CERES :: Browsing by Author "Perinpanayagam, Suresh"

Browsing by Author "Perinpanayagam, Suresh"

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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
Applications of virtual machine using multi-objective optimization scheduling algorithm for improving CPU utilization and energy efficiency in cloud computing
(MDPI, 2022-12-02) Choudhary, Rajkumar; Perinpanayagam, Suresh
Financial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, and thus, require efficient energy consumption and entail a high financial cost. Through the effective utilization of scheduled gaps, the total execution time in a workflow can be decreased by placing uncompleted tasks in the gaps through approximate computations. In the current research, a novel approach based on multi-objective optimization is utilized with CloudSim as the underlying simulator in order to evaluate the VM (virtual machine) allocation performance. In this study, we determine the energy consumption, CPU utilization, and number of executed instructions in each scheduling interval for complex VM scheduling solutions to improve the energy efficiency and reduce the execution time. Finally, based on the simulation results and analyses, all of the tested parameters are simulated and evaluated with a proper validation in CloudSim. Based on the results, multi-objective PSO (particle swarm optimization) optimization can achieve better and more efficient effects for different parameters than multi-objective GA (genetic algorithm) optimization can.
Open Access
A Brief Overview of SiC MOSFET Failure Modes and Design Reliability
(Elsevier, 2017-03-02) Nel, Barry; Perinpanayagam, Suresh
This paper briefly introduces various aspects which should be considered when implementing Silicon Carbide (SiC) based metal-oxide-semiconductor-field-effect-transistors (MOSFETs) into a design. There is an increasing trend regarding the use of these devices in various applications due to their improved performance over conventional Silicon (Si) based devices. The failure modes of SiC MOSFETs are discussed, as well as the indicators which signal device degradation and failure. The impact of packing design on reliability and performance is also discussed along with a number of application related concepts which bring to light some of the issues regarding the use of SiC MOSFETs as a relatively young technology.
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
Certification approach for physics informed machine learning and its application in landing gear life assessment
(IEEE, 2021-11-15) El Mir, Haroun; Perinpanayagam, Suresh
The efficacy of fatigue life approximation methodologies for Landing Gear systems is studied and compared to the ongoing Structural Health Monitoring techniques being researched, which will forecast failures based on the system’s specific life and withstanding abilities, ranging from creating a digital simulation model to applying neural network technologies, in order to simulate and approximate locations and levels of failure along the structure. Explainable Artificial Intelligence allows for the ease-of-integration of Deep Neural Network data into Predictive Maintenance, which is a procedure focused on the health of a system and its efficient upkeep via the use of sensor-based data. Test data from a flight includes a multitude of conditions and varying parameters such as the surface of the landing strip as well as the aircraft itself, requiring the use of Deep Neural Network models for damage assessment and failure anticipation, where compliance to standards is a major question raised, as the EASA AI roadmap is followed, as well as the ICAO and FAA. This paper additionally discusses the challenges faced with respect to standardizing the Explainable AI methodologies and their parameters specifically for the case of Landing Gear.
Open Access
Certification of machine learning algorithms for safe life assessment of landing gear
(Frontiers, 2022-11-15) El Mir, Haroun; Perinpanayagam, Suresh
This paper provides information on current certification of landing gear available for use in the aerospace industry. Moving forward, machine learning is part of structural health monitoring, which is being used by the aircraft industry. The non-deterministic nature of deep learning algorithms is regarded as a hurdle for certification and verification for use in the highly-regulated aerospace industry. This paper brings forth its regulation requirements and the emergence of standardisation efforts. To be able to validate machine learning for safety critical applications such as landing gear, the safe-life fatigue assessment needs to be certified such that the remaining useful life may be accurately predicted and trusted. A coverage of future certification for the usage of machine learning in safety-critical aerospace systems is provided, taking into consideration both the risk management and explainability for different end user categories involved in the certification process. Additionally, provisional use case scenarios are demonstrated, in which risk assessments and uncertainties are incorporated for the implementation of a proposed certification approach targeting offline machine learning models and their explainable usage for predicting the remaining useful life of landing gear systems based on the safe-life method.
