Planetary Gearbox Fault Diagnosis via Torsional Vibration Signal Analysis in Resonance Region

Li, Kangqiang; Feng, Zhipeng; Liang, Xihui

doi:10.1155/2017/6565237

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…Chen and Feng's lab experimental tests, based on torsional vibrations, diagnosed sun, planet and ring gears local faults, despite the running conditions timevariability [96]. In [54], instead, amplitude modulation and frequency modulation (AM-FM) processes were used to model induced faults by torsional vibrations in resonance region.…”

Section: Multiple Sourcesmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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Section: Multiple Sourcesmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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“…Also, eliminating noise in the time-frequency domain is addressed by generalized S transform, thereby filtering a vibration signal and noted advantageous to WT [162]. Approaches such as torsional vibration analysis [122,163,164], envelope windowed synchronous average in the angle domain [165], and demodulation based on decomposition methods [131,166] have been found suitable for extraction of weak gear fault features in a planetary gear set, modulated by time-varying transmission paths under varying speed conditions. To this end, Obuchowski et al [167] presented a method for vibration signal enhancement.…”

Section: Other Signal-processing Techniquesmentioning

confidence: 99%

A Review on Vibration-Based Fault Diagnosis Techniques for Wind Turbine Gearboxes Operating Under Nonstationary Conditions

Sharma

2021

J. Inst. Eng. India Ser. C

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Numerous studies on vibration-based techniques for fault diagnosis of wind turbine gearboxes operating under nonstationary conditions have been reported. In spite, a review on vibration-based condition monitoring and fault diagnosis techniques for gearboxes of wind turbines operating under nonstationary conditions is unavailable. Thus, the objective of this review is to discuss filtering, decomposition, entropy, and cyclostationary analysis techniques, as well as summarizing the remaining issues. This review will discuss various vibration-based diagnostic approaches developed for wind turbine gearboxes under nonstationary conditions, including both simulation and experimental approaches. Studies on dynamic models of gear systems and advanced signal-processing techniques developed for nonstationary conditions are reviewed. Additionally, the importance of multi-sensor and cointegration based approaches is discussed, and intelligent classification methods that have been used to distinguish healthy and faulty gear systems are also reviewed. Finally, the remaining research challenges are outlined.

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“…Converting a time domain signal into the frequency or another domain suppresses noise while highlighting features, because background noise is also included in the time domain signal. The work in [7] expressed the resonance signal caused by the fault through Amplitude Modulation and Frequency Modulation (AM-FM) processes and derived the explicit equation of the Fourier spectrum. The work in [5] proposed a time-frequency analysis method based on an Ensemble Local Mean Decomposition (ELMD) and FKthat effectively separates fault features from non-Gaussian noise and ambient noise.…”

Section: Related Workmentioning

confidence: 99%

“…According to different Condition Indicators (CIs), a series of methods is generated, for example the Standard Deviation (SD), Peak-to-Peak (P2P), Skewness (SK), Kurtosis (KT), Condition Factor (CF), and Root Mean Square (RMS) [4,5], as shown in Figure 1. Vibration signals are the focus of attention in this type of research [6,7] because the acquisition is easier and the characteristics are more obvious [5]. From an energy perspective, the fault signal is periodic (recurring), and the cumulative strength of the signal is continuously increased with the periodic operation of the gearbox.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Zhang

Deng

et al. 2019

Sensors

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Given its importance, fault diagnosis has attracted considerable attention in the literature, and several machine learning methods have been proposed to discover the characteristics of different aspects in fault diagnosis. In this paper, we propose a Hybrid Deep Belief Network (HDBN) learning model that integrates data in different ways for intelligent fault diagnosis in motor drive systems, such as a vehicle drive system. In particular, we propose three data fusion methods: data union, data join, and data hybrid, based on detailed data fusion research. Additionally, the significance of the fusion is explained from the energy perspective of the signal. In particular, the appropriate fusion methods and data structures suitable for model training requirements can help improve the accuracy of fault diagnosis. Moreover, mixed-precision training is used as a special fusion method to further improve the performance of the model. Experiments with the datasets obtained from the simulation platform demonstrate the superiority of our proposed model over the state-of-the-art methods.

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Planetary Gearbox Fault Diagnosis via Torsional Vibration Signal Analysis in Resonance Region

Cited by 8 publications

References 20 publications

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A Review on Vibration-Based Fault Diagnosis Techniques for Wind Turbine Gearboxes Operating Under Nonstationary Conditions

Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

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