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Vibration spectrum imaging : A novel bearing fault classification approach

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

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2015
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Electronics Vol. 62, no. 1 (2015), p. 494-502
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  • Description: Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier.

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.

Autonomous behavior modeling approach for diverse anomaly detection application

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

  • Authors: Amar, Muhammad , Wilson, Campbell , Gondal, Iqbal
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: ICOSST 2014 - 2014 International Conference on Open Source Systems and Technologies, Lahore, Pakistan, 18-20th Dec 2014 p. 122-127
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  • Description: For absolute process safety in diverse machine applications, timely and reliable anomalous behavior detection is very crucial. Different machine applications have different normal behavior patterns and safety standards thus require adjustable and adaptive anomaly detection techniques. In this paper an autonomous behavior modeling approach for anomaly detection has been presented. In this approach time segmented vibration signals from the machines are transformed into spectral contents. After normalization, these frequency domain contents are divided into weighted frequency bins and then Gaussian models are achieved for these frequency bins over the entire training set. Using summation rule on the outputs of Gaussian models a single indicative measure of the machine health: normality score is obtained. The sensitivity of the normality score and anomaly detector towards potential anomalous signals can be controlled by using different number of bins and weights. Suitable parameters values, number of bins and weights profile, for anomaly detector model are selected autonomously using minimum value of the cost function. The increase of normality score of this model above a certain threshold is considered an alarm indicating anomalous behavior. Thus the proposed method enables us to achieve autonomously a suitable anomaly detection model with suitable parameters with controlled sensitivity during the test phase.

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.

Fuzzy logic inspired bearing fault-model membership estimation

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

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing p. 420-425
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  • Description: In rotary machines bearings are a primary cause of failure. In order to estimate the time before failure to provide information for timely bearing replacement strategies, condition-based machine health monitoring techniques are employed. This paper discusses a model for estimating the severity of bearing faults that can be used for residual bearing life estimation by processing the vibration signal. The proposed technique used in this model examines the spectral content of vibration signals across frequency bins and then fits Gaussian distributions to each frequency bin. With the use of these Gaussian models and training set examples with different fault severity levels, characteristic membership functions are constructed. This enables estimation of the severity levels of the bearing faults through a fuzzy-logic inspired process, whereby the severity level corresponds to the maximum of the set of corresponding membership functions. Thus based on discrete fault severity levels, trained Gaussian fittings of spectral bins and characteristic fault membership functions are capable to estimate the fault severity on a continuous scale.

Weighted ANN input layer for adaptive features selection for robust fault classification

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

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2015
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Model based feature selection for identification of diverse faults in rotary machines can significantly cost time and money and it is nearly impossible to model all faults under different operating environments. In this paper, feedforward ANN input-layer-weights have been used for the adaptive selection of the least number of features, without fault model information, reducing the computations significantly but assuring the required accuracy by mitigating the noise. In the proposed approach, under the assumption that presented features should be translation invariant, ANN uses entire set of spectral features from raw input vibration signal for training. Dominant features are then selected using input-layer-weights relative to a threshold value vector. Different instances of ANN are then trained and tested to calculate F1_score with the reduced dominant features at different SNRs for each threshold value. Trained ANN with best average classification accuracy among all ANN instances gives us required number of dominant features. © Springer International Publishing Switzerland 2015.

Complex anomaly for enhanced machine independent condition monitoring

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

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: 9th International Conference on Open Source Systems and Technologies, ICOSST 2015; Lahore, Pakistan; 17th-19th December 2015
  • Full Text: false
  • Description: Safety in machine applications requires tracking machine health during the time of operations. Anomaly detection techniques are used to model normal behavior of the machines and raise an alarm if any anomaly is observed. But traditional anomaly detection techniques do not identify type and severity of aberrance in terms of amplitude, pattern or both. Once the anomalous behavior is observed then fault detection techniques are applied to diagnose faults. For machine independent condition monitoring (MICM) a range of features transforms are needed for autonomous learning of the fault classifiers for different parameters to identify variety of fault types which requires huge amount of time. In this paper a novel complex anomaly plan (CAP) representation has been proposed with amplitude anomalies on real and pattern anomalies on imaginary axis. To plot amplitude and pattern anomalies in the CAP, normal state vibrations frequency features are used to train Gaussian models for each of the frequency. The dynamic location of the anomaly plotted in the CAP gives a measure of the intensity of the anomaly, where real and imaginary axis components help the fault classifier to make an appropriate selection of the transform and thus enhances the efficiency of MICM framework. © 2015 IEEE.
  • Description: ICOSST 2015 - 2015 International Conference on Open Source Systems and Technologies, Proceedings

A novel color image fusion QoS measure for multi-sensor night vision applications

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

  • Authors: Anwaar, Ul-Haq , Gondal, Iqbal , Murshed, Manzur
  • Date: 2010
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Color image fusion of visible and infra-red imagery can play an important role in multi-sensor night vision systems that are an integral part of modern warfare. Image fusion minimizes the amount of required bandwidth by transmitting the fused image rather than multiple sensor images. Color image fusion can be achieved by combining inputs from original colored sensors or by employing pseudo colorization and color transfer to grayscale images. Various quality measures have been proposed for multi-sensor grayscale image fusion techniques; but no appropriate quality measure has been devised for the quality evaluation of multi-sensor color image fusion. In this paper, we propose a novel color image fusion quality measure, Color Fusion Objective Index (CFOI) based on colorfulness, gradient similarity and mutual information techniques. Experimental results show the effectiveness of CFOI to evaluate the color and salient feature extraction introduced by color fusion techniques into the final fused imagery as well as its consistency with subjective evaluation.

