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Multi-size-window spectral augmentation: Neural network bearing fault classifier

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • 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
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  • 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.

Multi-step support vector regression and optimally parameterized wavelet packet transform for machine residual life prediction

- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • 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
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  • 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.

On temporal order invariance for view-invariant action recognition

- Ul-Haq, Anwaar, Gondal, Iqbal, Murshed, Manzur

  • Authors: Ul-Haq, Anwaar , Gondal, Iqbal , Murshed, Manzur
  • Date: 2013
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Circuits and Systems for Video Technology Vol. 23, no. 2 (2013), p. 203-211
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  • Description: View-invariant action recognition is one of the most challenging problems in computer vision. Various representations are being devised for matching actions across different viewpoints to achieve view invariance. In this paper, we explore the invariance property of temporal order of action instances during action execution and utilize it for devising a new view-invariant action recognition approach. To ensure temporal order during matching, we utilize spatiotemporal features, feature fusion and temporal order consistency constraint. We start by extracting spatiotemporal cuboid features from video sequences and applying feature fusion to encapsulate within-class similarity for the same viewpoints. For each action class, we construct a feature fusion table to facilitate feature matching across different views. An action matching score is then calculated based on global temporal order constraint and number of matching features. Finally, the action label of the class with the maximum value of the matching score is assigned to the query action. Experimentation is performed on multiple view Inria Xmas motion acquisition sequences and West Virginia University action datasets, with encouraging results, that are comparable to the existing view-invariant action recognition techniques.

Regularly frequent patterns mining from sensor data stream

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: International Conference on Neural Information Processing (ICONIP 2013) p. 417-424
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  • Description: Mining interesting and useful knowledge from the huge amount of data gathered in wireless sensor networks is a challenging task. Works reported in literature use support metric-based sensor association rule which employs the occurrence frequency of patterns as criteria. Such criteria may not be appropriate for finding significant patterns. Moreover, temporal regularity in occurrence behavior should be considered as another important measure for assessing the importance of patterns in WSNs. Frequent sensor patterns that occur after regular intervals is called regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely important in many real-time applications, no such algorithm has been proposed yet. In this paper, we propose a novel tree structure called Regularly Frequent Sensor Pattern-tree (RSP-tree) and an efficient mining approach for finding regularly frequent sensor patterns from WSNs. Extensive performance analyses show that our technique is time and memory efficient in finding regularly frequent sensor patterns.

Smart phone based vehicle condition monitoring

- Yaqub, Muhammad Farrukh, Gondal, Iqbal

  • 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
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  • 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.

Social-connectivity-aware vertical handover for heterogeneous wireless networks

- Haider, Ammar, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Haider, Ammar , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Journal Of Network And Computer Applications Vol. 36, no. 4 (2013), p. 1131-1139
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  • Description: Vertical handover mechanism for a WLAN-cellular heterogeneous network could be made efficient with the use of context aware admission control strategy. Existing admission control methods aim to provide satisfactory quality of service, but rely solely on the availability of wireless resources in the target network. We propose that the admission control in WLAN should make use of social connectivity context of users in its coverage area to classify local and global traffic. In this paper, we introduce a novel Social-Connectivity-aware Vertical Handover (SCVH) scheme, which performs admission control using connectivity graph data from the online social networking services. A higher importance of visiting node for users resident in WLAN, advocates a higher priority for granting admission. We employ graph-theoretic concept of centrality to calculate the social importance of potential handing-over nodes. By giving handover precedence to higher-centrality nodes, we achieve an optimal allocation of wireless resources in addition to improved quality of service. The proposed handover strategy offers an additional advantage of reducing global social network traffic.

Dynamic clusters graph for detecting moving targets using WSNs

- Armaghani, Farzaneh, Gondal, Iqbal, Kamruzzaman, Joarder, Green, David

  • Authors: Armaghani, Farzaneh , Gondal, Iqbal , Kamruzzaman, Joarder , Green, David
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 76th IEEE Vehicular Technology Conference, VTC Fall 2012; Quebec City, Canada; 3rd-6th September 2012 p. 1-5
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  • Description: Efficient target tracking applications require active sensor nodes to track a cluster of moving targets. Clustering could lead to significant cost improvement as compared to tracking individual targets. This paper presents accurate clustering of targets for both coherent and incoherent movement patterns. We propose a novel clustering algorithm that utilises an implicit dynamic time frame to assess the relational history of targets in creating a weighted graph of connected components. The proposed algorithm employs key features of localisation algorithms in target tracking, namely, estimated current and predicted locations to determine the relational directions and distances of moving targets. Our simulation results show a significant improvement on the clustering accuracy and computation time by dynamically adjusting the history-window size and predicting the relationships among targets.

