Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions

Liu, Zepeng; Yang, Boyuan; Wang, Xuefei; Zhang, Long

doi:10.1109/tia.2021.3058557

Cited by 36 publications

(12 citation statements)

References 23 publications

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“…Meanwhile, the failure signal is a signal that appears when there is an abnormality or disturbance when the engine is operating. Assuming that the deterministic signal and the failure signal are unrelated, Liu et al [ 50 ] write the acoustic emission signal as Equation ( 1 ), where

, and

are, respectively, acoustic emission signals, deterministic signals, and fault signals.

Microphone-Based Apart from the acoustic emission approach, there are various other ways to retrieve the acoustic signal from the component to be inspected.…”

Section: Resultsmentioning

confidence: 99%

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Qurthobi

Maskeliūnas

Damaševičius

2022

Sensors

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One of the most important strategies for preventative factory maintenance is anomaly detection without the need for dedicated sensors for each industrial unit. The implementation of sound-data-based anomaly detection is an unduly complicated process since factory-collected sound data are frequently corrupted and affected by ordinary production noises. The use of acoustic methods to detect the irregularities in systems has a long history. Unfortunately, limited reference to the implementation of the acoustic approach could be found in the failure detection of industrial machines. This paper presents a systematic review of acoustic approaches in mechanical failure detection in terms of recent implementations and structural extensions. The 52 articles are selected from IEEEXplore, Science Direct and Springer Link databases following the PRISMA methodology for performing systematic literature reviews. The study identifies the research gaps while considering the potential in responding to the challenges of the mechanical failure detection of industrial machines. The results of this study reveal that the use of acoustic emission is still dominant in the research community. In addition, based on the 52 selected articles, research that discusses failure detection in noisy conditions is still very limited and shows that it will still be a challenge in the future.

show abstract

, and

are, respectively, acoustic emission signals, deterministic signals, and fault signals.

Microphone-Based Apart from the acoustic emission approach, there are various other ways to retrieve the acoustic signal from the component to be inspected.…”

Section: Resultsmentioning

confidence: 99%

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Qurthobi

Maskeliūnas

Damaševičius

2022

Sensors

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“…High-performance bearings also bear internal friction and high temperature caused from the outside (such as heavy loads and temperature changes in different environments) [ 33 ]. In this study, we also carried out high-temperature durability testing of high-performance bearings in continuous operation for 300 h (the base temperature was 20 °C, and no external cooling was required during the test.).…”

Section: Multidimensional Experimental Analysismentioning

confidence: 99%

Multidimensional Study on the Wear of High-Speed, High-Temperature, Heavy-Load Bearings

et al. 2023

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The friction and wear performance of high-performance bearings directly affects the accuracy and maneuverability of weapons and equipment. In this study, high-speed, high-temperature, and heavy-load durability experiments of weapon bearings were carried out, and their wear properties (i.e., surface wear, metamorphic layer, scanning electron microscopy/energy-dispersive spectroscopy (SEM/EDS), residual stress, and retained austenite) were analyzed in multiple dimensions. The results showed the following: (1) The experimental temperature of the serviced front-end bearing is always lower than that of the rear bearing. (2) The metamorphic layer of the serviced rear bearing (i.e., inner ring, outer ring, rolling body, and cage) > the metamorphic layer of the serviced front-end bearing > the metamorphic layer of the unserviced bearing. (3) The rolling body of the rear bearing at high experimental temperatures contains not only elemental O, but also elemental P and Sr. (4) In the EDS analysis of the rolling elements, with the migration from the “ball edge” to the “ball center”, the elemental C in the rolling elements of serviced or unserviced bearings decreases slowly, while the elemental Fe content increases slowly.

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“…However, the features in the dictionary

are usually redundant and may not be necessary for the tool wear estimation. A sparse model representation is therefore beneficial in this case [ 22 , 23 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Real-Time Remaining Useful Life Prediction of Cutting Tools Using Sparse Augmented Lagrangian Analysis and Gaussian Process Regression

Xiao

Huang

Wang

et al. 2022

Sensors

Self Cite

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Remaining useful life (RUL) of cutting tools is concerned with cutting tool operational status prediction and damage prognosis. Most RUL prediction methods utilized different features collected from different sensors to predict the life of the tool. To increase the prediction accuracy, it is often necessary to mount a great deal of sensors on the machine in order to collect more types of signals, which can heavily increase the cost in industrial applications. To deal with this issue, this study, for the first time, proposed a new feature network dictionary, which can enlarge the number of candidate features under limited sensor conditions, and the developed dictionary can potentially contain as much useful information as possible. This process can replace the installation of more sensors and incorporate more information. Then, the sparse augmented Lagrangian (SAL) feature selection method is proposed to reduce the number of candidate features and select the most significant features. Finally, the selected features are input to the Gaussian Process Regression (GPR) model for the RUL estimation. Extensive experiments demonstrate that our proposed RUL estimation framework output performs traditional methods, especially for the cost savings for on-line RUL estimation.

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Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions

Cited by 36 publications

References 23 publications

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Multidimensional Study on the Wear of High-Speed, High-Temperature, Heavy-Load Bearings

Real-Time Remaining Useful Life Prediction of Cutting Tools Using Sparse Augmented Lagrangian Analysis and Gaussian Process Regression

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