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.

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

Dynamic sensor selection for target tracking in wireless sensor networks

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

  • Authors: Armaghani, Farzaneh , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: IEEE 74th Vehicular Technology Conference, VTC Fall 2011; San Francisco, United States; 5th-8th September 2011 p. 1-6
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  • Description: Optimum selection of sensors in target tracking applications has a great potential to maintain right trade-off between energy consumption and quality of tracking. In this paper, we propose a dynamic sensor selection scheme to achieve energy efficiency while ensuring the required quality of tracking. To this end, relative information utility projection of a target on sensors' observation is used in niche overlap measurements. Niche overlap measures are used to assess the similarity in information utilities where information utility is inversely proportional to error in target's state estimation based on prior distribution. The proposed scheme is a greedy approach in which sensor nodes are selected such that the overall niche overlap of all the selected nodes is maximized until the required level of accuracy is achieved. Our simulation results show significant improvement in tracking accuracy and network's lifetime over the existing methods.

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.

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

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

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.

Sensor selection for tracking multiple groups of targets

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

  • Authors: Armaghani, Farzaneh , Gondal, Iqbal , Kamruzzaman, Joarder , Green, David
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 46, no. (2014), p. 36-47
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  • Description: Group target tracking is a challenge for sensor networks. It occurs where large numbers of closely spaced targets move together in different groups. In these applications, the sensor selection scheme plays a vital role in extending network lifetime while providing high tracking accuracy. Existing schemes cause an extreme imbalance between energy usages and tracking accuracy. They are capable of tracking only individual groups and without using prior knowledge about the groups. These problems make them impractical for group target tracking. With the aim of balancing the trade-off between lifetime and accuracy, we present a novel Multi-Sensor Group Tracking (MSGT) scheme. MSGT comprises the following steps to accomplish concurrent tracking of multiple groups: (1) Clustering to capture changes in the behavioural properties of groups, such as formation, merging, and splitting; (2) Sensor selection to activate the contributory sensors for the estimated group regions; and (3) Group tracking using the activated sensors. We develop a probabilistic decision-making strategy that triggers the clustering step adaptively with any detected change in group behavioural patterns. The sensor selection step coordinates periodic selection of leader and tracking sensor nodes in a distributed manner. We introduce cost metrics that include sensor′s energy parameters in the selection of active sensors that fully cover the group regions. The tracking step is a Bayesian modelling of the target groups which uses particle filtering algorithm to estimate the group locations. Simulation results show that our scheme achieves substantial improvements over existing approaches in terms of network lifetime and tracking accuracy.

CAM : Congestion avoidance and mitigation in wireless sensor networks

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

LACAR : Location aided congestion aware routing in wireless sensor networks

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

  • Authors: Bhuiyan, Mohammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Wireless Communications and Networking Conference (WCNC), p. 1-6
  • Full Text: false
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  • Description: Trade-off between energy-efficiency and reliability in wireless sensor networks is application dependent. Without the reliability, the extended lifetime of a network is of limited use. Due to the inherent correlation between reliability and congestion, it is necessary to reduce congestion to improve reliability. Existing congestion control algorithms in wireless sensor networks are reactive. They attempt to reduce the congestion only after its detection. In this paper, we present Location Aided Congestion Aware Routing (LACAR) protocol that proactively avoids congestion formation and improves data delivery success rate in data gathering wireless sensor networks. Location, energy and congestion information of neighbours together with the location information of the base station determine appropriate routes. Simulation results show that LACAR achieves high packet success rate in an energy-efficient way.

I-MAC: energy efficient intelligent MAC protocol for wireless sensor networks

- 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. 133-138
  • Full Text: false
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  • Description: Energy efficiency is a vital aspect of resource constrained wireless sensor networks (WSNs). All protocols designed for WSNs must be energy aware in order to prolong the network lifetime. In this paper, we have designed a novel MAC layer protocol (I-MAC: Intelligent MAC) for WSNs. By exercising intelligent sleep and wake-up schedule, I-MAC saves energy of the resource constrained sensor nodes greatly. At the same time, I-MAC does not compromise its operational performances. Both analytical study and simulation prove that I-MAC is not only highly energy efficient but also its operational performances are better than similar protocols.

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.

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
  • Full Text: false
  • Reviewed:
  • 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.