Browsing by Author "Jennions, Ian"
Now showing 1 - 19 of 19
Results Per Page
Sort Options
Item Open Access Aircraft predictive maintenance modeling using a hybrid imbalance learning approach(SSRN, 2020-10-26) Dangut, Maren David; Skaf, Zakwan; Jennions, IanThe continued development of the industrial internet of things (IIoT) has caused an increase in the availability of industrial datasets. The massive availability of assets operational dataset has prompted more research interest in the area of condition-based maintenance, towards the API-lead integration for assets predictive maintenance modelling. The large data generated by industrial processes inherently comes along with different analytical challenges. Data imbalance is one of such problems that exist in datasets. It affects the performance of machine learning algorithms, which yields imprecise prediction. In this paper, we propose an advanced approach to handling imbalance classification problems in equipment heterogeneous datasets. The technique is based on a hybrid of soft mixed Gaussian processes with the EM method to improves the prediction of the minority class during learning. The algorithm is then used to develop a prognostic model for predicting aircraft component replacement. We validate the feasibility and effectiveness of our approach using real-time aircraft operation and maintenance datasets. The dataset spans over seven years. Our approach shows better performance compared to other similar methods.Item Open Access Aircraft system-level diagnosis with emphasis on maintenance decisions(SAGE, 2021-10-26) Skliros, Christos; Ali, Fakhre; King, Steve; Jennions, IanThis paper proposes a diagnostic technique that can predict component degradation for a number of complex systems. It improves and clarifies the capabilities of a previously proposed diagnostic approach, by identifying the degradation severity of the examined components, and uses a 3D Principal Component Analysis approach to provide an explanation for the observed diagnostic accuracy. The diagnostic results are then used, in a systematic way, to influence maintenance decisions. Having been developed for the Auxiliary Power Unit (APU), the flexibility and power of the diagnostic methodology is shown by applying it to a completely new system, the Environmental Control System (ECS). A major conclusion of this work is that the proposed diagnostic approach is able to correctly predict the health state of two aircraft systems, and potentially many more, even in cases where different fault combinations result in similar fault patterns. Based on the engineering simulation approach verified here, a diagnostic methodology suitable from aircraft conception to retirement is proposed.Item Open Access Assessment of heat exchanger degradation in a Boeing 737-800 environmental control system(ASME, 2021-04-02) Jennions, Ian; Ali, FakhreThere are a number of systems on an aircraft working together, in harmony, to produce safe and trouble free flight. The Environmental Control System (ECS) is one of these systems, and its failure is a major contributor to unscheduled maintenance, particularly in older aircraft. The ECS is composed of several complex sub-systems and components, but at its heart is the passenger air conditioner (PACK). The PACK is prone to degradation, which can lead to the functional failure of the ECS. Often its degradation is masked by the overall ECS control system and this can, ultimately, result in the ECS shutting down and extensive maintenance being required. There are a number of critical fault modes associated with the PACK, and in this paper those modes associated with the primary and secondary heat exchangers are explored. A robust ECS simulation framework called SESAC (Simscape ECS Simulation under All Conditions) has previously been implemented, calibrated, and tested, against data from healthy systems. Here the simulations are extended to cover degraded components in a representative Boeing 737-800 aircraft PACK model. Fault modes such as blockage and fouling are assessed for the primary and secondary heat exchangers of the PACK. Simulation results, in terms of temperature, pressure and mass flow at various degradation severities, are presented and discussed. The results highlight the interdependency between the PACK components, and the strong association between the primary and secondary heat exchanger performance.Item Open Access A composite learning approach for multiple fault diagnosis in gears(SAGE, 2022-11-05) Inyang, Udeme Ibanga; Petrunin, Ivan; Jennions, IanA major part of Prognostic and Health Management of rotating machines is dedicated to diagnosis operations. This makes early and accurate diagnosis of single and multiple faults an economically important requirement of many industries. With the well-known challenges of multiple faults, this paper proposes a new Blended Ensemble Convolutional Neural Network – Support Vector Machine (BECNN-SVM) model for multiple and