The Experts below are selected from a list of 1974 Experts worldwide ranked by ideXlab platform
Michael Pecht - One of the best experts on this subject based on the ideXlab platform.
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Health Assessment of Cooling Fan Bearings Using Wavelet-Based Filtering
Sensors, 2012Co-Authors: Qiang Miao, Wei Liang, Chao Tang, Michael PechtAbstract:As commonly used forced convection air cooling devices in electronics, cooling Fans are crucial for guaranteeing the reliability of electronic systems. In a cooling Fan assembly, Fan Bearing failure is a major failure mode that causes excessive vibration, noise, reduction in rotation speed, locked rotor, failure to start, and other problems; therefore, it is necessary to conduct research on the health assessment of cooling Fan Bearings. This paper presents a vibration-based Fan Bearing health evaluation method using comblet filtering and exponentially weighted moving average. A new health condition indicator (HCI) for Fan Bearing degradation assessment is proposed. In order to collect the vibration data for validation of the proposed method, a cooling Fan accelerated life test was conducted to simulate the lubricant starvation of Fan Bearings. A comparison between the proposed method and methods in previous studies (i.e., root mean square, kurtosis, and fault growth parameter) was carried out to assess the performance of the HCI. The analysis results suggest that the HCI can identify incipient Fan Bearing failures and describe the Bearing degradation process. Overall, the work presented in this paper provides a promising method for Fan Bearing health evaluation and prognosis.
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Fan Bearing fault diagnosis based on continuous wavelet transform and autocorrelation
Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), 2012Co-Authors: Lei Xie, Qiang Miao, Yi Chen, Wei Liang, Michael PechtAbstract:Cooling Fan is commonly used in electronic system, and it is crucial for guarantying the reliability of it. In a cooling Fan assembly, Fan Bearing failure is a major part which causes noise, vibration; reduction in rotation speed, or even locks the motor. It is necessary to conduct research on the life assessment of Fan Bearings. This paper presents a vibration-based Fan Bearing prognosis and health evaluation through continuous-wavelet transform with ACFIs. The autocorrelation function indicator (ACFI) was used for determining the coefficients of continuous-wavelet transform (CWT). A cooling Fan accelerated life test was conducted to collect the lifetime vibration data of Fan Bearing. In addition, comparisons between the frequency spectrum of the proposed method and some commonly used methods were conducted to evaluate performance of this method. The work presented in this paper provides a promising way for finding the fault characteristic frequencies and detecting the work state of Fan Bearing.
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Cooling Fan Bearing fault identification using vibration measurement
2011 IEEE Conference on Prognostics and Health Management, 2011Co-Authors: Qiang Miao, Michael Azarian, Michael PechtAbstract:As a commonly used assembly in computer cooling systems, the normal operation of a cooling Fan is critical for guaranteeing system stability and reducing damage to electronic components. Reliability analyses have shown that Fan Bearing failure is a major failure mode. Therefore, it is necessary to conduct research on fault detection of cooling Fan Bearings. In this paper we propose vibration-based Fan Bearing fault detection through the wavelet transform and the Hilbert transform. An experiment on Fan Bearings was conducted to collect vibration data for the validation of our proposed method. The analysis results show that the proposed method can identify different Bearing faults.
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Rolling element Bearing fault detection: Combining energy operator demodulation and wavelet packet transform
2011 Prognostics and System Health Managment Confernece, 2011Co-Authors: Chao Tang, Qiang Miao, Michael PechtAbstract:Rolling element Bearing vibration signal is a modulated signal, and the existence of modulation increases the difficulty in Bearing fault detection. Hilbert transform has been widely used in signal demodulation. However, the endpoint effect often makes the result unsatisfactory. Further, Bearing vibration signals are usually polluted by various noises at low frequency, and the analysis of high frequency can be more effective for Bearing fault feature extraction. This paper proposes an improved method that combines the energy operator demodulation and the dual reconstruction scheme in wavelet packet transform. A Fan Bearing test rig is established and the vibration signals collected from this test rig are used to validate the proposed method. The analysis results show that the proposed method has a good frequency resolution.
