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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
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  • 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.

Severity invariant feature selection for machine health monitoring

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Journal article
  • Relation: International Review of Electrical Engineering Vol. 6, no. 1 (2011), p. 238-248
  • Full Text: false
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  • Description: Vibration signals used for abnormality detection in machine health monitoring (MHM) suffer from significant variation in the patterns with fault severity. This variation results in overlap among the features extracted against different fault types and causes severe degradation in fault detection accuracy. This paper identifies a newfangled problem originated by severity variant features and mitigates this impact by using appropriate feature selection based on Fisher linear discriminant (FLD) and Bhattacharyya distance (BCD) to enhance fault classification accuracy. In order to validate the performance of the proposed scheme, training and testing data are obtained from different severity levels. To capture the non-stationary behavior of vibration signal, robust tools such as wavelet transform (WT) for time-frequency analysis is employed. Simulation studies show that the proposed scheme ensures good fault diagnostic accuracy even if training and testing data belong to different severity levels. [ABSTRACT FROM AUTHOR]

Inchoate fault detection framework: adaptive selection of wavelet nodes and cumulant orders

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2012
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Instrumentation and Measurement Vol. 61, no. 3 (2012), p. 685-695
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  • Description: Inchoate fault detection for machine health monitoring (MHM) demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR) which persists in most industrial environment. Vibration signals are extensively used in signature matching for abnormality detection and diagnosis. In order to guarantee improved performance under poor SNR, feature extraction based on statistical parameters which are immune to Gaussian noise becomes inevitable. This paper proposes a novel framework for adaptive feature extraction based on higher order cumulants (HOCs) and wavelet transform (WT) (AFHCW) for MHM. Features extracted based on HOCs have the tendency to mitigate the impact of Gaussian noise. WT provides better time and frequency domain analysis for the nonstationary signals such as vibration in which spectral contents vary with respect to time. In AFHCW, stationary WT is used to ensure linear processing on the vibration data prior to feature extraction, and it helps in mitigating the impact of poor SNR. K-nearest neighbor classifier is used to categorize the type of the fault. Simulation studies show that the proposed scheme outperforms the existing techniques in terms of classification accuracy under poor SNR.

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
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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.

Resonant frequency band estimation using adaptive wavelet decomposition level selection

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: 2011 IEEE International Conference on Mechatronics and Automation (ICMA) p. 376-381
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  • Description: The vibrations induced by machine faults help in diagnosis and prognosis of the machine. It is crucial for the fault diagnostic system to extract resonant frequency band which carries useful information about the defect frequencies and contains maximum signal to noise ratio. The spectral orientation of the resonant frequency band varies with the variation in machine dynamics. The existing techniques which employ wavelet transformation to exploit the signal energy distribution among different frequency sub-bands, are based on fixed decomposition level and do not optimize the wavelet parameters according to varying machine dynamics. The proposed study develops a novel technique: Adaptive Wavelet Decomposition and Resonance Frequency Estimation (AWRE) which estimates the positioning of the resonant frequency band based on adaptive selection of the wavelet decomposition levels. The results for the simulated as well as actual vibration data demonstrate that the proposed technique estimates the bandwidth of the resonant frequency band quite effectively.

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

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Loparo, Kenneth
  • Date: 2013
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 171-182
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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.

Severity invariant machine fault diagnosis

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: 6th IEEE Conference on Industrial Electronics and Applications p. 21-26
  • Full Text: false
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  • Description: Vibration signals used for abnormality detection in machine health monitoring (MHM) suffer from significant variation with fault severity. This variation causes overlap among the features belonging to different types of faults resulting in severe degradation of fault detection accuracy. This paper identifies a new problem due to severity variant features and proposes a novel adaptive training set and feature selection (ATSFS) scheme based upon the orientation of the test data. In order to build ATSFS and validate its performance, training and testing data are obtained from different severity levels. To capture the non-stationary behavior of vibration signal, robust tools such as wavelet transform (WT) for time-frequency analysis are employed. Simulation studies show that ATSFS attains high classification accuracy even if training and testing data belong to different severity levels.

Diversified adaptive frequency rolling to mitigate self and static interferences

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2010
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Increase in the number of coexisting networks in Industrial, Scientific and Medical (ISM) band cause interferences and demands for intelligent interference avoidance schemes. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets which has the tendency to mitigate both the self and static interferences and ensures sufficient frequency diversity. Simulation studies validate the prospects for the proposed scheme to be used for frequency hopping networks against already existing techniques, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR).

Machine fault severity estimation based on adaptive wavelet nodes selection and SVM

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: IEEE International Conference on Mechatronics and Automation (ICMA),Beijing 7 August 2011 to 10 August 2011) p. 1951-1956
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  • Description: The study is focused on estimating the severity level of the bearing faults which helps in determining the residual life of the equipment and planned maintenance. A novel technique, adaptive severity estimation model (ASEM) is proposed based on adaptive selection of wavelet decomposition nodes and support vector machines. Vibration data from multiple severity levels are used to build the fault estimation model. An adaptive criterion for wavelet decomposition node selection is developed which helps ASEM to achieve robustness in estimating fault severity under varying signal to noise ratio (SNR), a key demand in industrial environment. The simulated data with known severity level is used to parameterize the estimation model. The fault severity estimation performance of ASEM is also validated for the real vibration data and its robustness is gauged under varying SNR conditions.

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

  • 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
  • Full Text: false
  • Reviewed:
  • 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.

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

  • 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
  • Full Text: false
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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.