single faults diagnosis of gears. The proposed approach is obtained by preprocessing the acquired signals using complementary signal processing techniques. This form inputs to 2D Convolutional Neural Network base learners which are fused through a blended ensemble model for fault detection in gears. Discriminative properties of the complementary features ensure the high capabilities of the approach to give good results under different load, speed, and fault conditions of the gear system. The experimental results show that the proposed method can accurately detect rotating machine faults. The proposed approach compared with other state-of-the-art methods indicates improved overall effectiveness for gear faults diagnosis.Item Open Access Development and Implementation of a Framework for Aerospace Vehicle Reasoning (FAVER)(IEEE, 2021-07-28) Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, IanThis paper discusses the development and implementation of the architecture of a Framework for A erospace Ve hicle R easoning, ‘FAVER’. Integrated Vehicle Health Management systems require a holistic view of the aircraft to isolate faults cascading between aircraft systems. FAVER is a system-agnostic framework developed to isolate such propagating faults by incorporating Digital Twins (DTs) and reasoning techniques. The flexibility of FAVER to work with different types and scales of DTs and diagnostics, and its ability to adapt and expand for previously unknown faults and new systems are demonstrated in this paper. The paper also shows the novel combination of relationship matrix and fault attributes database used to structure the knowledge of FAVER’s expert system. The paper provides the working mechanism of FAVER’s reasoning and its ability to isolate faults at the system level, identify their root causes, and predict the cascading effects at the vehicle level. Four aircraft systems are used for demonstration purposes: i) the Electrical Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control System, and the use case scenarios are adapted from real aircraft incidents. The paper also discusses the pros and cons of FAVER’s reasoning via demonstrations and evaluates the performance of FAVER’s reasoning through a comparative study with a supervised neural network model.Item Open Access Diagnosis of multiple faults in rotating machinery using ensemble learning(MDPI, 2023-01-15) Inyang, Udeme Ibanga; Petrunin, Ivan; Jennions, IanFault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.Item Open Access Evaluation of CAN bus security challenges(MDPI, 2020-04-21) Bozdal, Mehmet; Samie, Mohammad; Aslam, Sohaib; Jennions, IanThe automobile industry no longer relies on pure mechanical systems; instead, it benefits from many smart features based on advanced embedded electronics. Although the rise in electronics and connectivity has improved comfort, functionality, and safe driving, it has also created new attack surfaces to penetrate the in-vehicle communication network, which was initially designed as a close loop system. For such applications, the Controller Area Network (CAN) is the most-widely used communication protocol, which still suffers from various security issues because of the lack of encryption and authentication. As a result, any malicious/hijacked node can cause catastrophic accidents and financial loss. This paper analyses the CAN bus comprehensively to provide an outlook on security concerns. It also presents the security vulnerabilities of the CAN and a state-of-the-art attack surface with cases of implemented attack scenarios and goes through different solutions that assist in attack prevention, mainly based on an intrusion detection system (IDS)Item Open Access Evaluation of component level degradation in the Boeing 737-800 air cycle machine(American Society of Mechanical Engineers, 2022-12-19) Jennions, Ian; Ali, FakhreAn aircraft is composed of several highly integrated and complex systems that enable it to deliver safe and comfortable flight. Its functionality is therefore strongly dependent on the safe operation of these systems within their designed optimal efficiencies. The air cycle machine (ACM) is a subsystem of the pressurized air conditioner (PACK) system, its key function is to enable refrigeration of the air in order to comply with the wide range of cabin environment requirements for maintaining aircraft safety and passenger comfort. The operation of the ACM is governed by the PACK control system which can mask degradation in its component during operation until severe degradation or failure results. The required maintenance is then both costly and disruptive. The ACM has been reported as one of the most frequently replaced subsystem and has been therefore reported as a major driver of unscheduled maintenance by the operators. This paper aims to investigate the component level degradation in the ACM at various severities and quantify the impact of its performance characteristics and associated