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Physics-of-failure analysis of cooling Fans
2011 Prognostics and System Health Managment Confernece, 2011Co-Authors: Xiaohang Jin, Michael H. Azarian, Chunpiu Lau, L. L. Cheng, Michael PechtAbstract:The use of cooling Fans is a low-cost solution for thermal management in electronic products. Cooling Fans are used to create designed airflow to cool down the host system. Since many failures in the field can be traced back to thermally-related issues in electronic products, the reliability of cooling Fans is a critical part of the overall reliability of thermal management systems in electronic products. When cooling Fans fail to work at the designed operating point, the host system will undergo overheating, which will degrade its performance and lead to intermittent failures and catastrophic failures. There is growing interest in the prognostics and health management (PHM) of cooling Fans in electronic products. This paper works on the implementation of failure modes, mechanisms and effects analysis (FMMEA) of cooling Fans in electronic products, and discusses three situations in which cooling Fans can fail to provide the designed airflow to cool down their host systems. An overview of the cooling Fan Bearing is provided, and Bearing reliability issues are also reported. Finally, the prioritization of potential failure mechanisms was performed with the estimation of risk priority numbers based on past experience and engineering judgment.
Qiang Miao - One of the best experts on this subject based on the ideXlab platform.
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Condition multi-classification and evaluation of system degradation process using an improved support vector machine
Microelectronics Reliability, 2017Co-Authors: Qiang Miao, Xin Zhang, Zhiwen Liu, Heng ZhangAbstract:Abstract Degradation process is a non-negligible phenomenon in system condition monitoring and reliability practices. Traditional binary-state characterization (i.e., normal and failure) on system health condition may not provide accurate information for maintenance scheduling, and the multi-state classification for degradation process is a necessary step to realize cost-effective condition based maintenance. Support vector machine (SVM) is a useful technique for system condition monitoring and fault diagnosis. However, the SVM classification accuracy of deteriorating system is usually poor, because characteristics of different degradation states may not be very distinctive. This paper presented an improved support vector machine for system degradation classification and evaluation. The core of the proposed method can be summarized as: an improved voting scheme in one-against-one SVM, and an optimization of classification process by utilizing inherent physical property of system state transition. A case study of cooling Fan Bearing accelerated life time test is conducted to obtain the experimental data, and a comparison before and after optimization shows that the proposed method improves the classification accuracy.
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Health Assessment of Cooling Fan Bearings Using Wavelet-Based Filtering
Sensors, 2012Co-Authors: Qiang Miao, Wei Liang, Chao Tang, Michael PechtAbstract:As commonly used forced convection air cooling devices in electronics, cooling Fans are crucial for guaranteeing the reliability of electronic systems. In a cooling Fan assembly, Fan Bearing failure is a major failure mode that causes excessive vibration, noise, reduction in rotation speed, locked rotor, failure to start, and other problems; therefore, it is necessary to conduct research on the health assessment of cooling Fan Bearings. This paper presents a vibration-based Fan Bearing health evaluation method using comblet filtering and exponentially weighted moving average. A new health condition indicator (HCI) for Fan Bearing degradation assessment is proposed. In order to collect the vibration data for validation of the proposed method, a cooling Fan accelerated life test was conducted to simulate the lubricant starvation of Fan Bearings. A comparison between the proposed method and methods in previous studies (i.e., root mean square, kurtosis, and fault growth parameter) was carried out to assess the performance of the HCI. The analysis results suggest that the HCI can identify incipient Fan Bearing failures and describe the Bearing degradation process. Overall, the work presented in this paper provides a promising method for Fan Bearing health evaluation and prognosis.
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Cooling Fan Bearing diagnosis based on AR& MED
2012 International Conference on Quality Reliability Risk Maintenance and Safety Engineering, 2012Co-Authors: Chaoqin Liu, Wei Liang, Xue Zhou, Shuai Yang, Qiang MiaoAbstract:To monitor the initial failure of cooling Fan's rolling Bearing, this paper reviews the theory of autoregressive (AR) model and the minimum entropy deconvolution (MED) filtering technique. The AR method can remove the deterministic components of the original signal, and the MED filter could reduce the effect of the transmission path. These two filtering techniques were combined in this paper to pre-process the rolling Bearing's vibration signal, and then the envelope spectrum of the residual signal was analyzed. The method leads to efficient filtering result.