Open Access
CNN-fusion architecture with visual and thermographic images for object detection
(SPIE, 2023-06-12) Adiuku, Amaka; Avdelidis, Nicolas Peter; Tang, Gilbert; Plastropoulos, Angelos; Perinpanayagam, Suresh
Mobile robots performing aircraft visual inspection play a vital role in the future automated aircraft maintenance, repair and overhaul (MRO) operations. Autonomous navigation requires understanding the surroundings to automate and enhance the visual inspection process. The current state of neural network (NN) based obstacle detection and collision avoidance techniques are suitable for well-structured objects. However, their ability to distinguish between solid obstacles and low-density moving objects is limited, and their performance degrades in low-light scenarios. Thermal images can be used to complement the low-light visual image limitations in many applications, including inspections. This work proposes a Convolutional Neural Network (CNN) fusion architecture that enables the adaptive fusion of visual and thermographic images. The aim is to enhance autonomous robotic systems’ perception and collision avoidance in dynamic environments. The model has been tested with RGB and thermographic images acquired in Cranfield’s University hangar, which hosts a Boeing 737-400 and TUI hangar. The experimental results prove that the fusion-based CNN framework increases object detection accuracy compared to conventional models.
Open Access
Computationally efficient, real-time, and embeddable prognostic techniques for power electronics
(IEEE, 2014-10-02) Alghassi, Alireza; Perinpanayagam, Suresh; Samie, Mohammad
Power electronics are increasingly important in new generation vehicles as critical safety mechanical subsystems are being replaced with more electronic components. Hence, it is vital that the health of these power electronic components is monitored for safety and reliability on a platform. The aim of this paper is to develop a prognostic approach for predicting the remaining useful life of power electronic components. The developed algorithms must also be embeddable and computationally efficient to support on-board real-time decision making. Current state-of-the-art prognostic algorithms, notably those based on Markov models, are computationally intensive and not applicable to real-time embedded applications. In this paper, an isolated-gate bipolar transistor (IGBT) is used as a case study for prognostic development. The proposed approach is developed by analyzing failure mechanisms and statistics of IGBT degradation data obtained from an accelerated aging experiment. The approach explores various probability distributions for modeling discrete degradation profiles of the IGBT component. This allows the stochastic degradation model to be efficiently simulated, in this particular example ~1000 times more efficiently than Markov approaches.
Open Access
Developing prognostic models using duality principles for DC-to-DC converters
(IEEE, 2019-12-02) Samie, Mohammad; Perinpanayagam, Suresh; Alghassi, Alireza; Motlagh, Amir M. S.; Kapetanios, Epaminondas
Within the field of Integrated System Health Management, there is still a lack of technological approaches suitable for the creation of adequate prognostic model for large applications whereby a number of similar or even identical subsystems and components are used. Existing similarity among a number of different systems, which are comprised of similar components but with different topologies, can be employed to assign the prognostics of one system to other systems using an inference engine. In the process of developing prognostics, this approach will thereby save resources and time. This paper presents a radically novel approach for building prognostic models based on system similarity in cases where duality principle in electrical systems is utilized. In this regard, unified damage model is created based on standard Tee/Pi models, prognostics model based on transfer functions, and remaining useful life (RUL) estimator based on how energy relaxation time of system is changed due to degradation. An advantage is that the prognostic model can be generalized such that a new system could be developed on the basis and principles of the prognostic model of other systems. Simple electronic circuits, dc-to-dc converters, are to be used as an experiment to exemplify the potential success of the proposed technique validated with prognostics models from particle filter.