interdependencies at PACK system level. In this paper, Cranfield University’s in-house environmental control system (ECS) simulation framework called simscape ECS simulation under all conditions (SESAC) has been implemented to evaluate degradation in the ACM components in a representative Boeing 737-800 aircraft PACK model. The fault modes of interest are those highlighted by the operators and correspond to the ACM compressor, turbine, and interconnecting mechanical shaft efficiency degradation. Simulation results, in terms of temperature, pressure, and mass flow at various degradation severities, are presented and discussed for each component at PACK system level. The acquired results suggest that, for all three fault modes, the PACK controller can compensate for an ACM degradation severity of up to 20%, allowing the PACK to sustain the delivery of the demanded temperature and mass flow. For degradation severity of above 20%, the PACK is able to deliver the demanded temperature with a substantially reduced mass flow. This has a significant impact on the PACK’s ability to meet the cabin demand efficiently. The methodology reported and the findings conceived to serve as an enabler toward formulating an effective PACK fault diagnostics and condition monitoring solution at system level, and fault reasoning at vehicle level.Item Open Access Fault simulations and diagnostics for a Boeing 747 Auxiliary Power Unit(Elsevier, 2021-07-01) Skliros, Christos; Ali, Fakhre; Jennions, IanHealth monitoring of aircraft systems is of great interest to aircraft manufacturers and operators because it minimises the aircraft downtime (due to avoiding unscheduled maintenance), which in turn reduces the operating costs. The work that is presented in this paper explores, for a Boeing 747 APU, fault simulation and diagnostics for single and multiple component faults. Data that corresponds to healthy and faulty conditions is generated by a calibrated simulation model, and a set of performance parameters (symptom vector) are selected to characterise the components health state. For each component under examination, a classification algorithm is used to identify its health state (healthy or faulty) and the training strategy that is used considers the existence of multiple faults in the system. The proposed diagnostic technique is tested against single and multiple fault cases and shows good results for the compressor, turbine, Load Control Valve (LCV) and Fuel Metering Valve (FMV), even though these faults present similar fault patterns. On the contrary, the classifiers for the Speed Sensor (SS) and the generator do not provide reliable predictions. As regards the SS, the sensitivity assessment for this component showed that the existence of faults in the other components can sometimes mask the SS fault. The reason that the generator diagnosis fails under the proposed diagnostic technique is attributed to the fact that it has only a very slight influence on the other symptom vector parameters. In both cases, additional diagnostic strategies are suggested.Item Open Access Hardware trojan enabled denial of service attack on CAN bus(Elsevier, 2018-11-02) Bozdal, Mehmet; Randa, Maulana; Samie, Mohammad; Jennions, IanThe trend of technological advances in the vehicle industry illustrates that future cars would have added functionalities with smart features, better connectivity and autonomous behaviour. These naturally involve a higher number of Electronic Control Units (ECUs) being connected using existing conventional in-vehicle network protocols such as Controller Area Network (CAN). In this context, security of systems is now becoming a major concern while industry’s primary interest in the manufacturing of cars is reliability and safety. It is now in daily news that smart cars are being hacked due to weaknesses in their embedded electronics that provides ways of hardware attacks [1] [2]. Hardware Trojan (HT) is the threat that has been recently recognised as one of the primary sources of backdoor access that enables hackers to attack systems. As trouble, HT remains silent until a rare function/event triggers it for activation. This paper contributes to the challenge of demonstration of disruption in CAN buses raised from hidden Hardware Trojan. In this regard, it is presented how just a small size Hardware Trojan disrupts the CAN bus communication without an adversary having physical access to the bus. The attack is neither detectable via frame analysis, nor can be prevented via network segmentation; additionally, a rare triggering mechanism activates HT to process untraceable faults.Item Open Access Health condition estimation of bearings with multiple faults by a composite learning-based approach(MDPI, 2021-06-28) Inyang, Udeme; Petrunin, Ivan; Jennions, IanBearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.Item Open Access Intelligent fault diagnosis of an aircraft fuel system using machine learning - a literature review(MDPI, 