Multi-step support vector regression and optimally parameterized wavelet packet transform for machine residual life prediction
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Journal article
- Relation: JVC/Journal of Vibration and Control Vol. 19, no. 7 (2013), p. 963-974
- Full Text: false
- Reviewed:
- Description: Condition based maintenance (CBM) in the process industry helps in determining the residual life of equipment, avoiding sudden breakdown and facilitating the maintenance staff to schedule repairs by optimizing demand–supply relationships. One of the prevalent issues in CBM is to predict the residual life of the equipment. This paper proposes a novel framework to predict the remnant life of the equipment, called residual life prediction, based on optimally parameterized wavelet transform and multi-step support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications; decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature–signal energy contents. The prediction model is based on multi-step support vector regression to determine the nonlinear crack propagation in the rotary machine according to Paris’s fatigue model. The results both for the simulated as well as the actual vibration datasets validate the enhanced performance of RWMS in comparison with the existing techniques to predict the residual life of the equipment.
Mobile agent based artificial immune system for machine condition monitoring
- Authors: Hua, Xue-Liang , Gondal, Iqbal , Yaqub, Farrukh
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 8th IEEE Conference on Industrial Electronics and Applications (ICIEA) p. 108-113
- Full Text: false
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- Description: Machine condition monitoring is a process of continuously observing the status of a machine to ensure that proactive measures are taken to prevent damage due to abnormal operating conditions. Generally, industrial units such as mining, oil and gas etc, are located in geographically remote areas, so a large amount of data need to be acquired for fault diagnosis and prognosis remotely. To achieve this, certain resources such as stable communication network and adequate bandwidth are required. Furthermore, it is not always feasible to dispatch human resources simultaneously over large areas of operation to perform on-site maintenance. To overcome these issues, a mobile agent based system architecture is proposed for machine condition monitoring by imitating human immune system (ACMIS), which is also known as artificial immune system. The experiment results are presented to evaluate the performance of the proposed system in terms of fault detection accuracy and bandwidth allocation. Overall performance evaluation of the proposed framework suggests that our proposed scheme not only provides excellent fault detection accuracy but also a flexible and reliable machine condition monitoring system with reduced network and computational resources. Further our approach provides cost effective solution in building a practical machine condition monitoring system.
Optimally parameterized wavelet packet transform for incipient machine fault diagnosis
- Authors: Yaqub, Muhammad Farrukh , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Conference paper
- Relation: 6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011
- Full Text: false
- Reviewed:
- Description: Vibration signals used for abnormality detection in machine health monitoring (MHM) are non-stationary in nature. Wavelet packet transform is extensively used in the literature for comprehensive analysis of non-stationary vibration signal but these techniques work only for a specific application lacking in some generalized methodology for selecting appropriate wavelet decomposition level and nodes for optimal performance. This study proposes a framework for inchoate fault detection by selecting the optimal wavelet decomposition level and nodes, named Optimally Parameterized Wavelet Packet Transform (OPWPT). OPWPT uses support vector machine to build the fault diagnostic model. Results in comparison with the existing schemes validate that OPWPT enhances the fault detection accuracy significantly in case of incipient faults when vibration signatures are very weak and overall signal to noise ratio is very poor.
Multi-size-window spectral augmentation: Neural network bearing fault classifier
- Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 8th IEEE Conference on Industrial Electronics and Applications (ICIEA) p. 261-266
- Full Text: false
- Reviewed:
- Description: Features extraction has always been crucial in rotary machines for Condition based machine health monitoring. Time-domain-segmentation being among the preliminary steps for further classification process plays a momentous role. Vibration signals from bearing are quasistationary in nature therefore calculation of constituent frequencies amplitudes in the vibration signal is dependent upon time-segmentation-window size. The proposed research confers the effects of time-segmentation window size on spectral features amplitudes calculation and its impacts on classification accuracy of the Artificial Neural Network (ANN). Using multi-size time-segmentation-window, for comprehensive spectral features calculation, ANN pattern classifier has been trained for enhanced classification. ANN learning assigns importance based relative weights to the links using supervised learning. Experimental results have shown that multi-size-window spectral features for ANN fault classifier perform efficiently for quasi-stationary bearing vibrations.
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.
A smart priority-based traffic control system for emergency vehicles
- Authors: Karmakar, Gour , Chowdhury, Abdullahi , Kamruzzaman, Joarder , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15849-15858
- Full Text: false
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- Description: Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV's travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly. © 2001-2012 IEEE.