CERES :: Browsing by Author "Jennions, Ian K."

Browsing by Author "Jennions, Ian K."

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Open Access
Aging detection capability for switch-mode power converters
(Institute of Electrical and Electronics Engineers, 2016-02-25) Kaur Mann, Jaspreet; Perinpanayagam, Suresh; Jennions, Ian K.
The detection of degradations and resulting failures in electronic components/systems is of paramount importance for complex industrial applications including nuclear power reactors, aerospace, automotive, and space applications. There is an increasing acceptance of the importance of detection of failures and degradations in electronic components and of the prospect of system-level health monitoring to make a key contribution to detecting and predicting any impending failures. This paper describes a parametric system identification-based health-monitoring method for detecting aging degradations of passive components in switch-mode power converters (SMPCs). A nonparametric system response is identified by perturbing the system with an optimized multitone sinusoidal signal of the order of mVs. The parametric system model is estimated from nonparametric system response using recursive weighted least-square (WLS) algorithm. Finally, the power-stage component values, including their parasitics, are extracted from numerator and denominator coefficients based on the assumed Laplace system model. These extracted component values provide direct diagnostic information of any degradation or anomalies in the components and the system. A proof of concept is initially verified on a simple point-of-load (POL) converter but the same methodology can be applied to other topologies of SMPC.
Open Access
Application of an AIS to the problem of through life health management of remotely piloted aircraft
(American Institute of Aeronautics and Astronautics, 2015-12-31) Pelham, Jonathan G.; Fan, Ip-Shing; Jennions, Ian K.; McFeat, Jim
The operation of RPAS includes a cognitive problem for the operators(Pilots, maintainers, ,managers, and the wider organization) to effectively maintain their situational awareness of the aircraft and predict its health state. This has a large impact on their ability to successfully identify faults and manage systems during operations. To overcome these system deficiencies an asset health management system that integrates more cognitive abilities to aid situational awareness could prove beneficial. This paper outlines an artificial immune system (AIS) approach that could meet these challenges and an experimental method within which to evaluate it.
Open Access
The application of Bayesian Change Point Detection in UAV fuel systems
(Elsevier, 2014-10-31) Niculita, Octavian; Skaf, Zakwan; Jennions, Ian K.
A significant amount of research has been undertaken in statistics to develop and implement various change point detection techniques for different industrial applications. One of the successful change point detection techniques is Bayesian approach because of its strength to cope with uncertainties in the recorded data. The Bayesian Change Point (BCP) detection technique has the ability to overcome the uncertainty in estimating the number and location of change point due to its probabilistic theory. In this paper we implement the BCP detection technique to a laboratory based fuel rig system to detect the change in the pre-valve pressure signal due to a failure in the valve. The laboratory test-bed represents a Unmanned Aerial Vehicle (UAV) fuel system and its associated electrical power supply, control system and sensing capabilities. It is specifically designed in order to replicate a number of component degradation faults with high accuracy and repeatability so that it can produce benchmark datasets to demonstrate and assess the efficiency of the BCP algorithm. Simulation shows satisfactory results of implementing the proposed BCP approach. However, the computational complexity, and the high sensitivity due to the prior distribution on the number and location of the change points are the main disadvantages of the BCP approach.
Open Access
Application of data analytics for predictive maintenance in aerospace: an approach to imbalanced learning.