Dynamic sensors collaboration to balance the accuracy-lifetime trade-off in multiple-target tracking

- Armaghani, Farzaneh, Gondal, Iqbal, Kamruzzaman, Joarder, Green, David

Dynamic sensors selection for overlapped multiple-target tracking using eagerness

- Armaghani, Farzaneh, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Armaghani, Farzaneh , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 76th IEEE Vehicular Technology Conference, VTC Fall 2012; Quebec City, Canada; 3rd-6th September 2012 p. 1-6
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  • Description: Efficient target tracking applications use active sensor nodes collaboratively to track multiple moving targets by balancing the trade-off between the quality of tracking and network's lifetime. In this paper, we propose a low-energy dynamic sensor selection (LEDS) scheme to track multiple targets by estimating energy consumption of sensors and information utility projection of the targets on sensors to calculate the eagerness in tracking. Eagerness represents the eligibility of a sensor node to be selected for tracking, considering relative profiles of other sensors and location of all the targets in its vicinity. LEDS enables an even distribution of energy consumption among the nodes to prolong their remaining energies. Our results show that the proposed scheme can significantly improve the network lifetime over the existing methods while maintaining the high tracking accuracy in congested areas where multiple concurrent targets overlap.

Impact characterization of multiple-points-defect on machine fault diagnosis

- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 2012 IEEE International Conference on Automation Science and Engineering: Green Automation Toward a Sustainable Society, CASE 2012; Seoul, South Korea; 20th-24th August 2012 p. 479-484
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  • Description: Multiple points fault in the rotary machinery induce complex vibration signatures, which have the tendency to mislead the fault diagnostic models. One of the challenging problems in machine fault diagnosis is to model and study fault signatures dynamics in case of multiple points fault. The existing literature lacks in the study of multiple points fault and the associated vibration signatures. In this study, a multiple-points defect model (MPDM) is proposed which can formulate n-points bearing fault signature's dynamics. Impact of multiple points defect on the well-established state-of-the-art fault diagnostic models is quantified in terms of fault detection accuracy. Results for fault detection accuracy are obtained using Support Vector Machine (SVM) and a modification is recommended in the existing fault diagnostic models in order to nullify the impact of multiple-points fault.

Machine health monitoring based on stationary wavelet transform and fourth-order cumulants

- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2012
  • Type: Text , Journal article
  • Relation: International Review of Electrical Engineering Vol. 6, no. 1 (2012), p. 238-248
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  • Description: Early stage faults detection for machine health monitoring demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR). Vibration signal which is used for signature matching in case of abnormality detection and diagnosis, requires robust tools such as wavelet transform (WT) for time-frequency analysis. WT is specifically used to deal with nonstationary signals. In order to guarantee improved performance under poor SNR, this paper proposes a scheme for feature extraction based on fourth-order cumulant and stationary wavelet transform (FoCSWT). Higher order cumulants have the tendency to mitigate the impact of Gaussian noise. Fourth-order cumulant corresponds to the "peakedness" of the random distribution and the fault detection capability quantifies it as the most dominant cumulant among higher order statistics. Stationary wavelet transform is used to avoid down-sampling on the vibration data prior to feature extraction which gives better estimation of statistical parameters of the data distribution and gives performance enhancement in terms of fault classification accuracy. Simulation studies show that FoCSWT outperforms the existing techniques in terms of fault detection accuracies under poor SNR.

Priority based expansion of neighborhood size for heterogeneous traffic routing in WSN

- Rizal, Muhammad Nur, Gondal, Iqbal, Haghighi, P. Delir, Qiu, Bin

  • Authors: Rizal, Muhammad Nur , Gondal, Iqbal , Haghighi, P. Delir , Qiu, Bin
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 9th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, PE-WASUN 2012 p. 101-106
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  • Description: This paper presents a new routing scheme that adapts to the requirements of heterogeneous traffic. The proposed scheme introduces further improvements to our developed ExtTeGAR algorithm. The aim of the scheme is to achieve low latency and to satisfy multiple quality of service (QoS) in various network conditions. In doing so, the proposed approach exploits multiple nodes’ attributes such as energy, distance to the base station (BS), history of hop delay of data to neighbors and probability of link availability to determine the best node for delivering different traffic requirements. The contribution of the scheme is its ability to differentiate the required data-related to each type of traffic and give different priorities to different data categories so that each type of traffic classification ensures to meet the demands of the application requirements. The proposed scheme uses a distance-based and a location-aware approach to create a shortest path for type of packets with given deadlines and it provides adaptability to increase the transmission range to expand the neighborhood sizes for data delivery continuation. The paper shows that the proposed scheme outperforms an existing QoS routing algorithm (SPEED) in various application scenarios and different network sizes.

Priority based expansion of neighbourhood size for heterogeneous traffic routing in WSN

- Rizal, Muhammad Nur, Gondal, Iqbal, Delir Haghighi, Pari, Qiu, Bin

Smart phone based machine condition monitoring system

- Gondal, Iqbal, Yaqub, Muhammad, Hua, Xueliang

  • Authors: Gondal, Iqbal , Yaqub, Muhammad , Hua, Xueliang
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Neural Information Processing p. 488-497
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  • Description: Machine condition monitoring has gained momentum over the years and becoming an essential component in the today’s industrial units. A cost effective machine condition monitoring system is need of the hour for predictive maintenance. In this paper, we have developed a machine condition monitoring system using smart phone, thanks to the rapidly growing smart-phone market both in scalability and computational power. In spite of certain hardware limitations, this paper proposes a machine condition monitoring system which has the tendency to acquire data, build the fault diagnostic model and determine the type of the fault in the case of unknown fault signatures. Results for the fault detection accuracy are presented which validate the prospects of the proposed framework in future condition monitoring services.

