An Integrated Fuzzy Inference Based Monitoring, Diagnostic, and Prognostic System

To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable grab bag of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ~45 hours and 95% CIs of ~162 hours

Collaborative prognostics in Social Asset Networks

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the fi eld of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must nd a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, de ned as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares t of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.EU H202

NOVELTY DETECTION FOR PREDICTIVE MAINTENANCE

Since the advent of Industry 4. 0 significant research has been conducted to apply machine learning to the vast array of Internet of Things (IoT) data produced by Industrial Machines. One such topic is to Predictive Maintenance. Unlike some other machine learning domains such as NLP and computer vision, Predictive Maintenance is a relatively new area of focus. Most of the published work demonstrates the effectiveness of supervised classification for predictive maintenance. Some of the challenges highlighted in the literature are the cost and difficulty of obtaining labelled samples for training. Novelty detection is a branch of machine learning that after being trained on normal operations detects if new data comes from the same process or is different, eliminating the requirement to label data points. This thesis applies novelty detection to both a public data set and one that was specifically collected to demonstrate a its application to predictive maintenance. The Local Optimization Factor showed better performance than a One-Class SVM on the public data. It was then applied to data from a 3-D printer and was able to detect faults it had not been trained on showing a slight lift from a random classifier

Data-Driven Prognostics in Industrial Service Business

There is a shift in the manufacturing industries in which original equipment manufacturers (OEM) are gaining increasingly large portion of their revenue from services rather than the manufacturing of goods. This change is called servitisation. Additionally, the advancements in information technology are opening new possibilities and opportunities, such as in how data can be processed, analysed and used to create data-driven applications to support the business functions. The possibilities are, however, still largely unexploited especially in the field of maintenance services. The data-driven prognostics could not only enhance the existing maintenance activities, but also create new ways of partnership and service development between the OEMs and their clients. This could induce further growth and increase in the servitisation level. However, there is lack of insight of how the methods could be applied to practice; especially case studies are few in quantity. Hence, this study aims to increase understanding of the practical application of the data to support maintenance service business. This study examines the application of data-driven methods, mainly machine learning, to aid valve maintenance business of a service providing OEM. The aim is to create a data-driven system to forecast failures in devices and generate automated service recommendations. The forecasting was based on idea that the failures would induce a detectable pattern in the measured data prior a failure. The chosen machine learning method, the neural networks, excel in this kind of task and hence can predict failures. The study is conducted in practical setting as a case study with real data. Various systems and processes were examined, and data was extracted for analysis. With this data several models for prediction were built. However, the accuracy of these was ultimately deemed insufficient for generation of service recommendations and hence all the set goals were not fully reached. As the greatest contributing factors for the poor performance of the forecasts, the data itself and the operations related to it were identified. The data was hard to access and lacking both in quality and quantity as it is recorded, stored and managed with day-to-day operations in mind. As result, we found that significant portions of data were deleted or were recorded with accuracy insufficient for this research. However, through the analysis of these factors several concrete points of development emerged. The outcome of this study also confirms the inherent challenges regarding service partnering and intercompany data-transfer presented in literature. A need for standardised and light-weight legal frameworks and methods of data sharing was identified. Without these, the potential may not be fully realisable in practice and hence more case practically oriented studies on the subject are required. To conclude, the OEM had too optimistic view of the availability, quality and quantity of data, which resulted in an attempt, which did not reach all the set goals. On the other hand, the academic literature shows that there is great potential in these methods. Data refined into wisdom which may support decisions and actions can facilitate value generation in services. The findings encourage OEM to improve the collection, storage and management of data and other organisations to carefully evaluate whether their capabilities are sufficient

The development of an action priority matrix and technology roadmap for the implementation of data-driven and machine-learning-based predictive maintenance in the South African railway industry

Presented at the 2nd International Conference on Industrial Engineering, Systems Engineering and Engineering Management, held from 2 to 4 October 2023 in Somerset West, South Africa.In improving railways for the future, artificial intelligence and machine learning were identified as top-priority technology systems that enable data-driven methods and predictive maintenance. A local survey using semi-structured interviews showed that the railway industry lags behind in adopting and implementing data-driven and machine-learning methods for predictive maintenance. Insights from international studies were found to be relevant in South Africa. Other implementation barriers were identified in the socio-economic and socio-political areas of South Africa. An action priority matrix and technology roadmap was developed to guide the South African railway industry towards the implementation of data-driven and machine learning-based predictive maintenance. The action priority matrix was developed by using a two-round Delphi technique to rank the prioritisation of the required activities. The research showed the importance of considering insights from both international studies and the local context when adopting and implementing technology systems to improve business objectives.In die verbetering van spoorweë vir die toekoms, is kunsmatige intelligensie en masjienleer geïdentifiseer as top-prioriteit tegnologiestelsels wat data-gedrewe metodes en voorspellende instandhouding moontlik maak. 'n Plaaslike opname wat semi-gestruktureerde onderhoude gebruik het, het getoon dat die spoorwegbedryf agterbly met die aanvaarding en implementering van datagedrewe- en masjienleermetodes vir voorspellende instandhouding. Daar is gevind dat insigte uit internasionale studies relevant is in Suid-Afrika. Ander implementeringshindernisse is in die sosio-ekonomiese en sosio-politieke gebiede van Suid-Afrika geïdentifiseer. ’n Aksieprioriteitmatriks en tegnologie-padkaart is ontwikkel om die Suid-Afrikaanse spoorwegbedryf te lei tot die implementering van datagedrewe- en masjienleer-gebaseerde voorspellende instandhouding. Die aksieprioriteitmatriks is ontwikkel deur 'n twee-rondte Delphi-tegniek te gebruik om die prioritisering van die vereiste aktiwiteite te rangskik. Die navorsing het getoon hoe belangrik dit is om insigte uit beide internasionale studies en die plaaslike konteks in ag te neem wanneer tegnologiestelsels aangeneem en geïmplementeer word om besigheidsdoelwitte te verbeter.http://sajie.journals.ac.za/pubam2024Graduate School of Technology Management (GSTM)SDG-09: Industry, innovation and infrastructur