(2021-05) Dangut, Maren David; Jennions, Ian K.; King, Steve
The use of aircraft operational logs to predict potential failure that may lead to disruption poses many challenges and has yet to be fully explored. These logs are captured during each flight and contain streamed data from various aircraft subsystems relating to status and warning indicators. They may, therefore, be regarded as complex multivariate time-series data. Given that aircraft are high-integrity assets, failures are extremely rare, and hence the distribution of relevant data containing prior indicators will be highly skewed to the normal (healthy) case. This will present a significant challenge in using data-driven techniques to 'learning' relationships/patterns that depict fault scenarios since the model will be biased to the heavily weighted no-fault outcomes. This thesis aims to develop a predictive model for aircraft component failure utilising data from the aircraft central maintenance system (ACMS). The initial objective is to determine the suitability of the ACMS data for predictive maintenance modelling. An exploratory analysis of the data revealed several inherent irregularities, including an extreme data imbalance problem, irregular patterns and trends, class overlapping, and small class disjunct, all of which are significant drawbacks for traditional machine learning algorithms, resulting in low-performance models. Four novel advanced imbalanced classification techniques are developed to handle the identified data irregularities. The first algorithm focuses on pattern extraction and uses bootstrapping to oversample the minority class; the second algorithm employs the balanced calibrated hybrid ensemble technique to overcome class overlapping and small class disjunct; the third algorithm uses a derived loss function and new network architecture to handle extremely imbalanced ratios in deep neural networks; and finally, a deep reinforcement learning approach for imbalanced classification problems in log- based datasets is developed. An ACMS dataset and its accompanying maintenance records were used to validate the proposed algorithms. The research's overall finding indicates that an advanced method for handling extremely imbalanced problems using the log-based ACMS datasets is viable for developing robust data-driven predictive maintenance models for aircraft component failure. When the four implementations were compared, deep reinforcement learning (DRL) strategies, specifically the proposed double deep State-action-reward-state-action with prioritised experience reply memory (DDSARSA+PER), outperformed other methods in terms of false-positive and false-negative rates for all the components considered. The validation result further suggests that the DDSARSA+PER model is capable of predicting around 90% of aircraft component replacements with a 0.005 false-negative rate in both A330 and A320 aircraft families studied in this research
Open Access
Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance
(Elsevier, 2022-02-08) Dangut, Maren David; Jennions, Ian K.; King, Steve; Skaf, Zakwan
The use of aircraft operational logs to predict potential failure that may lead to disruption poses many challenges and has yet to be fully explored. Given that aircraft are high-integrity assets, failures are extremely rare, and hence the distribution of relevant log data containing prior indicators will be highly skewed to the normal (healthy) case. This will present a significant challenge in using data-driven techniques because the model will be biased to the heavily weighted no-fault outcomes. This paper presents a novel approach for predicting unscheduled aircraft maintenance action based on deep reinforcement learning techniques using aircraft central maintenance system logs. The algorithm transforms the rare failure prediction problem into a sequential decision-making process that is optimised using a reward system that penalises proposed predictions that result in a false diagnosis and preferentially favours predictions that result in the right diagnosis. The validation data is directly associated with the physical health aspects of the aircraft components. The influence of extremely rare failure prediction on the proposed method is analysed. The effectiveness of the new approach is verified by comparison with previous studies, cost-sensitive and oversampling methods. Performance was evaluated based on G-mean and false-positives rates. The proposed approach shows the superior performance of 20.3% improvement in G-mean and 97% reduction in false-positive rate.
Open Access
The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities
(Elsevier, 2019-01-11) Ezhilarasu, Cordelia Mattuvarkuzhali; Skaf, Zakwan; Jennions, Ian K.
This paper aims to discuss the importance and the necessity of reasoning applications in the field of Aerospace Integrated Vehicle Health Management (IVHM). A fully functional IVHM system is required to optimize Condition Based Maintenance (CBM), avoid unplanned maintenance activities and reduce the costs inflicted thereupon. This IVHM system should be able to utilize the information from multiple subsystems of the vehicle to assess the health of those subsystems, their effect on the other subsystems, and on the vehicle as a whole. Such a system can only be realized when the supporting technologies like sensor technology, control and systems engineering, communications technology and Artificial Intelligence (AI) are equally advanced. This paper focuses on the field of AI, especially reasoning technology and explores how it has helped the growth of IVHM in the past. The paper reviews various reasoning strategies, different reasoning systems, their architectures, components and finally their numerous applications. The paper discusses the shortcomings found in the IVHM field, particularly in the area of vehicle level health monitoring and how reasoning can be applied to address some of them. It also highlights the challenges faced when the reasoning system is developed to monitor the health at the vehicle level and how a few of these challenges can be mitigated.