Open Access
Development of a qualification procedure, and quality assurance and quality control concepts and procedures for repairing and reproducing parts with additive manufacturing in MRO processes
(Cranfield University, 2015-03) Uriondo Del Pozo, Adrian; Perinpanayagam, Suresh
This MSc by Research is focused mainly on Quality Assurance (QA) and Qualification Procedures for metal parts manufactured using new Additive Manufacturing (AM) techniques in the aerospace industry. The main aim is to understand the state of the art of these technologies and the strong regulatory framework of this industry in order to develop correct QA/QC procedures in accordance with the certification process for the technology and spare parts. These include all the testing and validation necessary to implement them in the field, as well as to maintain their capability throughout their lifecycle, specific procedures to manufacture or repair parts, workflows and records amongst others. At the end of this MSc by Research, an entire Qualification Procedure for Electron Beam Melting (EBM) and Selective Laser Melting (SLM) for reproduction of an aerospace part will be developed and defined. Also, General Procedures, Operational Instructions, and Control Procedures with its respective registers, activities, and performance indicators for both technologies will be developed. These will be part of the future Quality Assurance and Quality Management systems of those aerospace companies that implement EBM or SLM in their supply chain.
Open Access
Digital simulation and identification of faults with neural network reasoners in brushed actuators employed in an e-brake system
(MDPI, 2021-10-02) Ramesh, Gouri; Garza, Pablo; Perinpanayagam, Suresh
The aerospace industry is constantly looking to adopt new technologies to increase the performance of the machines and procedures they employ. In recent years, the industry has tried to introduce more electric aircraft and integrated vehicle health management technologies to achieve various benefits, such as weight reduction, lower fuel consumption, and a decrease in unexpected failures. In this experiment, data obtained from the simulation model of an electric braking system employing a brushed DC motor is used to determine its health. More specifically, the data are used to identify faults, namely open circuit fault, intermittent open circuit, and jamming. The variation of characteristic parameters during normal working conditions and when faults are encountered are analysed qualitatively. The analysis is used to select the features that are ideal to be fed into the reasoner. The selected features are braking force, wheel slip, motor temperature, and motor angular displacement, as these parameters have very distinct profiles upon injection of each of the faults. Due to the availability of clean data, a data-driven approach is adopted for the development of the reasoner. In this work, a Long Short-Term Memory Neural Network time series classifier is proposed for the identification of faults. The performance of this classifier is then compared with two others—K Nearest Neighbour time series and Time Series Forest classifiers. The comparison of the reasoners is then carried out in terms of accuracy, precision, recall and F1-score.
Open Access
Digital twin in aerospace industry: a gentle introduction
(IEEE, 2021-12-20) Li, Luning; Aslam, Sohaib; Wileman, Andrew; Perinpanayagam, Suresh
Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly transformative technology with profound implications. Whether it be product development, design optimisation, performance improvement, or predictive maintenance, digital twins are changing the ways work is undertaken in various industries with multifarious business applications. Aerospace industry, including its manufacturing base, is one such keen adopter of digital twins with an unprecedented interest in their bespoke design, development, and implementation across wider operations and critical functions. This, however, comes with some misconceptions about the digital twin technology and lack of understanding with respect to its optimal implementation. For instance, equating a digital twin to an intelligent model while ignoring the essential components of data acquisition and visualisation, misleads the creators into building digital shadow or digital models, instead of the actual digital twin. This paper unfolds such intricacies of digital twin technology for the aerospace community in particular and others in general so as to remove the fallacies that affect their effective realisation for safety-critical systems. It comprises a comprehensive survey of digital twins and their constituent elements. Elaborating their characteristic state-of-the-art composition along with corresponding limitations, three dimensions of the future digital twins for the aerospace sector, termed as aero-Digital Twins (aero-DTs), are proposed as an outcome of this survey. These include the interactive, standardisation, and cognitive dimensions of digital twins, which if leveraged diligently could help the aero-DT research and development community quadruple the efficiency of existing and future aerospace systems as well as their associated processes.