Coexistence mechanism for industrial automation network

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: 12th IEEE International Conference on High Performance Computing and Communications
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  • Description: Increase in the number of coexisting networks in license free Industrial, Scientific and Medical (ISM) band causes interferences for industrial automation, e.g., shop floors of manufacturing facilities. In order to ensure the reliability for automation networks, interference avoidance schemes are required. This paper proposes a novel Predefined Hopping Pattern (PHP) technique for frequency hopping in ISM band, which mitigates self-interferences and static interferers as well. This technique generates optimized frequency hopping sequences which ensure sufficient frequency diversity and frequency offset among the coexisting Bluetooth piconets and exploits transmission experiences for a particular frequency in eliminating interference. Simulation studies have shown that PHP has better collision avoidance rate than well known adaptive frequency hopping (AFH) and adaptive frequency rolling (AFR) schemes.

Self static interference mitigation scheme for coexisting wireless networks

  • 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.

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

  • 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.

Multiple-points fault signature's dynamics modeling for bearing defect frequencies

Envelope-Wavelet Packet Transform for Machine Condition Monitoring

Failure analysis of Slurry Pump assets in refinery for reduction of risks and costs

  • Authors: Welandage Don , Chattopadhyay, Gopi , Kahandawa, Gayan , Kamruzzaman, Joarder , Zhang, L.
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022, Anand, India, 12-15 December 2022, 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022
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  • Description: Assets fail due to design, manufacturing, installation, operations and maintenance, including ageing. Analysis of failures is needed for understanding the failure mechanism and identifying improvement opportunities to reduce risks and costs. Every year, the refinery spends a large amount of money on the maintenance of pumps and related accessories. There are unplanned maintenance and downtimes due to breakdowns. Reliability Centre Maintenance (RCM), Failure Mode and Effect Analysis (FMEA) and Residual life tracker, Equipment Management Strategy (EMS) were considered to create Preventive Maintenance (PM) activities and forecast the maintenance costs. However, budget overruns continued due to unplanned failures. This study analysed failures and identified gaps within the existing strategy for Washer and thickener underflow pumps. A review of maintenance activities was conducted along with review of design capabilities, failure modes and failure mechanisms and trends. Opportunities for improvements (OFI) were identified, and improvement actions were carried out to reduce risks and costs. © 2022 IEEE.

Sensitivity analysis for vulnerability mitigation in hybrid networks

  • Authors: Ur‐rehman, Attiq , Gondal, Iqbal , Kamruzzaman, Joarder , Jolfaei, Alireza
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 11, no. 2 (2022), p.
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  • Description: The development of cyber‐assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Vulnerability modelling for hybrid industrial control system networks

  • Authors: Ur-Rehman, Attiq , Gondal, Iqbal , Kamruzzaman, Joarder , Jolfaei, Alireza
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Journal of Grid Computing Vol. 18, no. 4 (2020), p. 863-878
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  • Description: With the emergence of internet-based devices, the traditional industrial control system (ICS) networks have evolved to co-exist with the conventional IT and internet enabled IoT networks, hence facing various security challenges. The IT industry around the world has widely adopted the common vulnerability scoring system (CVSS) as an industry standard to numerically evaluate the vulnerabilities in software systems. This mathematical score of vulnerabilities is combined with environmental knowledge to determine the vulnerable nodes and attack paths. IoT and ICS systems have unique dynamics and specific functionality as compared to traditional computer networks, and therefore, the legacy cyber security models would not fit these advanced networks. In this paper, we studied the CVSS v3.1 framework’s application to ICS embedded networks and an improved vulnerability framework, named CVSSIoT-ICS, is proposed. CVSSIoT-ICS and CVSS v3.1 are applied to a realistic supply chain hybrid network which consists of IT, IoT, and ICS nodes. This hybrid network is assigned with actual vulnerabilities listed in the national vulnerability database (NVD). The comparison results confirm the effectiveness of CVSSIoT-ICS framework as it is equally applicable to all nodes of a hybrid network and evaluates the vulnerabilities based on the distinct features of each node type. © 2020, Springer Nature B.V.

Spam email categorization with nlp and using federated deep learning

  • Authors: Ul Haq, Ikram , Black, Paul , Gondal, Iqbal , Kamruzzaman, Joarder , Watters, Paul , Kayes, A.
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Advanced Data Mining and Applications, ADMA 2022, Brisbane, Australia, 28-30 November 2022, Advanced Data Mining and Applications, 18th International Conference, ADMA 2022 Vol. 13726 LNAI, p. 15-27
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  • Description: Emails are the most popular and efficient communication method that makes them vulnerable to misuse. Federated learning (FL) provides a decentralized machine learning (ML) model, where a central server coordinates clients that collaboratively train a shared ML model. This paper proposes Federated Phishing Filtering (FPF) technique based on federated learning, natural language processing, and deep learning. FL for intelligent algorithms fuses trained models of ML algorithms from multiple sites for collective learning. This approach improves ML performance by utilizing large collective training data sets across the corporate client base, resulting in higher phishing email detection accuracy. FPF techniques preserve email privacy using local feature extraction on client email servers. Thus, the contents of emails do not need to be transmitted across the network or stored on third-party servers. We have applied FL and Natural Language Processing (NLP) for email phishing detection. This technique provides four training modes that perform FL without sharing email content. Our research categorizes emails as benign, spam, and phishing. Empirical evaluations with publicly available datasets show that accuracy is improved by the use of our Federated Deep Learning model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.