneous frequency estimation results for defect frequency of rolling bearings for multiple defect frequency harmonics, using the proposed technology in non-stationary conditions and in conditions of different levels of noise interference, including a strong noise interference. Quantitative instantaneous frequency estimation errors are employed to evaluate performance of the proposed IF estimation technology. Simulation and experimental estimation results show high effectiveness of the proposed estimation technology.

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“…Consequently, they are limited under nonstationary conditions. At present, the common methods to overcome the influence of rotational speed change are the OT [10][11][12] and the similarity calculation method of DTW. Firstly, the essence of OT is to resample the original vibration signal at a constant angle increment and transform the nonstationary signal in the time domain into the stationary signal in the angle domain [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

Chen

Wen

2020

Shock and Vibration

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Effective fault detection and diagnosis (FDD) for rotating machinery is always a focus issue in improving the prognostic and health management (PHM) of the equipments. The existing usage of similarity measurement has been widely spread in searching the homologous fault responses from vibration signals, but most of them are just suitable for stable speed and cannot be applied in all variable speed conditions. In order to improve measurement performance, a fast-meshed phase portrait (FMPP) frame combining the phase-space technique and box-scoring calculation is proposed. Firstly, the variable-speed signal is divided into multiple undetermined fragments according to fault characteristic orders (FCOs). Secondly, the undetermined fragments are reconstructed into corresponding phase-space trajectories to overcome the time-delay matching inconsistency of the variable speed fragments. Thirdly, the phase-space trajectories are mapped into meshed phase portraits via box-scoring calculation. Such decisive calculation can effectively transforms the diverse unequal fragments into the phase diagrams with same size, which saves time for the subsequent similarity measurement. Finally, the proposed FMPP is tested both for accuracy and timeliness on a self-built bearing bench, where the order tracking (OT) and dynamic time warping (DTW) methods are used for the comparisons. The experiments proved the effectiveness of the proposed method.

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Signal optimization based generalized demodulation transform for rolling bearing nonstationary fault characteristic extraction

Cited by 49 publications

References 27 publications

Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

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