A study on helicopter main gearbox planetary bearing fault diagnosis

Zhou, Linghao; Duan, Fang; Corsar, Michael; Elasha, Faris; David, Éric

doi:10.1016/j.apacoust.2017.12.004

Cited by 59 publications

(26 citation statements)

References 16 publications

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“…In this article for the feature extraction of the signals, the statistical information is used and in order to identify the pattern, the neural network is utilized. Helicopter main gearbox planetary bearing fault diagnosis is studied in [7] in which the envelope analysis and kurtogram were performed on vibration signals. Footprint analysis of Hilbert transform along with the neural network are used for ball bearings fault classification in [8].…”

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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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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“…For the method to be effective, an appropriate signal analysis should be performed with the use of methods in the time or frequency domain. In most cases required are more advanced diagnostic methods based on the statistical analysis or envelope analysis [2,3], and also on the wavelet analysis [4,5]. In the analysis of the signal envelope, it is significant to select the frequency band for which this analysis is to be carried out.…”

Section: Introductionmentioning

confidence: 99%

The diagnostic method of rolling bearing in planetary gearbox operating at variable load

Pawlik¹

2019

Diagnostyka

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Diagnostics of machines operating at variable loads has been widely described in literature. The methods of analysing the vibroacoustic signals generated by such machines have been developed since the 1980s. They involve a synchronous sampling of signals which carry diagnostic information, where the sampling frequency depends on the machine rotational speed. Presently, there are many methods in the literature used for synchronization of signals with rotational speed based on signal decimation, subsampling or Gabor transform. However, these methods do not totally solve the problem of diagnosing the machines operating at various loads. The change of machine load also affects the amplitudes of diagnostic parameters.The paper attempts to develop a diagnostic method that is independent of the system load change. The method is based on parameterization of amplitudes of characteristic orders. Single-number statistical parameters have been proposed for diagnostics of rolling bearings operating at variable loads. Tests have been conducted on a laboratory rig where the tested object was a rolling bearing on an output shaft of a planetary gearbox. The bearing was damaged by removing the grease which is a frequent type of damage in industry and can lead to a quick bearing seizure.Keywords: vibroacoustic diagnostics, bearing diagnostics, order analysis, variable load.METODA DIAGNOZOWANIA ŁOŻYSKA TOCZNEGO PRZEKŁADNI PLANETARNEJ PRACUJĄCEJ PRZY ZMIENNYM OBCIĄŻENIU Streszczenie Diagnostyka maszyn pracujących przy zmiennym obciążeniu jest problemem szeroko opisywanym w literaturze. Metody analizy sygnałów wibroakustycznych pochodzący od tego typu maszyn rozwijane były już od lat 80-tych. Polegają one na synchronicznym próbkowaniu sygnałów niosących informację diagnostyczną, gdzie zmienna częstotliwość próbkowania zależy od sygnału prędkości obrotowej diagnozowanej maszyny. Obecnie w literaturze znaleźć można wiele metod służących do synchronizacji sygnałów z prędkością obrotową opartych na decymacji sygnału, subsamplingu czy transformacji Gabora. Jednak zastosowanie tych metod nie rozwiązuje do końca problemu diagnozowania maszyn pracujących przy zmiennym obciążeniu. Zmiana obciążenia maszyny ma również wpływ na zmianę wartości amplitud parametrów diagnostycznych.W pracy podjęto problem opracowania metody oceny stanu technicznego niewrażliwej na zmianę obciążenia badanego układu. Metoda bazuje na parametryzacji przebiegów amplitud rzędów charakterystycznych. Zaproponowano jednoliczbowe parametry statystyczne do diagnozowania łożysk tocznych pracujących przy zmiennym obciążeniu. Przeprowadzono badana na stanowisku laboratoryjnym gdzie badanym obiektem było łożysko toczne na wale wyjściowym przekładni planetarnej. Diagnozowane łożysko uszkodzono poprzez wypłukanie smaru, co jest często spotykanym uszkodzeniem w przemyśle i może prowadzić do szybkiego zatarcia łożyska.Słowa kluczowe: diagnostyka wibroakustyczna, diagnostyka łożysk, analiza rzędów, zmienne obciążenie.

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“…Analysis of the frequency-domain provides a more direct way to monitor the health condition of its components by tracking the amplitude at specific frequencies related to specific components. Signal processing techniques are usually applied in this scenario in order to detect modulations related to the fault that may be hidden in the spectrum [1,2]. One of the most common approaches in bearing fault detection is the Envelope Analysis, where the signal is demodulated in order to detect the lower cyclic modulations of the bearing.…”

Section: Introductionmentioning

confidence: 99%

“…al. [1] concluded that the Kurtogram was ineffective by itself, and the damage was only diagnosable after application of a self-adaptive noise cancellation filter. The last decade, the interest in the development and use of cyclostationary based tools, such as the Cyclic Spectral Correlation (CSC) and Cyclic Spectral Coherence (CSCoh) for condition monitoring, has been increased as they achieve high performance in revealing hidden periodicities in their bi-variable representations.…”

Section: Introductionmentioning

confidence: 99%

Advanced signal processing tools for helicopters’ future Health and Usage Monitoring Systems (HUMS)

Mauricio

Zhou

Greaves³

et al. 2019

MATEC Web Conf.

Self Cite

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Health and Usage Monitoring Systems (HUMS) have been developed in order to monitor the health condition of helicopter drivetrains, focusing towards early, accurate and on time fault detection with limited false alarms and missed detections. Among other systems, the Main GearBox (MGB) is the heart of the drivetrain, reducing the high input speed generated by the engines, in order to provide the appropriate torque to the main rotors and to other auxiliary systems. HUMS are mounted on helicopters aiming to enhance the operational reliability and to support maintenance decision making, in order to increase the flight safety keeping in the meanwhile the overall maintenance cost low. Currently used HUMS seems to have reached their limits and the need for improvement has been recently highlighted by the post-accident analysis of the helicopter LN-OJF, which crashed in Norway in 2016. The aim of this paper is the application and further extension of recently proposed advanced cyclostationary based signal processing tools for the accurate detection of faults in helicopter gearboxes. The methodologies are tested, evaluated and compared with state of the art methods on datasets captured during experimental tests under various operating conditions on helicopter gearboxes, including a Category A Super Puma SA330 main planetary gearbox.

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A study on helicopter main gearbox planetary bearing fault diagnosis

Cited by 59 publications

References 16 publications

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

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

The diagnostic method of rolling bearing in planetary gearbox operating at variable load

Advanced signal processing tools for helicopters’ future Health and Usage Monitoring Systems (HUMS)

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