Open Access
Artificial immune systems for case based reasoning of unmanned aircraft flight data
(2017-09) Pelham, Jonathan Gerald; Fan, Ip-Shing; McFeat, Jim; Jennions, Ian K.
UAS(Unmanned Aerial Systems) mishaps are high, and their pilots face many control challenges. The reliability of UAS has been seen as a dominant mishap cause but in several instances the aircraft could have been saved if the health state of the aircraft had been understood at an earlier point by the pilot. Manned and unmanned aircraft pilots both benefit from the use of their own experience in the detection and mitigation of faults during flight. However it has been suggested that pilots within a GCS(Ground Control Station) face difficulties in maintaining their situational awareness due to the nature of remote control. The use of a cognitive framework as a basis for case based reasoning is suggested as a way to integrate through life learning into the Safety Management System. The population of the case base for such a system would require a large investment of time to create. The use of machine learning is suggested and evaluated to address this issue by generating cases for CBR. This has seen some success and even the use of an AIS(Artificial Immune System) in this thesis. An AIS was used in order to try to address the problem of cost and time caused by high pre-processing required by common machine learning methods. A simulation of the Aerosonde UAS was created and multiple flights simulated to build up a set of representative set flight data. Several fault cases were included in the simulated flights of varying severities. Different machine learning schemes were evaluated using the data set and their effectiveness compared in order to evaluate the ability of the algorithm to learn from flight data without extensive pre-processing. The complex dataset made the problem difficult but in analysis the AIS performed slightly better than the neural network with which it was compared. In due time and with development it's computational cost could be reduced and its effectiveness increased. The benefit of an automated method to learn from aircraft incidents and mishaps can readily be seen in a fleet scenario where it would be uneconomical to analyse flight data of unmanned aircraft in the same way that it would be done for manned aircraft. This semi-supervised approach reduces personnel requirements and enhances the ability of operators to learn from mishaps by relating mishap cases to the current situation and being transparent in their alerting criteria.
Open Access
A Bayesian approach to fault identification in the presence of multi-component degradation
(PHM Society, 2017-03-10) Lin, Yufei; Zakwan, Skaf; Jennions, Ian K.
Fault diagnosis typically consists of fault detection, isolation and identification. Fault detection and isolation determine the presence of a fault in a system and the location of the fault. Fault identification then aims at determining the severity level of the fault. In a practical sense, a fault is a conditional interruption of the system ability to achieve a required function under specified operating condition; degradation is the deviation of one or more characteristic parameters of the component from acceptable conditions and is often a main cause for fault generation. A fault occurs when the degradation exceeds an allowable threshold. From the point a new aircraft takes off for the first time all of its components start to degrade, and yet in almost all studies it is presumed that we can identify a single fault in isolation, i.e. without considering multi-component degradation in the system. This paper proposes a probabilistic framework to identify a single fault in an aircraft fuel system with consideration of multi-component degradation. Based on the conditional probabilities of sensor readings for a specific fault, a Bayesian method is presented to integrate distributed sensory information and calculate the likelihood of all possible fault severity levels. The proposed framework is implemented on an experimental aircraft fuel rig which illustrates the applicability of the proposed method.
Open Access
Boeing 737-400 passenger air conditioner control system model for accurate fault simulation
(American Society of Mechanical Engineers, 2022-03-08) Chowdhury, Shafayat H.; Ali, Fakhre; Jennions, Ian K.
