Condition monitoring and fault diagnosis methods for low-speed and heavy-load slewing bearings: a literature review

Wang, Fengtao; Liu, Chenxi; Su, Wensheng; Xue, Zhigang; Li, Hongkun; Han, Qingkai

doi:10.21595/jve.2017.18454

Cited by 20 publications

(7 citation statements)

References 41 publications

(40 reference statements)

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“…The reviews in the literature summarize that to obtain information on the state of a low-speed bearing, several indicators can be calculated [7,16,17,[77][78][79][80][81][82][83]. From all indicators found in the literature, a specific group is regularly used, which are referred to the remainder of this article as CIs.…”

Section: Classic Indicatorsmentioning

confidence: 99%

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Sandoval

Leturiondo

Vidal

et al. 2021

Sensors

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To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

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Section: Classic Indicatorsmentioning

confidence: 99%

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Sandoval

Leturiondo

Vidal

et al. 2021

Sensors

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“…Compared with ordinary bearings, hoist bearings are characterized by heavier loads and lower speeds [ 2 ]. A low rotation speed leads to weak fault vibration signals, and the vibration energy is easily attenuated in the transmission process.…”

Section: Introductionmentioning

confidence: 99%

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

Kou

et al. 2021

Entropy

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Low-speed hoist bearings are characterized by fault features that are weak and difficult to extract. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is an effective method for extracting periodic pulses in a signal. However, the decomposition effect of MOMEDA largely depends on the selected pulse period and filter length. To address these drawbacks of MOMEDA and accurately extract features from the vibration signal of a hoist bearing, an adaptive feature extraction method is proposed based on iterative autocorrelation (IAC) and MOMEDA. To automatically identify the pulse period, a new evaluation index named autocorrelation kurtosis entropy (AKE) was constructed to select the optimal IAC. To eliminate the influence of the filter length on the decomposition effect, an iterative MOMEDA strategy was designed to gradually enhance signal impulse features. The Case Western Reserve University bearing dataset and bearing data from a self-made hoisting test setup were used to verify the effectiveness of IAC-MOMEDA in extracting weak features. Moreover, the capability of IAC-MOMEDA for features extraction of normal bearing vibration signal was further confirmed by field test data.

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“…Because the pitch bearings operate under 10 rpm, they are considered very low-speed bearings. In this scheme, CBM and fault diagnosis for bearings use data from multiple sources, such as lubricant condition, acoustic emission (AE) and vibration signals [11]. Although lubricant analysis can be done mainly with spectrometric and ferrographic analysis with positive results [12], AE and vibration signal are the main data source for bearing fault diagnosis [13].…”

Section: Introductionmentioning

confidence: 99%

Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures

et al. 2020

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Despite its influence on wind energy service life, condition-based maintenance is still challenging to perform. For offshore wind farms, which are placed in harsh and remote environments, damage detection is critically important to schedule maintenance tasks and reduce operation and maintenance costs. One critical component to be monitored on a wind turbine is the pitch bearing, which can operate at low speed and high loads. Classical methods and features for general purpose bearings cannot be applied effectively to wind turbine pitch bearings owing to their specific operating conditions (high loads and non-constant very low speed with changing direction). Thus, damage detection of wind turbine pitch bearings is currently a challenge. In this study, entropy indicators are proposed as an alternative to classical bearing analysis. For this purpose, spectral and permutation entropy are combined to analyze a raw vibration signal from a low-speed bearing in several scenarios. The results indicate that entropy values change according to different types of damage on bearings, and the sensitivity of the entropy types differs among them. The study offers some important insights into the use of entropy indicators for feature extraction and it lays the foundation for future bearing prognosis methods.

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Condition monitoring and fault diagnosis methods for low-speed and heavy-load slewing bearings: a literature review

Cited by 20 publications

References 41 publications

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures

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