Open Access
Enhanced online identification of battery models exploiting data richness
(IEEE, 2023-05-11) Cai, Chengxi; Auger, Daniel J.; Perinpanayagam, Suresh
The online model parameter identification is essential to ensure the accuracy and dependability of other battery management system (BMS) tasks in the case of battery degradation and operational environment change. Traditional recursive least squares (RLS) algorithms have always been dependent on persistently exciting data, which limits their ability to operate online when this cannot be guaranteed. This paper proposed a modified RLS method that selects the data richest point for parameter identification. In this model, Fisher information matrix and Cramer-Rao bound are utilised to evaluate the data richness. The final algorithms solve the operational limitations of RLS algorithms, enabling a reliable online model parameter identification under real-world dynamic conditions. The identified model parameters from the single cycle dynamic stress test (DST) of an NCM battery are verified by terminal voltage and state of charge (SoC) estimation with the Root Mean Square Error (RMSE) 0.0332 and 0.0131, respectively.
Open Access
A fault detection technique based on deep transfer learning from experimental linear actuator to real-world railway door systems
(PHM Society, 2022-10-28) Shimizu, Minoru; Perinpanayagam, Suresh; Namoano, Bernadin
Fault detection for railway door systems based on data-driven approaches has been investigated in recent years due to the massive amount of available monitoring data. Despite much attention to its application, the major challenge is the lack of available faulty datasets to build a reliable model since railway maintenance is usually conducted regularly to avoid significant defects from economic and safety points of view. We aimed to tackle the issue by employing transfer learning. Firstly, we built a long-short term memory-based deep learning model using linear actuator experimental datasets. Then, we employed a transfer learning technique to adjust the deep learning model to be available to real-world railway door systems using a small amount of faulty data. As a result, high fault detection accuracy can be obtained at 0.979 as F1 score. The result reveals that an accurate fault detection model can be built even though a large amount of labelled datasets is unavailable. In addition, the proposed method is applicable to other door systems or electro-mechanical actuators since the method is unspecific to physical mechanisms and fault modes, and the only motor current signal is used in this research. The signal is primarily available from the controller or motor drive without additional sensors.
Open Access
Fault diagnosis in aircraft fuel system components with machine learning algorithms
(2022-01) Subramanian, Nithya; Starr, Andrew; Perinpanayagam, Suresh
There is a high demand and interest in considering the social and environmental effects of the component’s lifespan. Aircraft are one of the most high-priced businesses that require the highest reliability and safety constraints. The complexity of aircraft systems designs also has advanced rapidly in the last decade. Consequently, fault detection, diagnosis and modification/ repair procedures are becoming more challenging. The presence of a fault within an aircraft system can result in changes to system performances and cause operational downtime or accidents in a worst-case scenario. The CBM method that predicts the state of the equipment based on data collected is widely used in aircraft MROs. CBM uses diagnostics and prognostics models to make decisions on appropriate maintenance actions based on the Remaining Useful Life (RUL) of the components. The aircraft fuel system is a crucial system of aircraft, even a minor failure in the fuel system can affect the aircraft's safety greatly. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance and safety. There are very few diagnostic systems that monitor the health of the fuel system and even fewer that can contain detected faults. The fuel system is crucial for the operation of the aircraft, in case of failure, the fuel in the aircraft will become unusable/unavailable to reach the destination. It is necessary to develop fault detection of the aircraft fuel system. The future aircraft fuel system must have the function of fault detection. Through the information of sensors and Machine Learning Techniques, the aircraft fuel system’s fault type can be detected in a timely manner. This thesis discusses the application of a Data-driven technique to analyse the healthy and faulty data collected using the aircraft fuel system model, which is similar to Boeing-777. The data is collected is processed through Machine learning Techniques and the results are compared
Open Access
Fault Tolerance Enhance DC-DC Converter Lifetime Extension
(Elsevier, 2017-03-02) Alghassi, Alireza; Soulatiantork, Payam; Samie, Mohammad; Uriondo Del Pozo, Adrian; Perinpanayagam, Suresh; Faifer, M.
