List of Titles

A mapreduce based technique for mining behavioral patterns from sensor data

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

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2015
Type: Text , Conference paper
Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9492, p. 145-153
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Description: WSNs generate a large amount of data in the form of streams, and temporal regularity in occurrence behavior is considered as an important measure for assessing the importance of patterns in WSN data. A frequent sensor pattern that occurs after regular intervals in WSNs is called regularly frequent sensor patterns (RFSPs). Existing RFSPs techniques assume that the data structure of the mining task is small enough to fit in the main memory of a processor. However, given the emergence of the Internet of Things (IoT), WSNs in future will generate huge volume of data, which means such an assumption does not hold any longer. To overcome this, a distributed solution using MapReduce model has not yet been explored extensively. Since MapReduce is becoming the de-facto model for computation on large data, an efficient RFSPs mining algorithm on this model is likely to provide a highly effective solution. In this work, we propose a regularly frequent sensor patterns mining algorithm called RFSP-H which uses MapReduce based framework. Extensive performance analyses show that our technique is significantly time efficient in finding regularly frequent sensor patterns. © 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
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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

Condition monitoring through mining fault frequency from machine vibration data

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

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2015
Type: Text , Conference paper
Relation: International Joint Conference on Neural Networks, IJCNN 2015; Killarney; Ireland; 12th-17th July 2015 p. 1-8
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Description: In machine health monitoring, fault frequency identification of potential bearing faults is very important and necessary when it comes to reliable operation of a given system. In this paper, we proposed a data mining based scheme for fault frequency identification from the bearing data. In this scheme, we propose a compact tree called SAP-tree (sliding window associated frequency pattern tree) which is built upon the analysis of frequency domain characteristics of machine vibration data. Using this tree we devised a sliding window-based associated frequency pattern mining technique, called SAP algorithm, that mines for the frequencies relevant to machine fault. Our SAP algorithm can mine associated frequency patterns in the current window with frequent pattern (FP)-growth like pattern-growth method and used these patterns to identify the fault frequency. Extensive experimental analyses show that our technique is very efficient in identifying fault frequency over vibration data stream.

Content exchange among mobile tourists using users' interest and place-centric activities

- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal

Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
Date: 2015
Type: Text , Conference paper
Relation: 2015 10th International Conference on Information, Communications and Signal Processing (Icics); Singapore, Singapore; 2nd-4th December 2015 p. 1-5
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Description: In this work we investigate decentralized content exchange among tourists who are mostly strangers, depicts irregular movement patterns and most likely not to have any prior social relationship or difficult to establish any in a tourist spot. We incorporate user's interest, trustworthy online recommendations, and place-centric information to facilitate content exchange in such tourist destinations. The proposed administrator selection policy considers stay probability in activities, connectivity among nodes and their available resources. We have done extensive simulation using network simulator NS3 on a popular tourist spot in Australia that provides a number of activities. Our proposed approach shows promising results in exchanging contents among users measured in terms of content hit and delivery success rate as well as latency. The success rate is comparable to those reported in the literature for cases where social relationship exist and nodes follow regular predictable movement patterns.

Content sharing among visitors with irregular movement patterns in visiting hotspots

- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal

Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
Date: 2015
Type: Text , Conference paper
Relation: 2015 IEEE 14th International Symposium on Network Computing and Applications (NCA); Cambridge, United States; 28th - 30th September 2015; published in Proceedings - 2015 IEEE 14th International Symposium on Network Computing and Applications, NCA 2015 p. 230-234
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Description: Smart mobile devices have become immensely popular among the people worldwide and provide a new platform for generating and sharing contents. The centralized and hybrid architectures for content sharing require constant Internet connection, increase traffic and incur costs. To address these issues several content sharing approaches have been proposed using the decentralized architecture. Most of the proposed approaches use spatio-temporal regularity and pre-existing social relationships of the users to predict their movements and facilitate content sharing. However, there are scenarios such as visiting hotspots where regular movement patterns or established social relationships among people might not exist. Content sharing in such scenarios has not been addressed yet in literature and existing prediction based approaches are ineffectual. This study focuses on facilitating content sharing in the afore-mentioned scenarios. We take account of user interests, recommendations from on-line social networks, hotspot specific activities and other relevant information to construct communities which facilitate content sharing. For each community an administrator, who maintains content and member lists and render directory services, is selected based on stay probability, interest score, battery lifetime and device configuration. Simulation results show that our proposed approach attains high content hit and success rate and low latency in delivery which is nearly comparable to those proposed for scenarios with regular predictable movement patterns reported in literature.

Ethical considerations when using online datasets for research purposes

- Kopp, Christian, Layton, Robert, Gondal, Iqbal, Sillitoe, Jim

Authors: Kopp, Christian , Layton, Robert , Gondal, Iqbal , Sillitoe, Jim
Date: 2015
Type: Text , Book chapter
Relation: Automating Open Source Intelligence: Algorithms for OSINT p. 131-157
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Description: The Internet has become an important community communications platform, supporting a range of programs and virtual environments. While there are many ways in which people choose to develop personal interactions over the Internet, one of the most popular manifestations is the creation and maintenance of social relationships using social and dating websites. In this chapter, the collection and use of data from such sites is assessed from an ethical frame, and key concepts such as informed consent, information, comprehension, and voluntariness are outlined.

Mining associated patterns from wireless sensor networks

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

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2015
Type: Text , Journal article
Relation: IEEE Transactions on Computers Vol. 64, no. 7 (2015), p. 1998-2011
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Description: Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques.