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Fan Bearing fault diagnosis based on continuous wavelet transform and autocorrelation
Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), 2012Co-Authors: Lei Xie, Qiang Miao, Yi Chen, Wei Liang, Michael PechtAbstract:Cooling Fan is commonly used in electronic system, and it is crucial for guarantying the reliability of it. In a cooling Fan assembly, Fan Bearing failure is a major part which causes noise, vibration; reduction in rotation speed, or even locks the motor. It is necessary to conduct research on the life assessment of Fan Bearings. This paper presents a vibration-based Fan Bearing prognosis and health evaluation through continuous-wavelet transform with ACFIs. The autocorrelation function indicator (ACFI) was used for determining the coefficients of continuous-wavelet transform (CWT). A cooling Fan accelerated life test was conducted to collect the lifetime vibration data of Fan Bearing. In addition, comparisons between the frequency spectrum of the proposed method and some commonly used methods were conducted to evaluate performance of this method. The work presented in this paper provides a promising way for finding the fault characteristic frequencies and detecting the work state of Fan Bearing.
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Cooling Fan Bearing fault identification using vibration measurement
2011 IEEE Conference on Prognostics and Health Management, 2011Co-Authors: Qiang Miao, Michael Azarian, Michael PechtAbstract:As a commonly used assembly in computer cooling systems, the normal operation of a cooling Fan is critical for guaranteeing system stability and reducing damage to electronic components. Reliability analyses have shown that Fan Bearing failure is a major failure mode. Therefore, it is necessary to conduct research on fault detection of cooling Fan Bearings. In this paper we propose vibration-based Fan Bearing fault detection through the wavelet transform and the Hilbert transform. An experiment on Fan Bearings was conducted to collect vibration data for the validation of our proposed method. The analysis results show that the proposed method can identify different Bearing faults.
Hailiang Sun - One of the best experts on this subject based on the ideXlab platform.
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Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain
Mechanical Systems and Signal Processing, 2016Co-Authors: Jinglong Chen, Hailiang Sun, Jun Pan, Binqiang Chen, Yanyang Zi, Zhiguo Wan, Jing Yuan, Yu Wang, Zhengjia HeAbstract:Fault identification timely of rolling mill drivetrain is significant for guaranteeing product quality and realizing long-term safe operation. So, condition monitoring system of rolling mill drivetrain is designed and developed. However, because compound fault and weak fault feature information is usually sub-merged in heavy background noise, this task still faces challenge. This paper provides a possibility for fault identification of rolling mills drivetrain by proposing customized maximal-overlap multiwavelet denoising method. The effectiveness of wavelet denoising method mainly relies on the appropriate selections of wavelet base, transform strategy and threshold rule. First, in order to realize exact matching and accurate detection of fault feature, customized multiwavelet basis function is constructed via symmetric lifting scheme and then vibration signal is processed by maximal-overlap multiwavelet transform. Next, based on spatial dependency of multiwavelet transform coefficients, spatial neighboring coefficient data-driven group threshold shrinkage strategy is developed for denoising process by choosing the optimal group length and threshold via the minimum of Stein's Unbiased Risk Estimate. The effectiveness of proposed method is first demonstrated through compound fault identification of reduction gearbox on rolling mill. Then it is applied for weak fault identification of dedusting Fan Bearing on rolling mill and the results support its feasibility.
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A data-driven threshold for wavelet sliding window denoising in mechanical fault detection
Science China-technological Sciences, 2014Co-Authors: Chen Yimin, Hongrui Cao, Hailiang SunAbstract:Wavelet denoising is an effective approach to extract fault features from strong background noise. It has been widely used in mechanical fault detection and shown excellent performance. However, traditional thresholds are not suitable for nonstationary signal denoising because they set universal thresholds for different wavelet coefficients. Therefore, a data-driven threshold strategy is proposed in this paper. First, the signal is decomposed into different subbands by wavelet transformation. Then a data-driven threshold is derived by estimating the noise power spectral density in different subbands. Since the data-driven threshold is dependent on the noise estimation and adapted to data, it is more robust and accurate for denoising than traditional thresholds. Meanwhile, sliding window method is adopted to set a flexible local threshold. When this method was applied to simulation signal and an inner race fault diagnostic case of dedusting Fan Bearing, the proposed method has good result and provides valuable advantages over traditional methods in the fault detection of rotating machines.
Chao Tang - One of the best experts on this subject based on the ideXlab platform.
