A study of white etching crack bearing failure detection using electrostatic sensing in wind turbine gearboxes

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well.

Section: Electromagnetic Testing (Et)mentioning

confidence: 99%

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

Civera

Surace

2022

“…It has been discussed in the literature that manifestation of electrical discharges, due to the presence of a shaft voltage and the dielectric properties of the lubricant, drives fluting and corrugation damages [ 18 ]. However, recent studies have revealed that at much lower current densities, e.g., <1 mA/mm 2 , bearings’ life has been significantly reduced (to 5–10% of the expected L 10 life) due to the formation of WECs, but often combined with the presence of critical lubricants [ 19 , 20 , 21 ]. In these studies, it was concluded that the electrical discharges promote the formation of WECs [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, recent studies have revealed that at much lower current densities, e.g., <1 mA/mm 2 , bearings’ life has been significantly reduced (to 5–10% of the expected L 10 life) due to the formation of WECs, but often combined with the presence of critical lubricants [ 19 , 20 , 21 ]. In these studies, it was concluded that the electrical discharges promote the formation of WECs [ 19 , 20 , 21 ]. As illustrated in Figure 1 , the electrical circuit of a bearing race-lubricant-rolling element (a bearing contact) can be simplified as a capacitor in parallel with a resistor [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Electrostatic (ES) sensing techniques are proven to be capable of detecting charge-related phenomena, e.g., white layer formation [ 23 ], bearing failures in rolling contact fatigue mode [ 24 ], and WEC [ 20 , 21 ]. This study investigates electrostatic discharging on a twin-roller machine under various operating conditions where the applied electrical potential, lubricant temperature, entrain velocity, and load are varied, while the feasibility of ES sensing technique detecting discharging events is evaluated.…”

Section: Introductionmentioning

confidence: 99%

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Electrical Discharges in Oil-Lubricated Rolling Contacts and Their Detection Using Electrostatic Sensing Technique

Esmaeili

Harvey

White

et al. 2022

The reliability of rolling element bearings has been substantially undermined by the presence of parasitic and stray currents. Electrical discharges can occur between the raceway and the rolling elements and it has been previously shown that these discharges at relatively high current density levels can result in fluting and corrugation damages. Recent publications have shown that for a bearing operating at specific mechanical conditions (load, temperature, speed, and slip), electrical discharges at low current densities (<1 mA/mm2) may substantially reduce bearing life due to the formation of white etching cracks (WECs) in bearing components, often in junction with lubricants. To date, limited studies have been conducted to understand the electrical discharges at relatively low current densities (<1 mA/mm2), partially due to the lack of robust techniques for in-situ quantification of discharges. This study, using voltage measurement and electrostatic sensors, investigates discharges in an oil-lubricated steel-steel rolling contact on a TE74 twin-roller machine under a wide range of electrical and mechanical conditions. The results show that the discharges events between the rollers are influenced by temperature, load, and speed due to changes in the lubricant film thickness and contact area, and the sensors are effective in detecting, characterizing and quantifying the discharges. Hence, these sensors can be effectively used to study the influence of discharges on WEC formation.

“…Acoustic emission (AE) refers to an acoustic signal that is generated when the strain energy that is formed locally in a solid material is rapidly released [ 4 ]. AE is used for detecting predicted fractures in bearings [ 5 ], gearboxes [ 6 ], pressure vessels [ 7 ], and pipes [ 8 , 9 ] at many sites in the industry. Moreover, an AE signal is generated when an object is plastically deformed or fractured.…”

Section: Introductionmentioning

confidence: 99%

A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

Jeong

Hong

2021

In this study, an acoustic emission (AE) sensor was utilized to predict fractures that occur in a product during the sheet metal forming process. An AE activity was analyzed, presuming that AE occurs when plastic deformation and fracturing of metallic materials occur. For the analysis, a threshold voltage is set to distinguish the AE signal from the ripple voltage signal and noise. If the amplitude of the AE signal is small, it is difficult to distinguish the AE signal from the ripple voltage signal and the noise signal. Hence, there is a limitation in predicting fractures using the AE sensor. To overcome this limitation, the Kalman filter was used in this study to remove the ripple voltage signal and noise signal and then analyze the activity. However, it was difficult to filter out the ripple voltage signal using a conventional low-pass filter or Kalman filter because the ripple voltage signal is a high-frequency component governed by the switch-mode of the power supply. Therefore, a Kalman filter that has a low Kalman gain was designed to extract only the ripple voltage signal. Based on the KF-RV algorithm, the measured ripple voltage and noise signal were reduced by 97.3% on average. Subsequently, the AE signal was extracted appropriately using the difference between the measured value and the extracted ripple voltage signal. The activity of the extracted AE signal was analyzed using the ring-down count among various AE parameters to determine if there was a fracture in the test specimen.