The aircraft environmental control system (ECS) is a highly integrated and complex system. The passenger air conditioner (PACK) is the heart of the ECS and has been reported as a key driver of unscheduled maintenance by aircraft operators. This is principally due to the PACK’s ability to compensate for degraded components, and hence mask their real condition, so that when failure occurs it is a major event. The development of an accurate diagnostic solution would identify the degradation early and hence focus effective maintenance and reduce cost. This paper is a continuation of the authors’ work on the development of a systematically derived PACK simulation for accurate fault diagnostics, utilizing a model-based approach. In practice, the PACK simulation accuracy is dependent on a number of factors, which include the understanding of its control system. The paper addresses this by taking an in-depth look at the factors controlling the operation of the PACK to enable the gap between the theoretical understanding of the PACK and the engineering design of the system to be bridged, and accurate simulations under healthy and degraded scenarios obtained. This paper provides a comprehensive explanation of the PACK control system elements (principally valves) and verifies their operation based on experimental test data acquired from a B737-400 aircraft. A discussion of the control used in the simulation is then given, resulting in the correct temperature, pressure, and flow being delivered to the cabin. The overall simulation results are then presented to demonstrate the importance of using a systematically derived control logic. They are then further used to assess the impact of degradation in the main PACK valves (PVs).
Open Access
A Carrier Signal Approach for Intermittent Fault Detection and Health Monitoring for Electronics Interconnections System
(Science and Information Organisation, 2015-12-01) Ahmad, Syed Wakil; Perinpanayagam, Suresh; Jennions, Ian K.; Samie, Mohammad
Abstract: Intermittent faults are completely missed out by traditional monitoring and detection techniques due to non-stationary nature of signals. These are the incipient events of a precursor of permanent faults to come. Intermittent faults in electrical interconnection are short duration transients which could be detected by some specific techniques but these do not provide enough information to understand the root cause of it. Due to random and non-predictable nature, the intermittent faults are the most frustrating, elusive, and expensive faults to detect in interconnection system. The novel approach of the author injects a fixed frequency sinusoidal signal into electronics interconnection system that modulates intermittent fault if persist. Intermittent faults and other channel effects are computed from received signal by demodulation and spectrum analysis. This paper describes technology for intermittent fault detection, and classification of intermittent fault, and channel characterization. The paper also reports the functionally tests of computational system of the proposed methods. This algorithm has been tested using experimental setup. It generate an intermittent signal by external vibration stress on connector and intermittency is detected by acquiring and processing propagating signal. The results demonstrate to detect and classify intermittent interconnection and noise variations due to intermittency. Monitoring the channel in-situ with low amplitude, and narrow band signal over electronics interconnection between a transmitter and a receiver provides the most effective tool for continuously watching the wire system for the random, unpredictable intermittent faults, the precursor of failure. - See more at: http://thesai.org/Publications/ViewPaper?Volume=6&Issue=12&Code=ijacsa&SerialNo=20#sthash.8RXsdW0t.dpuf
Open Access
Cross-condition fault diagnosis of an aircraft environmental control system (ECS) by transfer learning
(MDPI, 2023-12-09) Jia, Lilin; Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, Ian K.
Fault diagnosis models based on machine learning are often subjected to degradation in performance when dealing with data that are differently distributed than the training data. Such an occasion is common in reality because machines usually operate under various conditions. Transfer learning is a solution for the performance degradation of cross-condition fault diagnosis problems. This paper studies how transfer learning algorithms transfer component analysis (TCA) and joint distribution alignment (JDA) improve the cross-condition fault diagnosis accuracy of an aircraft environmental control system (ECS). Both methods work by transforming the source and target domain data into a feature space where their distributions are aligned to allow a uniform classifier to act accurately in both domains. This paper discovered that both TCA and JDA produce significantly more accurate results than traditional methods on target domains with unlabelled ECS data taken at different operating conditions than the source domain. Additionally, when dealing with unlabelled data from unknown conditions bearing a different composition of classes in the target domain, TCA is found to be more robust and accurate, generating an average predictive accuracy of 95.22%, which demonstrates the ability of transfer learning in solving similar problems in the real-world application of fault diagnosis.
