Predictive maintenance of electrical grid assets: internship at EDP Distribuição - Energia S.A

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis report will describe the activities developed during an internship at EDP Distribuição, focusing on a Predictive Maintenance analytics project directed at high voltage electrical grid assets including Overhead Lines, Power Transformers and Circuit Breakers. The project’s main goal is to support EDP’s asset management processes by improving maintenance and investing planning. The project’s main deliverables are the Probability of Failure metric that forecast asset failures 15 days ahead of time, estimated through supervised machine learning models; the Health Index metric that indicates asset’s current state and condition, implemented though the Ofgem methodology; and two asset management dashboards. The project was implemented by an external service provider, a consultant company, and during the internship it was possible to integrate the team, and participate in the development activities

Dmarc: A Framework For The Integration Of Dmaic And Dmadv

The Lean Six Sigma methodology is being applied extensively to tackle many quality related issues in many processes of today s industries. Various companies have benefited greatly from the adoption of Six Sigma and Lean engineering concepts since their introduction, and continue to do so. The DMAIC method that is traditionally adopted in the implementation of the Lean Six Sigma methodology has proven to yield cost saving results in most cases. Yet, industries have found that just improvement of existent process and products to reduce defects, does not quench the customer s growing thirst for greater quality. In order to tackle variation and defects pro actively, the initiative to achieve Six Sigma level of quality (3.4 DPMO) or greater is being infused into the design of new products using the Design for Six Sigma (DFSS) methodology, through systematic approaches such as DMADV. This research integrates the DMADV approach into the classic DMAIC methodology through a framework, DMARC, which details the improvement an existing process through re design. It provides a systematic approach to avoid the mis direction of projects into following the path of continued improvement of existing processes that are deemed to be beyond such efforts. A real life industrial case: a successfully completed Lean Six Sigma project, tackling the downtime of the Launch Pad Meteorological System at Launch Pads 39A and B at the Kennedy Space Center, was studied to exemplify the possibility of the achievement of greater results from the implementation of the DMARC framework

A Feasibility Study of Azure Machine Learning for Sheet Metal Fabrication

The research demonstrated that sheet metal fabrication machines can utilize machine learning to gain competitive advantage. With various possible applications of machine learning, it was decided to focus on the topic of predictive maintenance. Implementation of the predictive service is accomplished with Microsoft Azure Machine Learning. The aim was to demonstrate to the stakeholders at the case company potential laying in machine learning. It was found that besides machine learning technologies being founded on sophisticated algorithms and mathematics it can still be utilized and bring benefits with moderate effort required. Significance of this study is in it demonstrating potentials of the machine learning to be used in improving operations management and especially for sheet metal fabrication machines.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Total Quality Management and Six Sigma

In order to survive in a modern and competitive environment, organizations need to carefully organize their activities regarding quality management. TQM and six sigma are the approaches that have been successful in solving intricate quality problems in products and services. This volume can help those who are interested in the quality management field to understand core ideas along with contemporary efforts done in the field and authored as case studies in this volume. This volume may be useful to students, academics and practitioners across diversified disciplines

Methodology for Evaluating Reliability Growth Programs of Discrete Systems

The term Reliability Growth (RG) refers to the elimination of design weaknesses inherent to intermediate prototypes of complex systems via failure mode discovery, analysis, and effective correction. A wealth of models have been developed over the years to plan, track, and project reliability improvements of developmental items whose test durations are continuous, as well as discrete. This research reveals capability gaps, and contributes new methods to the area of discrete RG projection. The purpose of this area of research is to quantify the reliability that could be achieved if failure modes observed during testing are corrected via a specified level of fix effectiveness. Fix effectiveness factors reduce initial probabilities (or rates) of occurrence of individual failure modes by a fractional amount, thereby increasing system reliability. The contributions of this research are as follows. New RG management metrics are prescribed for one-shot systems under two corrective action strategies. The first is when corrective actions are delayed until the end of the current test phase. The second is when they are applied to prototypes after associated failure modes are first discovered. These management metrics estimate: initial system reliability, projected reliability (i.e., reliability after failure mode mitigation), RG potential, the expected number of failure modes observed during test, the probability of discovering new failure modes, and the portion of system unreliability associated with repeat failure modes. These management metrics give practitioners the means to address model goodness-of-fit concerns, quantify programmatic risk, assess reliability maturity, and estimate the initial, projected, and upper achievable reliability of discrete systems throughout their development programs. Statistical procedures (i.e., classical and Bayesian) for point-estimation, confidence interval construction, and model goodness-of-fit testing are also developed. In particular, a new likelihood function and maximum likelihood procedure are derived to estimate model parameters. Limiting approximations of these parameters, as well as the management metrics, are also derived. The features of these new methods are illustrated by simple numerical example. Monte Carlo simulation is utilized to characterize model accuracy. This research is useful to program managers and practitioners working to assess the RG program and development effort of discrete systems

