Shaft angular misalignment detection using acoustic emission

Chacon, Juan Luis Ferrando; Artigao, Estefanía; Kappatos, Vassilios; Asfis, Georgios; Gan, Tat-Hean; Balachandran, W.

doi:10.1016/j.apacoust.2014.03.018

Cited by 53 publications

(16 citation statements)

References 20 publications

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“…As presented in the literature review, different condition monitoring strategies have focused on detecting the occurrence of faulty conditions in gearbox transmission systems; indeed, the occurrence of most of the addressed conditions is related to inadequate lubrication, eccentricity failures, fluctuating loads, decoupling, misalignment, poor cooling, and gear design, among others [8][9][10][11]. However, most of the reported methodologies are limited to the diagnosis of gearbox faults as a single gear tooth damage, whereas most analyzed conditions are related to the presence of irregularities in the teeth of the gears such as tooth breakage; chipping and cracks in the root; and chipping, pitting and damage to the surface of the tooth.…”

Section: Discussionmentioning

confidence: 99%

“…In the literature, different methodologies for monitoring and detecting gearbox faults have been reported based on different sensing techniques and methods for analyzing and classifying data. For example, acoustic emissions are used to detect angular misalignments of shafts in the gearbox [9]. Furthermore, the influence of the oil film thickness has been considered in the detection of failures in helical gearboxes [10], and also the application of the discrete wavelet transform and the use of artificial neural networks for acoustic signals have been taken into account for the detection of faults in gearboxes [11].…”

Section: Introductionmentioning

confidence: 99%

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Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

et al. 2020

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In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

et al. 2020

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“…The fault detection in which there is no need to direct access to the faulted part has been investigated in some literature and some processes are proposed. In the articles, the fault detection is carried out using acoustic signals [2][3][4][5], vibration signals [6][7][8][9][10][11], some other types of signals [12][13][14][15], and the combination of some signals [16][17][18][19][20]. The KNN method is utilized to classify the short-circuit fault in the main winding and auxiliary coil of the single-phase induction motor using acoustic signals [2].…”

Section: Introductionmentioning

confidence: 99%

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Rahnama

Vahedi

Mohammad‐Alikhani

et al. 2019

IET Science, Measurement & Technology

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In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. In this paper, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively.

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“…The acquisition of accurate data is critical to determine the occurrence of a fault and to address the solution to apply. Nowadays, different techniques are available for CM: vibration analysis [3,11], acoustic emission [3,12], ultrasonic testing techniques [13,14], oil analysis [15], thermography [3,13] and other methods [16].…”

Section: Applied To Wtmentioning

confidence: 99%

Optimal Maintenance Management of Offshore Wind Farms

2016

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Abstract:Nowadays offshore wind energy is the renewable energy source with the highest growth. Offshore wind farms are composed of large and complex wind turbines, requiring a high level of reliability, availability, maintainability and safety (RAMS). Firms are employing robust remote condition monitoring systems in order to improve RAMS, considering the difficulty to access the wind farm. The main objective of this research work is to optimise the maintenance management of wind farms through the fault probability of each wind turbine. The probability has been calculated by Fault Tree Analysis (FTA) employing the Binary Decision Diagram (BDD) in order to reduce the computational cost. The fault tree presented in this paper has been designed and validated based on qualitative data from the literature and expert from important European collaborative research projects. The basic events of the fault tree have been prioritized employing the criticality method in order to use resources efficiently. Exogenous variables, e.g., weather conditions, have been also considered in this research work. The results provided by the dynamic probability of failure and the importance measures have been employed to develop a scheduled maintenance that contributes to improve the decision making and, consequently, to reduce the maintenance costs.

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Shaft angular misalignment detection using acoustic emission

Cited by 53 publications

References 20 publications

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Optimal Maintenance Management of Offshore Wind Farms

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