Abrasion modeling of multiple-point defect dynamics for machine condition monitoring
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Loparo, Kenneth
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 171-182
- Full Text: false
- Reviewed:
- Description: Multiple-point defects and abraded surfaces in rotary machinery induce complex vibration signatures, and have a tendency to mislead defect diagnosis models. A challenging problem in machine defect diagnosis is to model and study defect signature dynamics in the case of multiple-point defects and surface abrasion. In this study, a multiple-point defect model (MPDM) that characterizes the dynamics of n-point bearing defects is proposed. MPDM is further extended to model degradation in a rotating machine as a special case of multiple-point defects. Analytical and experimental results for multiple-point defects and abrasions show that the location of the fundamental defect frequency shifts depending upon the relative location of the defects and width of the abrasive region. This variation in the defect frequency results in a degradation of the defect detection accuracy of the defect diagnostic model. Based on envelope detection analysis, a modification in existing defect diagnostic models is recommended to nullify the impact of multiple-point defects, and general abrasion in machine components.
Smart phone based vehicle condition monitoring
- Authors: Yaqub, Muhammad Farrukh , Gondal, Iqbal
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 8th IEEE Conference on Industrial Electronics and Applications (ICIEA) p. 267-271
- Full Text: false
- Reviewed:
- Description: Condition monitoring (CM) of the industrial equipment is growing auspiciously since the last decade or so. Whereas, very little efforts have been exerted on monitoring the vehicles that we ride every day. One of the main reasons is the actual cost of the CM equipment. Today an average level vibration based condition monitoring unit costs around the total price of the vehicle. Thanks to the advancement in the smart phone technology which provides a broad range of sensors and remarkable computational power in a small handheld devices. Owing to the capability of the smartphones to capture the vibrations using an internal built-in accelerometer, this paper proposes a cost effective vibration condition monitoring unit for the motor vehicles. The accelerometer in the smart phone has very limited capacity in terms sampling rate for the data acquisition. This paper proposes an enhanced sampling rate (ESR) technique for capturing the data at an improved sampling rate in spite of device limitation. Though a lot needs to be done both in terms of hardware optimization and fault diagnosis, the focus of this paper is to achieve an efficient data acquisition using smartphone. Experimental results are presented both for the simulated as well actual vibration datasets which validate the practicality and suitability of the proposed technique.
Machine fault severity estimation based on adaptive wavelet nodes selection and SVM
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Conference paper
- Relation: IEEE International Conference on Mechatronics and Automation (ICMA),Beijing 7 August 2011 to 10 August 2011) p. 1951-1956
- Full Text: false
- Reviewed:
- Description: The study is focused on estimating the severity level of the bearing faults which helps in determining the residual life of the equipment and planned maintenance. A novel technique, adaptive severity estimation model (ASEM) is proposed based on adaptive selection of wavelet decomposition nodes and support vector machines. Vibration data from multiple severity levels are used to build the fault estimation model. An adaptive criterion for wavelet decomposition node selection is developed which helps ASEM to achieve robustness in estimating fault severity under varying signal to noise ratio (SNR), a key demand in industrial environment. The simulated data with known severity level is used to parameterize the estimation model. The fault severity estimation performance of ASEM is also validated for the real vibration data and its robustness is gauged under varying SNR conditions.
Resonant frequency band estimation using adaptive wavelet decomposition level selection
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Conference paper
- Relation: 2011 IEEE International Conference on Mechatronics and Automation (ICMA) p. 376-381
- Full Text: false
- Reviewed:
- Description: The vibrations induced by machine faults help in diagnosis and prognosis of the machine. It is crucial for the fault diagnostic system to extract resonant frequency band which carries useful information about the defect frequencies and contains maximum signal to noise ratio. The spectral orientation of the resonant frequency band varies with the variation in machine dynamics. The existing techniques which employ wavelet transformation to exploit the signal energy distribution among different frequency sub-bands, are based on fixed decomposition level and do not optimize the wavelet parameters according to varying machine dynamics. The proposed study develops a novel technique: Adaptive Wavelet Decomposition and Resonance Frequency Estimation (AWRE) which estimates the positioning of the resonant frequency band based on adaptive selection of the wavelet decomposition levels. The results for the simulated as well as actual vibration data demonstrate that the proposed technique estimates the bandwidth of the resonant frequency band quite effectively.