Share-frequent sensor patterns mining from wireless sensor network data

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

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2015
Type: Text , Journal article
Relation: IEEE Transactions on Parallel and Distributed Systems Vol. 26, no. 12 (2015), p. 3471-3484
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Description: Mining interesting knowledge from the huge amount of data gathered from WSNs is a challenge. Works reported in literature use support metric-based sensor association rules which employ the occurrence frequency of patterns as criteria. However, consideration of the binary frequency of a pattern is not a sufficient indicator for finding meaningful patterns because it only reflects the number of epochs which contain that pattern in the dataset. The share measure of sensorsets could discover useful knowledge about trigger values associated with a sensor. Here, we propose a new type of behavioral pattern called share-frequent sensor patterns (SFSPs) by considering the non-binary frequency values of sensors in epochs. SFSPs can find a correlation among a set of sensors and hence can improve the performance of WSNs in a resource management process. In this paper, a share-frequent sensor pattern tree (ShrFSP-Tree) has been proposed to facilitate a pattern growth mining technique to discover SFSPs from WSN data. We also present a parallel and distributed method where the ShrFSP-Tree is enhanced into PShrFSP-Tree and its performance is investigated for both homogeneous and heterogeneous systems. Results show that our method is time and memory efficient in finding SFSPs than the existing most efficient algorithms.

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.

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
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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.

A novel algorithm for mining behavioral patterns from wireless sensor networks

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

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2014
Type: Text , Conference paper
Relation: 2014 International Joint Conference on Neural Networks, IJCNN 2014; Beijing, China; 6th-11th July 2014 p. 1-7
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Description: Due to recent advances in wireless sensor networks (WSNs) and their ability to generate huge amount of data in the form of streams, knowledge discovery techniques have received a great deal of attention to extract useful knowledge regarding the underlying network. Traditionally sensor association rules measure occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. In this paper, we propose a new type of sensor behavioral pattern called associated sensor patterns that captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. We also propose a novel tree structure called as associated sensor pattern tree (ASPT) and a mining algorithm, associated sensor pattern (ASP) which facilitates frequent pattern (FP) growth-based technique to generate all associated sensor patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our algorithm is very efficient in finding associated sensor patterns than the existing significant algorithms.

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.

Self static interference mitigation scheme for coexisting wireless networks

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

Authors: Yaqub, Muhammad , Haider, Ammar , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2014
Type: Text , Journal article
Relation: Computers and Electrical Engineering Vol. 40, no. 2 (2014), p. 307-318
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Description: High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.

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.

Abrasion modeling of multiple-point defect dynamics for machine condition monitoring

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

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
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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.

ACSP-Tree: A tree structure for mining behavioral patterns from wireless sensor networks

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

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2013
Type: Text , Conference paper
Relation: IEEE Conference on Local Computer Networks (LCN 2013) (21 October 2013 to 24 October 2013) p. 691-694
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Description: WSNs generates a large amount of data in the form of stream and mining knowledge from the stream of data can be extremely useful. Association rules mining, from the sensor data, has been studied in recent literature. However, sensor association rules mining often produces a huge number of rules, but most of them either are redundant or fail to reflect the true correlation relationship among data objects. In this paper, we address this problem and propose mining of a new type of sensor behavioral pattern called associated-correlated sensor patterns. The proposed behavioral patterns capture not only association-like co-occurrences but also the substantial temporal correlations implied by such co-occurrences in the sensor data. Here, we also use a prefix tree-based structure called associated-correlated sensor pattern-tree (ACSP-tree), which facilitates frequent pattern (FP) growth-based mining technique to generate all associated-correlated patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our approach is time and memory efficient in finding associated-correlated patterns than the existing most efficient algorithms.

An adaptive self-configuration scheme for severity invariant machine fault diagnosis

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

Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2013
Type: Text , Journal article
Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 116-126
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Description: Vibration signals, used for abnormality detection in machine health monitoring (MHM), exhibit significant variation with varying fault severity. This signal variation causes overlap among the features characterizing different types of faults, which results in severe performance degradation of the fault diagnostic model. In this paper, a wavelet based adaptive training set and feature selection (WATF) self-configuration scheme is presented, which selects the optimum wavelet decomposition level, and employs adaptive selection of the training set and features. Optimal wavelet decomposition level selection is such that the maximum fault signature-signal energy bands are achieved. The severity variant features, which could cause detrimental class overlap for MHM, are avoided using adaptive selection of the training set and features based on the location of a test data in feature space. WATF uses Support Vector Machines (SVM) to build the fault diagnostic model, and its performance and robustness has been tested with data having different severity levels. Comparative studies of WATF with eight existing fault diagnosis schemes show that, for publicly available data sets, WATF achieves higher fault detection accuracy, even when training and testing data sets belong to different severity levels.

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.

Mining associated sensor patterns for data stream of wireless sensor networks

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

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2013
Type: Text , Conference proceedings
Relation: 8th ACM International Workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, PM2HW2N 2013, Barcelona; Spain; 3rd-8th November 2013 p. 91-98
Full Text: false
Reviewed:
Description: WSNs generate a large amount of data in the form of data stream; and mining these streams to extract useful knowledge is a highly challenging task. Existing works proposed in literature use sensor association rules measured in terms of occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. Additionally mining associated sensor patterns from sensor stream data, which is vital for real-time applications, has not been addressed yet in literature. In this paper, we address these problems and propose a new type of sensor behavioral pattern called associated sensor patterns which capture simultaneously association-like co-occurrence as well as substantial temporal correlations implied by such co-occurrences in sensor data. We propose a novel tree structure, called associated sensor pattern stream tree (ASPS-tree) and a new technique, called associated sensor pattern mining of data stream (ASPMS), using sliding window-based associated sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an ASPS-tree, our ASPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering associated sensor patterns over sensor data stream.

Mobile agent based artificial immune system for machine condition monitoring

- Hua, Xue-Liang, Gondal, Iqbal, Yaqub, Farrukh

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
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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.

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