Bearing Fault Diagnosis Based on Fault Characteristic Trend Template

The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.

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An intelligent method of roller bearing fault diagnosis and fault characteristic frequency visualization based on improved MobileNet V3

Measurement Science and Technology ◽

10.1088/1361-6501/ac27ea ◽

2021 ◽

Vol 32 (12) ◽

pp. 124009

Author(s):

Dechen Yao ◽

Guanyi Li ◽

Hengchang Liu ◽

Jianwei Yang

Keyword(s):

Fault Diagnosis ◽

Characteristic Frequency ◽

Roller Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Fault Characteristic

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Rolling Bearing Fault Diagnosis with EMD-Based Fault Characteristic Frequency Difference Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.437 ◽

2014 ◽

Vol 596 ◽

pp. 437-441 ◽

Cited By ~ 2

Author(s):

Yan Ping Guo ◽

Yu Xiong ◽

Guo Cui Song

Keyword(s):

Fault Diagnosis ◽

Single Point ◽

Vibration Signal ◽

Rolling Bearing ◽

Test Bed ◽

Vibration Measurements ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Vibration Signal Analysis ◽

Fault Characteristic

This paper presents a novel single-point rolling bearing fault diagnosis mechanism through vibration signal analysis. It is highlighted that the rolling bearing operational state can be well estimated by the first small set of Intrinsic Mode Function (IMF) components of the original vibration measurements through Empirical Mode Decomposition (EMD). These IMF components can be further translated into envelope spectrum by using Hilbert Transform. As a result, the difference of fault characteristic frequencies (DFCF) is derived to properly characterize different fault patterns for fault diagnosis. The suggested method is implemented and evaluated in a rolling bearing test bed for a range of failure scenarios (e.g. inner and outer raceway fault, rolling elements fault) with extensive vibration measurements. The result demonstrates that the proposed solution is effective for characterizing and detecting arrange of rolling bearing faults.quality).

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Algorithm for Multiple Time-Frequency Curve Extraction From Time-Frequency Representation of Vibration Signals for Bearing Fault Diagnosis Under Time-Varying Speed Conditions

Volume 8: 29th Conference on Mechanical Vibration and Noise ◽

10.1115/detc2017-67171 ◽

2017 ◽

Cited By ~ 2

Author(s):

Huan Huang ◽

Natalie Baddour ◽

Ming Liang

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Time Varying ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Order Tracking ◽

Frequency Representation ◽

Bearing Vibration ◽

Fault Characteristic

Bearing fault diagnosis under constant operational condition has been widely investigated. Monitoring the bearing vibration signal in the frequency domain is an effective approach to diagnose a bearing fault since each fault type has a specific Fault Characteristic Frequency (FCF). However, in real applications, bearings are often running under time-varying speed conditions which makes the signal non-stationary and the FCF time-varying. Order tracking is a commonly used method to resample the non-stationary signal to a stationary signal. However, the accuracy of order tracking is affected by many factors such as the precision of the measured shaft rotating speed and the interpolation methods used. Therefore, resampling-free methods are of interest for bearing fault diagnosis under time-varying speed conditions. With the development of Time-Frequency Representation (TFR) techniques, such as the Short-Time Fourier Transform (STFT) and wavelet transform, bearing fault characteristics can be shown in the time-frequency domain. However, for bearing fault diagnosis, instantaneous time-frequency characteristics, i.e. Time-Frequency (T-F) curves, have to be extracted from the TFR. In this paper, an algorithm for multiple T-F curve extraction is proposed based on a path-optimization approach to extract T-F curves from the TFR of the bearing vibration signal. The bearing fault can be diagnosed by matching the curves to the Instantaneous Fault Characteristic Frequency (IFCF) and its harmonics. The effectiveness of the proposed algorithm is validated by experimental data collected from a faulty bearing with an outer race fault and a faulty bearing with an inner race fault, respectively.

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Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Mathematical Problems in Engineering ◽

10.1155/2016/8139273 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Siliang Lu ◽

Xiaoxian Wang ◽

Fang Liu ◽

Qingbo He ◽

Yongbin Liu ◽

...

Keyword(s):

Fault Diagnosis ◽

Video Clip ◽

Health Condition ◽

Motor Shaft ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Fault Type ◽

Series Of Experiments ◽

Fault Characteristic ◽

Motor Bearing

Conventional bearing fault diagnosis methods require specialized instruments to acquire signals that can reflect the health condition of the bearing. For instance, an accelerometer is used to acquire vibration signals, whereas an encoder is used to measure motor shaft speed. This study proposes a new method for simplifying the instruments for motor bearing fault diagnosis. Specifically, a video clip recording of a running bearing system is captured using a cellphone that is equipped with a camera and a microphone. The recorded video is subsequently analyzed to obtain the instantaneous frequency of rotation (IFR). The instantaneous fault characteristic frequency (IFCF) of the defective bearing is obtained by analyzing the sound signal that is recorded by the microphone. The fault characteristic order is calculated by dividing IFCF by IFR to identify the fault type of the bearing. The effectiveness and robustness of the proposed method are verified by a series of experiments. This study provides a simple, flexible, and effective solution for motor bearing fault diagnosis. Given that the signals are gathered using an affordable and accessible cellphone, the proposed method is proven suitable for diagnosing the health conditions of bearing systems that are located in remote areas where specialized instruments are unavailable or limited.

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