Unitary anomaly detection for ubiquitous safety in machine health monitoring

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Neural Information Processing (INCONIP) p. 361-368
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  • Description: Safety has always been of vital concern in both industrial and home applications. Ensuring safety often requires certain quantifications regarding the inclusive behavior of the system under observation in order to determine deviations from normal behavior. In machine health monitoring, the vibration signal is of great importance for such measurements because it includes abundant information from several machine parts and surroundings that can influence machine behavior. This paper proposes a unitary anomaly detection technique (UAD) that, upon observation of abnormal behavior in the vibration signal, can trigger an alarm with an adjustable threshold in order to meet different safety requirements. The normalized amplitude of spectral contents of the quasi stationary time vibration signal are divided into frequency bins, and the summed amplitudes frequencies over bin are used as features. From a training set consisting of normal vibration signals, Gaussian distribution models are obtained for each feature, which are then used for anomaly detection.

A new resource distribution model for improved QoS in an integrated WiMAX/WiFi architecture

- Rabbani, Md, Kamruzzaman, Joarder, Gondal, Iqbal, Ahmad, Iftekhar

Action recognition using spatio-temporal distance classifier correlation filter

- Anwaar-Ul Haq, Gondal, Iqbal, Murshed, Manzur

  • Authors: Anwaar-Ul Haq , Gondal, Iqbal , Murshed, Manzur
  • Date: 2011
  • Type: Text , Conference proceedings
  • Relation: 2011 International Conference on Digital Image Computing Techniques and Applications (DICTA), Noosa, QLD, 6th-8th Dec, 2011
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  • Description: The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.
  • Description: The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like \emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a \emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.

CODAR: Congestion and delay aware routing to detect time critical events

- Bhuiyan, Mohammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Bhuiyan, Mohammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
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  • Description: Reliability and timeliness are two essential requirements of successful detection of critical events in Wireless Sensor Networks (WSNs). The base station (BS) is particularly interested about reliable and timely collection of data sent by the nodes close to the ongoing event, and at that time, the data sent by other nodes have little importance. In this paper, we propose Congestion and Delay Aware Routing (CODAR) protocol that tries to route data in congestion and delay aware manners. If congestion occurs, it also mitigates congestion by utilizing an accurate data-rate adjustment. Each node collects control information from neighbours and works in a distributed manner. CODAR also puts emphasis on successful collection of these control information which eventually provides desirable performance. Experimental results show that CODAR is capable of avoiding and mitigating congestion effectively, and performs better than similar known techniques in terms of reliable and timely event detection.

Contextual action recognition in multi-sensor nighttime video sequences

- Anwaar-Ul, Haq, Gondal, Iqbal, Murshed, Manzur

  • Authors: Anwaar-Ul, Haq , Gondal, Iqbal , Murshed, Manzur
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: Proceedings of the 2011 Digital Image Computing: Techniques and Applications (DICTA 2011), Noosa 6th-8th Dec, 2011 p. 256-261
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  • Description: Contextual information is important for interpreting human actions especially when actions exhibit interactive relationship with their context. Contextual clues become even more crucial when videos are captured in unfavorable conditions like extreme low light nighttime scenarios. These conditions encourage the use of multi-senor imagery and context enhancement. In this paper, we explore the importance of contextual knowledge for recognizing human actions in multi-sensor nighttime videos. Information fusion is utilized for encapsulating visual information about actions and their context. Space-time action information is contained using 3D fourier transform of fused action silhouette volume. In parallel, SIFT context images are extracted and fused using principal component analysis based feature fusion for each action class. Contextual dissimilarity is penalized by minimizing context SIFT flow energy. The action dataset comprises multi-sensor night vision video data from infra-red and visible spectrum. Experimental results show that fused contextual action information boost action recognition performance as compared to the baseline action recognition approac

Dual-channel based energy efficient event clustering and data gathering in WSNs

- Bhuiyan, Mohammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Bhuiyan, Mohammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: 17th Asia Pacific Conference on Communications, APCC 2011; Sabah, Malaysia; 2nd-5th October 2011 p. 241-246
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  • Description: Wireless sensor networks (WSNs), now-a-days, are deployed in environmental data collection as well as in critical event monitoring. Successful data collection requires reliability while reliable event detection necessitates timeliness. Simultaneous data gathering and event monitoring is not well studied in literature. In this paper, we propose a system model that works on homogeneous data gathering WSNs. When an event occurs, an event cluster with a different transmission channel is formed and both data gathering and event monitoring are performed at the same time. The proposed model has a novel routing strategy with a built-in congestion control technique to provide timely delivery of event data. Experimental results show that the proposed method performs better than known similar techniques in terms of reliable data gathering and reliable timely event monitoring. It also enhances the network lifetime significantly compared to other existing methods.