One of the most crucial renewable energy sources today is solar energy. Power convertors play an important role in adjusting the output voltage or current of photovoltaic (PV) systems. Using efficient and reliable switches for power converters and inverters is crucial for enhancing the safety and reliability of a platform. Generally, power converters suffer from failure mechanisms, such as wire bond fatigue, wire bond lift up, solder fatigue and loose gate control voltage, which mainly occur in power switches. In this paper, the junction temperature of the Insulated Gate Bipolar Transistor (IGBT) acting as a power switch used in the Impedance-Source DC-DC converter is estimated using an electro-thermal model in order to develop an adaptive thermal stress control (ATSC). The proposed stress control adjusts reference input of the PI control to extend the life expectancy of the device under the mission. The accuracy of results present using The Modified Coffin-Manson Law has been used to determine the life of IGBT and the lifetime has been successfully increased base on implementing imperative ATSC and comparing the result with the constant reference input of the PI controller. The result integrates with converter health management to develop advanced intelligent predictive maintenance.
Open Access
Health management design considerations for an all electric aircraft
(Elsevier, 2017-03-02) Sebastian, Robin K.; Perinpanayagam, Suresh; Choudhary, Raj
This paper explains the On-board IVHM system for a State-Of-the-Art “All electric aircraft” and explores implementing practices for analysis based design, illustrations and development of IVHM capabilities. On implementing the system as an on board system will carry out fault detection and isolation, recommend maintenance action, provides prognostic capabilities to highest possible problems before these became critical. The vehicle Condition Based Maintenance (CBM) and adaptive control algorithm development based on an open architecture system which allow “Plug in and Plug off” various systems in a more efficient and flexible way. The scope of the IVHM design included consideration of data collection and communication from the continuous monitoring of aircraft systems, observation of current system states, and processing of this data to support proper maintenance and repair actions. Legacy commercial platforms and HM applications for various subsystems of these aircraft were identified. The list of possible applications was down-selected to a reduced number that offer the highest value using a QFD matrix based on the cost benefit analysis. Requirements, designs and system architectures were developed for these applications. The application areas considered included engine, tires and brakes, pneumatics and air conditioning, generator, and structures. IVHM design program included identification of application sensors, functions and interfaces; IVHM system architecture, descriptions of certification requirements and approaches; the results of a cost/benefit analyses and recommended standards and technology gaps. The work concluded with observations on nature of HM, the technologies, and the approaches and challenges to its integration into the current avionics, support system and business infrastructure. The IVHM design for All Electric Hybrid Wing Body (HWB) Aircraft has a challenging task of addressing and resolving the shortfalls in the legacy IVHM framework. The challenges like sensor battery maintenance, handling big data from SHM, On-Ground Data transfer by light, Extraction of required features at sensor nodes/RDCUs, ECAM/EICAS Interfaces, issues of certification of wireless SHM network has been addressed in this paper. Automatic Deployable Flight Data recorders are used in the design of HWB aircraft in which critical flight parameters are recorded. The component selection of IVHM system including software and hardware have been based on the COTS technology. The design emphasis on high levels of reliability and maintainability. The above systems are employed using IMA and integrated on AFDX data bus. The design activities has to pass through design reviews on systematic basis and the overall approach has been to make system highly lighter, effective “All weather” compatible and modular. It is concluded from the study of advancement in IVHM capabilities and new service offerings that IVHM technology is emerging as well as challenging. With the inclusion of adaptive control, vehicle condition based maintenance and pilot fatigue monitoring, IVHM evolved as a more proactively involved on-board system.
