Measurement and Analysis of Current Signals for Gearbox Fault Recognition of Wind Turbine

Lin, Deng-Fa; Chen, Po-Hung; Williams, M.

doi:10.2478/msr-2013-0010

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…To avoid these shortages of prevalent techniques, the motor current signature analysis (MCSA) for the CM of motor-operated gearbox [14][15][16][17][18][19][20] has received growing attention in recent years and achieved considerable advancements. These publications show many interesting achievements in this direction.…”

Section: Introductionmentioning

confidence: 99%

“…As a result they can be insufficient to properly correlate the stator current data with faulted conditions. Therefore, many researchers have investigated alternative signal analysis methods, such as empiric mode decomposition (EMD) [17], wavelet demodulation analysis [18] and combined methods [19][20], for analysing current signals for more accurate feature extraction. Although these efforts have shown promising results, they may be still deficient because these signal analysis methods possess limited noise reduction capability.…”

Section: Introductionmentioning

confidence: 99%

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Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

Zhang

Haram

et al. 2017

Mechanical Systems and Signal Processing

101

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Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear's lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 hours of operation, made when one of the monitored features is about 123% higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

Zhang

Haram

et al. 2017

Mechanical Systems and Signal Processing

101

View full text Add to dashboard Cite

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“…Similarly, the k-means algorithm can also be used to predict the remaining useful life of WTs [85]. Additionally, the k-nearest neighbors (kNN) algorithm has application in gearbox fault of WTs [86]. AI algorithms are just emerging, and many theories need to be studied urgently.…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Zhang

2019

Energies

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Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

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“…As well known, to gain more accurate fault detection performance, integrated strategies are usually adopted (Suryawan et al , 2016; Nael and Farra, 2006; Lin et al , 2013). Therefore, a novel integrated fault detection method based on the combination of MTN dynamic model, conjugate gradient (CG) algorithm, weight-elimination (WE) algorithm and MA algorithm, namely, MA-WE-CG-MTN, is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Integrated fault detection for industrial process monitoring based on multi-dimensional Taylor network

Chen

et al. 2022

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Purpose This paper aims to provide a novel integrated fault detection method for industrial process monitoring. Design/methodology/approach A novel integrated fault detection method based on the combination of Mallat (MA) algorithm, weight-elimination (WE) algorithm, conjugate gradient (CG) algorithm and multi-dimensional Taylor network (MTN) dynamic model, namely, MA-WE-CG-MTN, is proposed in this paper. First, MA algorithm is taken as data pre-processing. Second, in virtue of approximation ability and low computation complexity owing to the simple structure of MTN, MTN dynamic models are constructed for each frequency band. Furthermore, the CG algorithm is used to discipline the model parameters and the outputs of MTN model of each frequency band are gained. Third, the authors introduce the WE algorithm to cut down the number of middle layer nodes of MTN, reducing the complexity of the network. Finally, the outputs of MTN model for each frequency band are superimposed to achieve outputs of MTN model, and fault detection is proceeded by the residual error generator based on the difference between the output of MTN model and the actual output. Findings The novel proposed method is used to perform fault detection for industrial process monitoring effectively, such as the Benchmark Simulation Model 1 wastewater treatment process. Originality/value The novel proposed method has generality and provides considerably improved performance and effectiveness, which is used to perform fault detection for industrial process monitoring. The proposed method has good robustness, low complexity and easy implementation.

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Measurement and Analysis of Current Signals for Gearbox Fault Recognition of Wind Turbine

Cited by 14 publications

References 14 publications

Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Integrated fault detection for industrial process monitoring based on multi-dimensional Taylor network

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