Similarity-based methods for machine diagnosis

This work presents a data-driven condition-based maintenance system based on similarity-based modeling (SBM) for automatic machinery fault diagnosis. The proposed system provides information about the equipment current state (degree of anomaly), and returns a set of exemplars that can be employed to describe the current state in a sparse fashion, which can be examined by the operator to assess a decision to be made. The system is modular and data-agnostic, enabling its use in different equipment and data sources with small modifications. The main contributions of this work are: the extensive study of the proposition and use of multiclass SBM on different databases, either as a stand-alone classification method or in combination with an off-the-shelf classifier; novel methods for selecting prototypes for the SBM models; the use of new similarity functions; and a new production-ready fault detection service. These contributions achieved the goal of increasing the SBM models performance in a fault classification scenario while reducing its computational complexity. The proposed system was evaluated in three different databases, achieving higher or similar performance when compared with previous works on the same database. Comparisons with other methods are shown for the recently developed Machinery Fault Database (MaFaulDa) and for the Case Western Reserve University (CWRU) bearing database. The proposed techniques increase the generalization power of the similarity model and of the associated classifier, having accuracies of 98.5% on MaFaulDa and 98.9% on CWRU database. These results indicate that the proposed approach based on SBM is worth further investigation.Este trabalho apresenta um sistema de manutenção preditiva para diagnóstico automático de falhas em máquinas. O sistema proposto, baseado em uma técnica denominada similarity-based modeling (SBM), provê informações sobre o estado atual do equipamento (grau de anomalia), e retorna um conjunto de amostras representativas que pode ser utilizado para descrever o estado atual de forma esparsa, permitindo a um operador avaliar a melhor decisão a ser tomada. O sistema é modular e agnóstico aos dados, permitindo que seja utilizado em variados equipamentos e dados com pequenas modificações. As principais contribuições deste trabalho são: o estudo abrangente da proposta do classificador SBM multi-classe e o seu uso em diferentes bases de dados, seja como um classificador ou auxiliando outros classificadores comumente usados; novos métodos para a seleção de amostras representativas para os modelos SBM; o uso de novas funções de similaridade; e um serviço de detecção de falhas pronto para ser utilizado em produção. Essas contribuições atingiram o objetivo de melhorar o desempenho dos modelos SBM em cenários de classificação de falhas e reduziram sua complexidade computacional. O sistema proposto foi avaliado em três bases de dados, atingindo desempenho igual ou superior ao desempenho de trabalhos anteriores nas mesmas bases. Comparações com outros métodos são apresentadas para a recém-desenvolvida Machinery Fault Database (MaFaulDa) e para a base de dados da Case Western Reserve University (CWRU). As técnicas propostas melhoraram a capacidade de generalização dos modelos de similaridade e do classificador final, atingindo acurácias de 98.5% na MaFaulDa e 98.9% na base de dados CWRU. Esses resultados apontam que a abordagem proposta baseada na técnica SBM tem potencial para ser investigada em mais profundidade

Fault detection of a wind turbine generator bearing using interpretable machine learning

A wind turbine is subjected to a number of degradation mechanisms during its operational lifetime. If left unattended, the degradation of components will result in poor performance and potential failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbines are regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally six-month intervals) and the maintenance program (list of tasks) is prepared using experience or risk-based reliability analysis, like risk-based inspection (RBI) and reliability-centered maintenance (RCM). This time-based maintenance program can be improved by incorporating results from condition monitoring (CM) involving data acquisition using sensors and fault detection using data analytics. It is important to ensure quality and quantity of data and to use correct procedures for data interpretation for fault detection to properly carry out condition assessment. This thesis contains the work carried out to develop a machine learning (ML) based methodology for detecting faults in a wind turbine generator bearing. The methodology includes application of ML using supervisory control and data acquisition (SCADA) data for predicting the operating temperature of a healthy bearing, and then comparing the predicted bearing temperature with the actual bearing temperature. Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing. This fault detection can then be used for rescheduling the maintenance tasks. The methodology is discussed in detail using a case study. In this thesis, interpretable ML tools are used to identify faults in a wind turbine generator bearing. Furthermore, variables affecting the generator bearing temperature are investigated. The analysis used two years of operational data from a 2 MW offshore wind turbine located in the Gulf of Guinea off the west coast of Africa. Out of the four ML models that were evaluated, the XGBoost model was determined to be the most effective performer. After utilizing the Shapley additive explanations (SHAP) to analyze the XGBoost model, it was determined that the temperature in the generator phase windings had the most significant effect on the model's predictions. Finally, based upon the deviation between the actual and the predicted temperatures, an anomaly in the generator bearing was successfully identified two months prior to a generator failure occurring.Masteroppgave i havteknologiHTEK3995MAMN-HTEKMAMN-HTE