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Health Assessment of Cooling Fan Bearings Using Wavelet-Based Filtering
Sensors, 2012Co-Authors: Qiang Miao, Wei Liang, Chao Tang, Michael PechtAbstract:As commonly used forced convection air cooling devices in electronics, cooling Fans are crucial for guaranteeing the reliability of electronic systems. In a cooling Fan assembly, Fan Bearing failure is a major failure mode that causes excessive vibration, noise, reduction in rotation speed, locked rotor, failure to start, and other problems; therefore, it is necessary to conduct research on the health assessment of cooling Fan Bearings. This paper presents a vibration-based Fan Bearing health evaluation method using comblet filtering and exponentially weighted moving average. A new health condition indicator (HCI) for Fan Bearing degradation assessment is proposed. In order to collect the vibration data for validation of the proposed method, a cooling Fan accelerated life test was conducted to simulate the lubricant starvation of Fan Bearings. A comparison between the proposed method and methods in previous studies (i.e., root mean square, kurtosis, and fault growth parameter) was carried out to assess the performance of the HCI. The analysis results suggest that the HCI can identify incipient Fan Bearing failures and describe the Bearing degradation process. Overall, the work presented in this paper provides a promising method for Fan Bearing health evaluation and prognosis.
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Rolling element Bearing fault detection: Combining energy operator demodulation and wavelet packet transform
2011 Prognostics and System Health Managment Confernece, 2011Co-Authors: Chao Tang, Qiang Miao, Michael PechtAbstract:Rolling element Bearing vibration signal is a modulated signal, and the existence of modulation increases the difficulty in Bearing fault detection. Hilbert transform has been widely used in signal demodulation. However, the endpoint effect often makes the result unsatisfactory. Further, Bearing vibration signals are usually polluted by various noises at low frequency, and the analysis of high frequency can be more effective for Bearing fault feature extraction. This paper proposes an improved method that combines the energy operator demodulation and the dual reconstruction scheme in wavelet packet transform. A Fan Bearing test rig is established and the vibration signals collected from this test rig are used to validate the proposed method. The analysis results show that the proposed method has a good frequency resolution.
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Rolling Element Bearing Fault Detection: Combining
2011Co-Authors: Chao Tang, Qiang Miao, Michael PechtAbstract:Rolling element Bearing vibration signal is a modulated signal, and the existence of modulation increases the difficUlty in Bearing fault detection. Hilbert transform has been widely used in signal demodulation. However, the endpoint effect often makes the result unsatisfactory. Further, Bearing vibration signals are usually polluted by various noises at low frequency, and the analysis of high frequency can be more effective for Bearing fault feature extraction. This paper proposes an improved method that combines the energy operator demodulation and the dual reconstruction scheme in wavelet packet transform. A Fan Bearing test rig is established and the vibration signals collected from this test rig are used to validate the proposed method. The analysis results show that the proposed method has a good frequency resolution.
Zhengjia He - One of the best experts on this subject based on the ideXlab platform.
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Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain
Mechanical Systems and Signal Processing, 2016Co-Authors: Jinglong Chen, Hailiang Sun, Jun Pan, Binqiang Chen, Yanyang Zi, Zhiguo Wan, Jing Yuan, Yu Wang, Zhengjia HeAbstract:Fault identification timely of rolling mill drivetrain is significant for guaranteeing product quality and realizing long-term safe operation. So, condition monitoring system of rolling mill drivetrain is designed and developed. However, because compound fault and weak fault feature information is usually sub-merged in heavy background noise, this task still faces challenge. This paper provides a possibility for fault identification of rolling mills drivetrain by proposing customized maximal-overlap multiwavelet denoising method. The effectiveness of wavelet denoising method mainly relies on the appropriate selections of wavelet base, transform strategy and threshold rule. First, in order to realize exact matching and accurate detection of fault feature, customized multiwavelet basis function is constructed via symmetric lifting scheme and then vibration signal is processed by maximal-overlap multiwavelet transform. Next, based on spatial dependency of multiwavelet transform coefficients, spatial neighboring coefficient data-driven group threshold shrinkage strategy is developed for denoising process by choosing the optimal group length and threshold via the minimum of Stein's Unbiased Risk Estimate. The effectiveness of proposed method is first demonstrated through compound fault identification of reduction gearbox on rolling mill. Then it is applied for weak fault identification of dedusting Fan Bearing on rolling mill and the results support its feasibility.