Open Access
Delay-based true random number generator in sub-nanomillimeter IoT devices
(MDPI, 2020-05-15) Randa, Maulana; Samie, Mohammad; Jennions, Ian K.
True Random Number Generators (TRNGs) use physical phenomenon as their source of randomness. In electronics, one of the most popular structures to build a TRNG is constructed based on the circuits that form propagation delays, such as a ring oscillator, shift register, and routing paths. This type of TRNG has been well-researched within the current technology of electronics. However, in the future, where electronics will use sub-nano millimeter (nm) technology, the components become smaller and work on near-threshold voltage (NTV). This condition has an effect on the timing-critical circuit, as the distribution of the process variation becomes non-gaussian. Therefore, there is an urge to assess the behavior of the current delay-based TRNG system in sub-nm technology. In this paper, a model of TRNG implementation in sub-nm technology was created through the use of a specific Look-Up Table (LUT) in the Field-Programmable Gate Array (FPGA), known as SRL16E. The characterization of the TRNG was presented and it shows a promising result, in that the delay-based TRNG will work properly, with some constraints in sub-nm technology
Open Access
Design for prognostics and security in field programmable gate arrays (FPGAs).
(Cranfield University, 2020-03) Aslam, Sohaib; Jennions, Ian K.; Samie, Mohammad
There is an evolutionary progression of Field Programmable Gate Arrays (FPGAs) toward more complex and high power density architectures such as Systems-on- Chip (SoC) and Adaptive Compute Acceleration Platforms (ACAP). Primarily, this is attributable to the continual transistor miniaturisation and more innovative and efficient IC manufacturing processes. Concurrently, degradation mechanism of Bias Temperature Instability (BTI) has become more pronounced with respect to its ageing impact. It could weaken the reliability of VLSI devices, FPGAs in particular due to their run-time reconfigurability. At the same time, vulnerability of FPGAs to device-level attacks in the increasing cyber and hardware threat environment is also quadrupling as the susceptible reliability realm opens door for the rogue elements to intervene. Insertion of highly stealthy and malicious circuitry, called hardware Trojans, in FPGAs is one of such malicious interventions. On the one hand where such attacks/interventions adversely affect the security ambit of these devices, they also undermine their reliability substantially. Hitherto, the security and reliability are treated as two separate entities impacting the FPGA health. This has resulted in fragmented solutions that do not reflect the true state of the FPGA operational and functional readiness, thereby making them even more prone to hardware attacks. The recent episodes of Spectre and Meltdown vulnerabilities are some of the key examples. This research addresses these concerns by adopting an integrated approach and investigating the FPGA security and reliability as two inter-dependent entities with an additional dimension of health estimation/ prognostics. The design and implementation of a small footprint frequency and threshold voltage-shift detection sensor, a novel hardware Trojan, and an online transistor dynamic scaling circuitry present a viable FPGA security scheme that helps build a strong microarchitectural level defence against unscrupulous hardware attacks. Augmented with an efficient Kernel-based learning technique for FPGA health estimation/prognostics, the optimal integrated solution proves to be more dependable and trustworthy than the prevalent disjointed approach.
Open Access
Design of hardware-orientated security towards trusted electronics.
(Cranfield University, 2020-07) Randa, Maulana; Jennions, Ian K.; Samie, Mohammad
While the Internet of Things (IoT) becomes one of the critical components in the cyber-physical system of industry 4.0, its root of trust still lacks consideration. The purpose of this thesis was to increase the root of trust in electronic devices by enhance the reliability, testability, and security of the bottom layer of the IoT system, which is the Very Large-Scale Integration (VLSI) device. This was achieved by implement a new class of security primitive to secure the IJTAG network as an access point for testing and programming. The proposed security primitive expands the properties of a Physically Unclonable Function (PUF) to generate two different responses from a single challenge. The development of such feature was done using the ring counter circuit as the source of randomness of the PUF to increase the efficiency of the proposed PUF. The efficiency of the newly developed PUF was measured by comparing its properties with the properties of a legacy PUF. The randomness test done for the PUF shows that it has a limitation when implemented in sub-nm devices. However, when it was implemented in current 28nm silicon technology, it increases the sensitivity of the PUF as a sensor to detect malicious modification to the FPGA configuration file. Moreover, the efficiency of the developed bimodal PUF increases by 20.4% compared to the legacy PUF. This shows that the proposed security primitive proves to be more dependable and trustworthy than the previously proposed approach.
