Autonomous Bearing Fault Diagnosis Method based on Envelope Spectrum

Klausen, Andreas; Robbersmyr, Kjell G.; Karimi, Hamid Reza

doi:10.1016/j.ifacol.2017.08.2262

Cited by 21 publications

(15 citation statements)

References 6 publications

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“…Moreover, it is possible that different faults result in different IMF components. Therefore, it is possible to select all IMFs with a K greater than three for further processing without losing fault information [20]. In this paper, IMFs with a K greater than three are added together to reconstruct a new signal, and the signal is then used to extract the fault feature frequencies based on envelope analysis.…”

Section: The Proposal Of the Methodsmentioning

confidence: 99%

“…A review of envelope detection was presented by Randall et al [18] and Tyagi et al [19], and envelope detection is being continuously improved to diagnose weaker fault information under strong noise. Klausen et al [20] presented a method for analyzing multiple narrow bands and bearing faults could be detected autonomously by a narrow-band envelope spectrum-based algorithm. The accelerated life test has verified the performance of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Wang²,

Wang³

et al. 2020

Sensors

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Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.

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Section: The Proposal Of the Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Wang²,

Wang³

et al. 2020

Sensors

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“…Envelope analysis of acoustic and vibration signals is used to detect such defective signals. Envelope analysis is first performed by filtering with a frequency band containing the resonant frequency excited by the defects [9,11,19,22]. The envelope is extracted by the Hilbert transform of the filtered signal.…”

Section: Hilbert Transformmentioning

confidence: 99%

“…CS approach plays a very important role in extracting the faulty signature of a rotating machinery [7,8]. Envelope analysis has been widely used as a tool to analyze CS signals for a long time, especially as a powerful faultdetection technique for rolling bearings [9][10][11]. The fault in rolling elements periodically impacts the mechanical system.…”

Section: Introductionmentioning

confidence: 99%

Time–Frequency Envelope Analysis for Fault Detection of Rotating Machinery Signals with Impulsive Noise

et al. 2021

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Envelope analysis is a widely used tool for fault detection in rotating machines. In envelope analysis, impulsive noise contaminates the measured signal, making it difficult to extract the features of defects. This paper proposes a time–frequency envelope analysis that overcomes the effects of impulsive noises. Envelope analysis is performed by dividing the signal into several sections through a time window. The effect of impulsive noises is eliminated by using the frequency characteristics of the short time rectangular wave. The proposed method was verified through simulation and experimental data. The simulation was conducted by mathematically modeling a cyclo-stationary process that characterizes rotating machinery signals. In addition, the effectiveness of the method was verified by the measured data of normal and defective air-conditioners produced on the actual assembly line. This simple proposed method is effective enough to detect the faults. In the future, the approaches of big data and deep learning will be required for the development of the prognostic health-management framework.

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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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Autonomous Bearing Fault Diagnosis Method based on Envelope Spectrum

Cited by 21 publications

References 6 publications

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Time–Frequency Envelope Analysis for Fault Detection of Rotating Machinery Signals with Impulsive Noise

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

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