Open Access
IGBT thermal stress reduction using advance control strategy
(Elsevier, 2017-03-02) Soulatiantork, Payam; Alghassi, Alireza; Faifer, Marco; Perinpanayagam, Suresh
Next-generation advances in stress control strategy will enable renewable energies, such as solar energy, to become more reliable and available. Critical components, such as power electronics, present uncertainties to the system control in malfunctioning process, which reduces the target of more clean energy development and CO2 emission reduction. Thus, developing and harnessing sustainable energy requires mitigating the impact of the variability of the source of energy and the impact of the adaptive stress control deployed for the proportional, integral, derivative (PID) controller to minimize the thermal stress in the power switch insulated gate bipolar transistor (IGBT). In response to this challenge, a fuzzy linear matrix inequality (FLMI) PID controller proposes initiatives for customizing parameters of PID controller corresponding to the uncertainty of IGBTs. In this paper, the uncertainty of the boost converter has been evaluated in the dynamic of the LMI model and Takagi-Sugino (TS) has applied in closed loop control to overcome the instability of the Boost converter parameters. Paper originally presented at the 5th International Conference in Through-life Engineering Services Cranfield University, 1st and 2nd November 2016.
Open Access
Ingress of threshold voltage-triggered hardware trojan in the modern FPGA fabric–detection methodology and mitigation
(IEEE, 2020-02-11) Aslam, Sohaib; Jennions, Ian K.; Samie, Mohammad; Perinpanayagam, Suresh; Fang, Yisen
The ageing phenomenon of negative bias temperature instability (NBTI) continues to challenge the dynamic thermal management of modern FPGAs. Increased transistor density leads to thermal accumulation and propagates higher and non-uniform temperature variations across the FPGA. This aggravates the impact of NBTI on key PMOS transistor parameters such as threshold voltage and drain current. Where it ages the transistors, with a successive reduction in FPGA lifetime and reliability, it also challenges its security. The ingress of threshold voltage-triggered hardware Trojan, a stealthy and malicious electronic circuit, in the modern FPGA, is one such potential threat that could exploit NBTI and severely affect its performance. The development of an effective and efficient countermeasure against it is, therefore, highly critical. Accordingly, we present a comprehensive FPGA security scheme, comprising novel elements of hardware Trojan infection, detection, and mitigation, to protect FPGA applications against the hardware Trojan. Built around the threat model of a naval warship’s integrated self-protection system (ISPS), we propose a threshold voltage-triggered hardware Trojan that operates in a threshold voltage region of 0.45V to 0.998V, consuming ultra-low power (10.5nW), and remaining stealthy with an area overhead as low as 1.5% for a 28 nm technology node. The hardware Trojan detection sub-scheme provides a unique lightweight threshold voltage-aware sensor with a detection sensitivity of 0.251mV/nA. With fixed and dynamic ring oscillator-based sensor segments, the precise measurement of frequency and delay variations in response to shifts in the threshold voltage of a PMOS transistor is also proposed. Finally, the FPGA security scheme is reinforced with an online transistor dynamic scaling (OTDS) to mitigate the impact of hardware Trojan through run-time tolerant circuitry capable of identifying critical gates with worst-case drain current degradation.
Open Access
Intermittent fault diagnosis and health monitoring for electronic interconnects
(Cranfield University, 2017-03) Ahmad, Syed Wakil; Perinpanayagam, Suresh
Literature survey and correspondence with industrial sector shows that No-Fault-Found (NFF) is a major concern in through life engineering services, especially for defence, aerospace, and other transport industry. There are various occurrences and root causes that result in NFF events but intermittent interconnections are the most frustrating. This is because it disappears while testing, and missed out by diagnostic equipment. This thesis describes the challenging and most important area of intermittent fault detection and health monitoring that focuses towards NFF situation in electronics interconnections. After introduction, this thesis starts with literature survey and describes financial impact on aerospace and other transport industry. It highlights NFF technologies and discuss different facts and their impact on NFF. Then It goes into experimental study that how repeatedly intermittent fault could be replicated. It describes a novel fault replicator that can generate repeatedly IFs for further experimental study on diagnosis techniques/algorithms. The novel IF replicator provide for single and multipoint intermittent connection. The experimental work focuses on mechanically induced intermittent conditions in connectors. This work illustrates a test regime that can be used to repeatedly reproduce intermittency in electronic connectors whilst subjected to vibration ... [cont.].