2023-04-16) Li, Jiajin; King, Stephen; Jennions, IanThe fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research.Item Open Access Progress towards a Framework for Aerospace Vehicle Reasoning (FAVER)(PHM Society, 2019-09-23) Ezhilarasu, Cordelia Mattuvarkuzhali; Skaf, Zakwan; Jennions, IanThis paper proposes a reasoning framework to diagnose faults at the vehicle level in a complex machine like an aircraft. The current focus of Integrated Vehicle Health Management (IVHM) is on diagnosing and prognosing faults at the component and subsystem levels; only a few IVHM systems consider the interaction between the systems. To diagnose faults at the vehicle level, an IVHM System needs a framework that recognizes the causal relationships between systems and the likelihood of fault propagation between them. The framework should also possess an element of reasoning to assess data from all systems, to assign priorities, and to resolve ambiguities. The Framework for Aerospace VEhicle Reasoning (FAVER) that is proposed in this paper uses a digital twin of the aircraft systems to emulate functioning of the aircraft and to simulate the effect of fault propagation due to systems interactions. FAVER applies reasoning that can handle fault signatures from multiple systems in the form of symptom vectors, to detect and isolate cascading faults and their root causes. The blending of a digital twin and reasoning in this framework will enable FAVER to: i) isolate faults that have both local and cascading effects on the concerned systems, ii) identify faults that were previously unknown, and iii) resolve ambiguous faults. This paper explains the different steps involved in developing FAVER and how this framework can be demonstrated in the aforementioned scenarios with the help of different use cases. This paper also talks about the challenges to be faced while developing this framework and ways to overcome them.Item Open Access A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurem(Elsevier, 2021-04-30) Ahmed, Umair; Ali, Fakhre; Jennions, IanThe Auxiliary Power Unit (APU) is an integral part of an aircraft, providing electrical and pneumatic power to various on-board sub-systems. APU failure results in delay or cancellation of a flight, accompanied by the imposition of hefty fines from the regional authorities. Such inadvertent situations can be avoided by continuously monitoring the health of the system and reporting any incipient fault to the MRO (Maintenance Repair and Overhaul) organization. Generally, enablers for such health monitoring techniques are embedded during a product's design. However, a situation may arise where only the critical components are regularly monitored, and their status presented to the operator. In such cases, efforts can be made during service to incorporate additional health monitoring features using the already installed sensing mechanisms supplemented by maintenance data or by instrumenting the system with appropriate sensors. Due to the inherently critical nature of aircraft systems, it is necessary that instrumentation does not interfere with a system's performance and does not pose any safety concerns. One such method is to install non-intrusive vibroacoustic sensors such that the system integrity is maintained while maximizing system fault diagnostic knowledge. To start such an approach, an in-depth literature survey is necessary as this has not been previously reported in a consolidated manner. Therefore, this paper concentrates on auxiliary power units, their failure modes, maintenance strategies, fault diagnostic methodologies, and their acoustic signature. The recent trend in APU design and requirements, and the need for innovative fault diagnostics techniques and acoustic measurements for future aircraft, have also been summarized. Finally, the paper will highlight the shortcomings found during the survey, the challenges, and prospects, of utilizing sound as a source of diagnostics for aircraft auxiliary power units.Item Open Access A review of model based and data driven methods targeting hardware systems diagnostics(PTDT, 2018-11-22) Skliros, Christos; Miguez, Manuel Esperon; Fakhre, Ali; Jennions, IanSystem health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls.Item Open Access Signal processing of acoustic data for condition monitoring of an aircraft ignition system(MDPI, 2022-09-19) Ahmed, Umair; Ali, Fakhre; Jennions, IanDegradation of the ignition system can result in startup failure in an aircraft’s auxiliary power unit. In this paper, a novel acoustics-based solution that can enable condition monitoring of an APU ignition system was proposed. In order to support the implementation of this research study, the experimental data set from Cranfield University’s Boeing 737-400 aircraft was utilized. The overall execution of the approach comprised background noise suppression, estimation of the spark repetition frequency and its fluctuation, spark event segmentation, and feature