Open Access
Developing a data and knowledge management approach for Integrated vehicle health management.
(Cranfield University, 2021-10) Alexslis Nyuyfoghan Maindze, Xxx Alexslis; Jennions, Ian K.; Skaf, Zakwan
In Integrated Vehicle Management (IVHM), research and engineering activities are conducted that generate large amounts of data and content. These activities include simulations, observations, derivation, experiments and referencing. However, IVHM still faces a range of data- and Knowledge Management (KM) challenges ranging from data accuracy to long-term availability for prognostic and diagnostic health management. IHVM is data-centric and therefore requires a robust data life cycle management to supports its data- and Knowledge Management activities. An understanding of the concept of KM is fundamental to addressing the IVHM data and knowledge management issues. In this regard, this thesis contextualises ‘Knowledge Management’ for IVHM by attempting to resolve the intellectual paradox that has characterised it over the years. It discusses the origins of Knowledge Management as a discipline and addresses its historical inconsistencies. This review of KM and its origins serves as a scoping study guiding a systematic review of data life cycle models. It reviews relevant standards and their role in the data life cycle. Guided by the V-Model, a Data Life Cycle Model is developed as a result and validated using a multi-technique approach combining peer review and expert insights obtained through a purposive survey. The model is then applied to IVHM centre Knowledge Management System development (KMS). The outcome includes an improved requirements gathering process and a solid foundation for resolving IVHM data and Knowledge Management challenges.
Open Access
The developing field of integrated vehicle health management
(2013-02-04T00:00:00Z) Jennions, Ian K.
The goals that are being set for aviation growth in the near future, combined with the growth in service provision, are unattainable without active health management of airplanes. Numbers associated with door to door travel time and accident rates, coupled with availability demands to provide cost-effective transport, simply do not allow time for unscheduled maintenance. We are therefore going to experience a step jump in the take up of Integrated Vehicle Health Management (IVHM) on these platforms in order to give accurate warning of sub-system and component degradation, allowing for maintenance to be carried out in a timely, scheduled, manner. This paper describes the development of IVHM, covering emerging services, standards, technology and IVHM as used in various industry sectors. This will lead to the commercial picture of today with the top level goals that are being set, providing the business push for technology and its adoption. Examples of research being conducted in the field will be shown, to support the claim that real progress is being made, with implementation of this technology on the horizon.
Open Access
Development of a far-field noise estimation model for an aircraft auxiliary power unit
(IEEE, 2021-09-14) Ahmed, Umair; Ali, Fakhre; Jennions, Ian K.
Aircraft Auxiliary Power Unit (APU) is one of the major aircraft systems and is reported to be a key driver of unscheduled maintenance. So far, the research has been focused on the implementation of the APU thermodynamic state data to isolate and diagnose faults. To advance the available diagnostic techniques, research work has been initiated to explore the potential of employing far-field microphone data for the identification and isolation of APU faults. This paper aims to address the first step required in the overall effort and proposes a novel methodology for the development of a noise model that can be used for evaluating noise as a source of fault diagnostics. The methodology integrates experimentally acquired full-scale aircraft state and noise data, a physics-based APU thermodynamic model, and semi-empirical noise models to estimate the noise produced by an aircraft APU based on a limited parameter-set. The methodology leads to a model which works by estimating the unknown thermodynamic parameters from the limited dataset and then passes on the relevant parameters to noise estimation models (combustion/jet noise models). An inherent part of the model is the effect of multipath propagation and ground reflections for which a relationship has been analytically derived that considers all the necessary parameters. The developed model has been validated against experimental noise and thermodynamic data acquired from a Boeing 737-400 aircraft APU under several different operating conditions. The acquired noise estimates suggest that the proposed approach provides an accurate estimation of the far-field noise under a wide range of APU operating conditions, both at the sub-system and APU level. The model would act as an enabler to simulate APU noise data under degraded functional states and subsequently developing fault diagnostic schemes based on the far-field noise data.
