On-line acoustic detection of bearing defects

Bambara, Joseph E.; Frarey, J. L.; Smith, Richard Langham

doi:10.1121/1.398043

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This makes bearing fault detection a very challenging task [1]. A popular approach is to avoid interferences using the high frequency resonance (HFR) method [2][3][4][5][6]. This method is composed of bandpass filtering and signal demodulation.…”

Section: Introductionmentioning

confidence: 99%

Bandstop filtering for interference removal in bearing fault detection

Zhang

Liang

2012

2012 IEEE Conference on Prognostics and Health Management

View full text Add to dashboard Cite

Interference components in the faulty bearing signals have been one of the major obstacles in detecting bearing faults featuring weak impulses in the vibration signals. This paper reports a band stop (BS) method to remove such interferences. The BS method is composed of two window merging schemes to identify the interference frequency bands and to preserve fault frequency bands. Our simulation analysis has shown that the proposed method compares favorably with several existing methods. Its effectiveness in bearing fault detection has also been demonstrated using experimental data.

show abstract

Section: Introductionmentioning

confidence: 99%

Bandstop filtering for interference removal in bearing fault detection

Zhang

Liang

2012

2012 IEEE Conference on Prognostics and Health Management

View full text Add to dashboard Cite

show abstract

“…These impulses excite a resonance in the entire system including the bearing, the sensor and the structure where the bearing is mounted. By the demodulation of this resonance band, Bearing Characteristic Frequencies (BCF) can be identified, this approach known as high-frequency resonance (HFR) technique [1,2] is a popular method in the industry. However, the major challenge in the application of the HFR technique is the proper selection of the center frequency and bandwidth of the band-pass filter.…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Bearing Condition Monitoring and Diagnosis

Lin

Ding

2010

AMM

View full text Add to dashboard Cite

Bearing failure is one of the foremost causes of breakdown in rotating machinery. To date, Envelope detection is always used to identify faults occurring at the Bearing Characteristic Frequencies (BCF). However, because the impact vibration generated by a bearing fault has relatively low energy, it is often overwhelmed by background noise and difficult to identify. Combined the results of extensive experiments performed in a series of bearings with artificial damage, this research investigates the effect of many influencing factors, such as demodulation methods, sampling frequency, variable machine speed and the signals collected in different directions, on the effectiveness of demodulation and the implications for bearing fault detection. By understanding these effects, a more skillful application of the envelope detection in condition monitoring and diagnosis is achieved.

show abstract

“…A high-frequency resonance (HFR) technique [2] and its variants are often used by industry to detect bearing faults [3][4][5][6][7][8]. This method involves bandpass filtering and amplitude demodulation (AD) prior to spectral analysis.…”

Section: Introductionmentioning

confidence: 99%

Parameter-free bearing fault detection based on maximum likelihood estimation and differentiation

Bozchalooi¹,

Liang²

2009

Meas. Sci. Technol.

View full text Add to dashboard Cite

Bearing faults can lead to malfunction and ultimately complete stall of many machines. The conventional high-frequency resonance (HFR) method has been commonly used for bearing fault detection. However, it is often very difficult to obtain and calibrate bandpass filter parameters, i.e. the center frequency and bandwidth, the key to the success of the HFR method. This inevitably undermines the usefulness of the conventional HFR technique. To avoid such difficulties, we propose parameter-free, versatile yet straightforward techniques to detect bearing faults. We focus on two types of measured signals frequently encountered in practice: (1) a mixture of impulsive faulty bearing vibrations and intrinsic background noise and (2) impulsive faulty bearing vibrations blended with intrinsic background noise and vibration interferences. To design a proper signal processing technique for each case, we analyze the effects of intrinsic background noise and vibration interferences on amplitude demodulation. For the first case, a maximum likelihood-based fault detection method is proposed to accommodate the Rician distribution of the amplitude-demodulated signal mixture. For the second case, we first illustrate that the high-amplitude low-frequency vibration interferences can make the amplitude demodulation ineffective. Then we propose a differentiation method to enhance the fault detectability. It is shown that the iterative application of a differentiation step can boost the relative strength of the impulsive faulty bearing signal component with respect to the vibration interferences. This preserves the effectiveness of amplitude demodulation and hence leads to more accurate fault detection. The proposed approaches are evaluated on simulated signals and experimental data acquired from faulty bearings.

show abstract

On-line acoustic detection of bearing defects

Cited by 4 publications

References 0 publications

Bandstop filtering for interference removal in bearing fault detection

Bandstop filtering for interference removal in bearing fault detection

Rolling Element Bearing Condition Monitoring and Diagnosis

Parameter-free bearing fault detection based on maximum likelihood estimation and differentiation

Contact Info

Product

Resources

About