A Method of Detecting Bearing Fault Signal Based on DLIA Implemented by FPGA

Chen, Xinda; Wei, Minxiang; Chen, Kai; Pei, Yuhang; Li, Shunming

doi:10.1109/phm-nanjing52125.2021.9612846

Cited by 3 publications

(7 citation statements)

References 17 publications

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“…Common characteristic frequencies corresponding to bearing faults include: ball pass frequency outer race (BPFO), ball pass frequency inner race (BPFI), ball spin frequency (BSF), and fundamental train frequency (FTF). Several studies have reported methods for calculating theoretical fault frequencies through bearing geometric parameters and rotational speeds [1,2,[20][21][22]. In summary, for different types of bearing faults, there are geometric formulae available for calculating the fault characteristic frequencies of bearings, manifested as followed.…”

Section: The Vibration Signal Of Faulty Bearingsmentioning

confidence: 99%

“…Generally, the theoretical fault characteristic frequencies of rolling bearings can be calculated based on the geometric relationship of the bearings themselves, which is also the common technical route adopted in previous reported works. For example [20], in detecting weak signals of bearing faults using an LIA with an integral averaging module as a filters [21], in developing an LIA for bearing fault diagnosis on a field programmable gate arrays platform, and [22] in acoustic detection of bearing faults using a fractional harmonic LIA, all used theoretical fault characteristic frequencies derived from the geometric relationship of bearings as the reference frequencies for the LIA.…”

Section: Introductionmentioning

confidence: 99%

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MKurt-LIA: mechanical fault vibration signal measurement scheme with frequency tracking capability for bearing condition monitoring

Zhang

2024

Meas. Sci. Technol.

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Monitoring the operational status of rolling bearings is crucial to ensuring the reliable operation of mechanical equipment, and it is considered as an important task of prognostics and health management in mechanical industry. Finding fault pattern directly from vibration signals measured on bearings can be challenging due to strong noise. Lock-in amplifiers (LIAs) can effectively measure the strength of weak fault signals distorted by noise, thus providing insights for fault diagnosis. However, LIAs are sensitive to the reference frequency, and an incorrect reference frequency can render the measurement even meaningless. In this study, a measurement scheme of LIA with the ability to track time-varying fault frequencies is proposed for measuring fault vibration signal components of in-service bearings, termed MKurt-LIA. The MKurt spectrum is employed for searching the local fault frequencies in the time domain, overcoming the negative impact introduced by using theoretical fault characteristic frequencies calculated based on bearing geometry in previous works. Guided by the statements in Guide to the Expression of Uncertainty in Measurement and its supplements, this work demonstrates through the analysis of propagation of distributions that the negative impact of fault frequency deviation on measurement results is significant and cannot be ignored. Experimental results confirm that the MKurt-LIA scheme effectively measures the amplitude of fault frequency components during bearing service, enabling the detection of fault evolution in the early stages where fault characteristics are completely submerged in the time domain waveform. MKurt-LIA scheme fully respects the fact that fault frequency always deviates from theoretical value, and achieves significantly robust performance in assessing in-service bearing’s operation condition.

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Section: The Vibration Signal Of Faulty Bearingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MKurt-LIA: mechanical fault vibration signal measurement scheme with frequency tracking capability for bearing condition monitoring

Zhang

2024

Meas. Sci. Technol.

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“…Regardless of the application scenarios of LIA, their basic principles are similar and follow the working process described by the following mathematical model, which has been reported in many works (Meade, 1982;Scofield, 1994;Huang et al, 2019;Chen et al, 2021aChen et al, , 2021bdel Rosario Bautista-Morales and Patiño-L opez, 2023). For example, considering the input to the LIA as:…”

Section: Lock-in Amplifiermentioning

confidence: 99%

“…This is also the method adopted in previously reported works. For example, Chen et al (2021a) used LIA based on integral averaging filters for detecting faint bearing fault signals, Chen et al (2021b) implemented LIA on Field Programmable Gate Array (FPGA) for bearing fault diagnosis and del Rosario Bautista-Morales and Patiño-L opez (2023) developed fractional harmonic LIAs for acoustic detection of bearings, all assuming that bearing characteristic frequencies can accurately represent the faults. This assumption is based on the belief that the characteristic frequencies of bearing faults are strictly linearly correlated with the rotational speed, with linear relationship determined by the geometric properties of the bearing itself.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Chen et al. (2021a) used LIA based on integral averaging filters for detecting faint bearing fault signals, Chen et al. (2021b) implemented LIA on Field Programmable Gate Array (FPGA) for bearing fault diagnosis and del Rosario Bautista-Morales and Patiño-López (2023) developed fractional harmonic LIAs for acoustic detection of bearings, all assuming that bearing characteristic frequencies can accurately represent the faults.…”

Section: Introductionmentioning

confidence: 99%

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Lock-in spectrum: a tool for representing long-term evolution of bearing fault in the time–frequency domain using vibration signal

Zhang

2024

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Purpose This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency. Design/methodology/approach The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals. Findings Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition. Originality/value The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.

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Short-Time Variance Providing Evidential Reference Frequency for Lock-in Amplifier in Fault Diagnosis of Rolling Bearings

Zhang

2024

J. Vib. Eng. Technol.

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A Method of Detecting Bearing Fault Signal Based on DLIA Implemented by FPGA

Cited by 3 publications

References 17 publications

MKurt-LIA: mechanical fault vibration signal measurement scheme with frequency tracking capability for bearing condition monitoring

MKurt-LIA: mechanical fault vibration signal measurement scheme with frequency tracking capability for bearing condition monitoring

Lock-in spectrum: a tool for representing long-term evolution of bearing fault in the time–frequency domain using vibration signal

Short-Time Variance Providing Evidential Reference Frequency for Lock-in Amplifier in Fault Diagnosis of Rolling Bearings

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