Open Access
Development of a novel ground test facility for aircraft environmental control system
(American Society of Mechanical Engineers, 2023-06-12) Chowdhury, Shafayat Hasan; Ali, Fakhre; Jennions, Ian K.
In this article, the development and experimental investigation of a Boeing 737 aircraft environmental control system (ECS) passenger air conditioner (PACK) has been reported. The PACK is the heart of the ECS that conditions bleed air prior to supplying it to the cabin and avionics bay. Its capability to mask fault occurrences has resulted in increased unscheduled maintenance of the system. As such it has been a key research topic to understand PACK performance characteristics in order to support an accurate diagnostic solution. This article is a continuation of the authors’ work on the development of a systematically derived PACK simulation model and reports the overall development and qualification of a novel in situ ground test facility (GTF) for the experimental investigation of a B737-400 aircraft PACK under various operating modes, including the effect of trim air system. The developed GTF enables the acquisition of the temperature, pressure, and mass flow data throughout the PACK. The overall process of instrumentation selection, installation, sensor uncertainty, and testing in terms of data repeatability and consistency has been reported. The acquired data are then employed to conduct verification and validation of the SESAC (simscape ECS simulation under all conditions) simulation framework. The reported research work therefore enables the advancement in the level of scientific understanding corresponding to the ECS PACK operation under real operating conditions, and therefore supports the development of a robust simulation framework for ECS fault diagnostics at the system level.
Open Access
Development of probability of detection data for structural health monitoring damage detection techniques based on acoustic emission
(Stanford University, 2013-12-12) Gagar, Daniel; Irving, Phil E.; Jennions, Ian K.; Foote, Peter; Read, Ian; McFeat, Jim
Structural Health Monitoring (SHM) techniques have been developed as a cost effective alternative to currently adopted Non-Destructive Testing (NDT) methods which have well understood levels of performance. Quantitative performance assessment, as used in NDT, needs to be applied to SHM techniques to establish their performance levels as a basis for technique comparison and also as a requirement for practical aerospace application according to set regulations. One such measurand is Probability of Detection (POD). This paper reports experiments conducted to investigate the location accuracy of the Acoustic Emission (AE) system in monitoring events from HsuNielson and fatigue crack AE sources as a route to establish the POD of AE in SHM. It was found that fatigue crack tips could be located at 90% POD within 10 mm accuracy.
Open Access
Distributed embedded condition monitoring management systems based on OSA-CBM standard
(Elsevier Science B.V., Amsterdam., 2013-02-28T00:00:00Z) Sreenuch, Tarapong; Tsourdos, Antonios; Jennions, Ian K.
This paper presents an approach to distributed condition monitoring systems that offers a reusable software architecture for a class of condition monitoring (CM) applications. The focus of this paper deals with an open software framework for development of CM applications stemming from 1) the Open System Architecture for Condition Based Maintenance (OSA-CBM) specification, which is an architecture promoting interoperability, and 2) a component framework that enables reuse, data process partitioning, configuration and rapid deployment. The publish/subscribe mechanism is the primary model used for both intra- and inter-module communications. The framework is developed using Java and Remote Method Invocation (RMI) distributed middleware, and its application is demonstrated through a gearbox CM system, where the CM software are deployed on the distributed embedded devices. This approach provides software enabled capability to distribute/re-configure the CM data process (through the OSA-CBM common interface and data model) across the hardware platforms to meet the given system configuration.