Design for prognostics and security in field programmable gate arrays (FPGAs).

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.Samie, Mohammad (Associate)PhD in Transport System

Specification and Evaluation of Prediction Concepts in Aircraft Maintenance

The goal of this thesis is to identify and quantify the potentials of predictive maintenance concepts in civil aviation. Fault prediction-based decision support is expected to optimise the effectiveness and efficiency of maintenance. Thus, it also enables further reduction of an airline's operating costs over the aircraft life cycle. Prediction aims to transform unscheduled maintenance events, often causing operational irregularities, into projectable preventive activities. Thereby, aircraft availability as well as maintenance processes are expected to be optimised. Because of low technology readiness levels in the industry as well as rather global scale scientific publications on predictive maintenance, the presented work aims to analyse its implementation's potentials in a more detailed manner. Thus, a decision-supporting tool for the cost-benefit assessment of predictive maintenance opposed to the initial state is to be developed. The starting point is literature research with respect to today's standards and characteristics in civil aviation aircraft maintenance. This includes the applied maintenance strategies and cost structures in particular. Thereafter, the state-of-the-art concerning fault prediction and relevant performance metrics is presented. Subsequently, the proposed evaluation concept is introduced. Two functions are to be covered: Firstly, based on information on today's maintenance, cost reduction potentials as well as minimum requirements with respect to prediction can be derived. Secondly, prediction concepts can be assessed concerning their specific cost-benefit characteristics. Whereas the evaluation focus lies on the effected costs, corresponding time- and ratio-based target values are analysed as well. A unique feature of the method is the use of mostly deterministic data enabling the derivation of more accurate and valid results than probabilistic approaches. Thereafter, the proposed model's software implementation is described. Based on theoretically defined business process and data models, a simulation model is built. The proposed model accounts for prediction-induced modifications within the aircraft maintenance as well as the interdependencies of the aircraft operations. By means of Monte-Carlo simulation, input data uncertainties are accounted for and processed for a statistical results assessment. In a case study, results of the method's application are presented. Firstly, the calibration of estimated process efforts by means of available real-world maintenance information is conducted. This enables the model validation as well. Thereafter, the analysis results concerning an exemplary aircraft component that is maintained correctively are presented. This real-world data is then counterposed to target values derived from the simulation of prediction-based maintenance approaches. It can be concluded that a predictive maintenance strategy's benefit depends on the amount or the ratio of the interdependent prediction errors, the prediction forecast as well as the costs of implementation. Among the prediction errors, it is necessary to distinguish between errors negatively affecting aircraft operations and errors having negative impact on aircraft maintenance activities. A longer forecast increases the ability to plan in advance, while also leading to higher prediction error rates. It is shown how the overall cost-benefit is affected by investment and operating costs of a predictive strategy's implementation. Only when the derived break-even thresholds are underrun, will the cost-benefit turn out positive as compared to the initial-state maintenance. The overall optimum incorporates a prediction model with the least costly parameter setting

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Subsystem architecture sizing and analysis for aircraft conceptual design

In traditional aircraft conceptual design, subsystems are largely accounted for implicitly based on available historical data and trends. Such an approach has limitations when novel subsystem architectures such as More Electric or All Electric aircraft are considered, since historical data regarding such architectures is either limited or non-existent. In such cases, the incorporation of more thorough and explicit consideration of the aircraft subsystems into the conceptual design phase is warranted. The first objective of this dissertation is to integrate subsystem sizing and analysis methods that are suitable for the early design phases with the traditional aircraft sizing methodology. The goal is to facilitate the assessment subsystem architecture performance with respect to vehicle and mission level metrics. The second objective is to investigate how the performance of different subsystem architectures varies with aircraft size. The third and final objective is to assess the sensitivity of architecture performance to epistemic and technological uncertainty. These objectives are pursued through the development of an integrated sizing and analysis environment where the subsystems are sized in parallel with the aircraft itself using subsystem models that are computationally inexpensive and do not require detailed aircraft definition. The effects of subsystem mass, secondary power requirements, and drag increments are propagated to the mission performance analysis following which the vehicle and subsystems are re-sized. A number of experiments are performed to first test the capabilities of the developed environment and subsequently assess the performance of numerous subsystem architectures and the sensitivity of select architectures to epistemic and technological uncertainty.Ph.D