extraction, in order to monitor the state of the ignition system. The methodology successfully demonstrated the usefulness of the approach in terms of detecting inconsistencies in the behavior of the ignition exciter, as well as detecting trends in the degradation of spark acoustic characteristics. The identified features proved to be robust against non-stationary background noise, and were also found to be independent of the acoustic path between the igniter and microphone locations, qualifying an acoustics-based approach to be practically viable.Item Open Access Simulation of an aircraft environmental control system(Elsevier, 2020-01-09) Jennions, Ian; Ali, Fakhre; Miguez, Manuel Esperon; Escobar, Ignacio CamachoThe environmental control system of a civil aircraft is a major driver of maintenance. Legacy systems, such as those on the Boeing 737, are particularly at risk, as they are not instrumented for health management. These systems degrade in operation and allow compensation within their operation for degrading components, until severe degradation or failure results. The required maintenance is then both costly and disruptive. The goal of this research is to produce a simulation environment that can model the aircraft environmental control system, in order that analysis for sensor placement and algorithms can be performed without extensive, and expensive, testing. A simulation framework called Simscape Environmental Control System Simulation under All Conditions has been proposed and implemented. It offers a library of components that can be assembled into specific aircraft environmental control system simulation configurations. It is capable of simulating the health state indicating parameters at sub-system and component levels under a wide-range of aircraft operating scenarios. The developed framework has been successfully implemented to simulate a Boeing 737-800 passenger air conditioner. Its verification and validation has been carried out against the actual data corresponding to a Boeing 737-800 passenger air conditioner operating at two different cruise operating points. An extensive comparison of the simulation is presented against the data for all the passenger air conditioner components. The overall acquired results suggest that changes in the aircraft ambient conditions can have a noticeable impact on the demanded passenger air conditioner outlet temperature, and a substantial impact on the heat transfer in the primary and secondary heat exchangers. The reported simulation capability serves as a first step towards formulating an environmental control system fault simulation and diagnostic solution.Item Open Access A survey on CAN bus protocol: attacks, challenges, and potential solutions(IEEE, 2019-03-07) Bozdal, Mehmet; Samie, Mohammad; Jennions, IanThe vehicles are equipped with electronic control units that control their functions. These units communicate with each other via in-vehicle communication protocols like CAN bus. Although CAN is the most common in-vehicle communication protocol, its lack of encryption and authentication can cause serious security shortcomings. In the literature, many attacks are reported related to CAN bus and the number increases with rising connectivity in the cars. In this paper, we present CAN protocol and analyze its security vulnerabilities. Then we survey the implemented attacks and proposed solutions in the literature.Item Open Access Understanding the role of a Digital Twin in Integrated Vehicle Health Management (IVHM)(IEEE, 2019-11-28) Ezhilarasu, Cordelia Mattuvarkuzhali; Skaf, Zakwan; Jennions, IanIntegrated Vehicle Health Management (IVHM) aims to support Condition-Based Maintenance (CBM) by monitoring, diagnosing, and prognosing the health of the host system. One of the technologies required by IVHM to carry out its objectives is the means to emulate the functioning of the host system, and the concept of a Digital Twin (DT) was introduced in aerospace IVHM to represent the functioning of such a complex system. This paper aims to discuss the role played by DT in the field of IVHM. A DT is the virtual representation of any physical product, that is used to project the functioning of the product at a given instance. The DT is used across the lifecycle of any product, and its output can be customized depending upon the area of application. The DT is currently popular in industry because of the technologies like sensors, cloud computing, Internet of Things, machine learning, and advanced software, which enabled its development. This paper discusses what encompasses a DT, the technologies that support the DT, its applications across industries, and its development in academia. This paper also talks about how a DT can combine with IVHM technology to assess the health of complex systems like an aircraft. Lastly, this paper presents various challenges faced by industry during the implementation of a DT and some of the possible opportunities for future growth.