Analytics and Intelligence for Smart Manufacturing

Digital transformation is one of the main aspects emerged by the current 4.0 revolution. It embraces the integration between the digital and physical environment,including the application of modelling and simulation techniques, visualization, and data analytics in order to manage the overall product life cycle

Mitigating Emergent Safety and Security Incidents of CPS by a Protective Shell

In today's modern world, Cyber-Physical Systems (CPS) have gained widespread prevalence, offering tremendous benefits while also increasing society's dependence on them. Given the direct interaction of CPS with the physical environment, their malfunction or compromise can pose significant risks to human life, property, and the environment. However, as the complexity of CPS rises due to heightened expectations and expanded functional requirements, ensuring their trustworthy operation solely during the development process becomes increasingly challenging. This thesis introduces and delves into the novel concept of the 'Protective Shell' – a real-time safeguard actively monitoring CPS during their operational phases. The protective shell serves as a last line of defence, designed to detect abnormal behaviour, conduct thorough analyses, and initiate countermeasures promptly, thereby mitigating unforeseen risks in real-time. The primary objective of this research is to enhance the overall safety and security of CPS by refining, partly implementing, and evaluating the innovative protective shell concept. To provide context for collaborative systems working towards higher objectives — common within CPS as system-of-systems (SoS) — the thesis introduces the 'Emergence Matrix'. This matrix categorises outcomes of such collaboration into four quadrants based on their anticipated nature and desirability. Particularly concerning are outcomes that are both unexpected and undesirable, which frequently serve as the root cause of safety accidents and security incidents in CPS scenarios. The protective shell plays a critical role in mitigating these unfavourable outcomes, as conventional vulnerability elimination procedures during the CPS design phase prove insufficient due to their inability to proactively anticipate and address these unforeseen situations. Employing the design science research methodology, the thesis is structured around its iterative cycles and the research questions imposed, offering a systematic exploration of the topic. A detailed analysis of various safety accidents and security incidents involving CPS was conducted to retrieve vulnerabilities that led to dangerous outcomes. By developing specific protective shells for each affected CPS and assessing their effectiveness during these hazardous scenarios, a generic core for the protective shell concept could be retrieved, indicating general characteristics and its overall applicability. Furthermore, the research presents a generic protective shell architecture, integrating advanced anomaly detection techniques rooted in explainable artificial intelligence (XAI) and human machine teaming. While the implementation of protective shells demonstrate substantial positive impacts in ensuring CPS safety and security, the thesis also articulates potential risks associated with their deployment that require careful consideration. In conclusion, this thesis makes a significant contribution towards the safer and more secure integration of complex CPS into daily routines, critical infrastructures and other sectors by leveraging the capabilities of the generic protective shell framework.:1 Introduction 1.1 Background and Context 1.2 Research Problem 1.3 Purpose and Objectives 1.3.1 Thesis Vision 1.3.2 Thesis Mission 1.4 Thesis Outline and Structure 2 Design Science Research Methodology 2.1 Relevance-, Rigor- and Design Cycle 2.2 Research Questions 3 Cyber-Physical Systems 3.1 Explanation 3.2 Safety- and Security-Critical Aspects 3.3 Risk 3.3.1 Quantitative Risk Assessment 3.3.2 Qualitative Risk Assessment 3.3.3 Risk Reduction Mechanisms 3.3.4 Acceptable Residual Risk 3.4 Engineering Principles 3.4.1 Safety Principles 3.4.2 Security Principles 3.5 Cyber-Physical System of Systems (CPSoS) 3.5.1 Emergence 4 Protective Shell 4.1 Explanation 4.2 System Architecture 4.3 Run-Time Monitoring 4.4 Definition 4.5 Expectations / Goals 5 Specific Protective Shells 5.1 Boeing 737 Max MCAS 5.1.1 Introduction 5.1.2 Vulnerabilities within CPS 5.1.3 Specific Protective Shell Mitigation Mechanisms 5.1.4 Protective Shell Evaluation 5.2 Therac-25 5.2.1 Introduction 5.2.2 Vulnerabilities within CPS 5.2.3 Specific Protective Shell Mitigation Mechanisms 5.2.4 Protective Shell Evaluation 5.3 Stuxnet 5.3.1 Introduction 5.3.2 Exploited Vulnerabilities 5.3.3 Specific Protective Shell Mitigation Mechanisms 5.3.4 Protective Shell Evaluation 5.4 Toyota 'Unintended Acceleration' ETCS 5.4.1 Introduction 5.4.2 Vulnerabilities within CPS 5.4.3 Specific Protective Shell Mitigation Mechanisms 5.4.4 Protective Shell Evaluation 5.5 Jeep Cherokee Hack 5.5.1 Introduction 5.5.2 Vulnerabilities within CPS 5.5.3 Specific Protective Shell Mitigation Mechanisms 5.5.4 Protective Shell Evaluation 5.6 Ukrainian Power Grid Cyber-Attack 5.6.1 Introduction 5.6.2 Vulnerabilities in the critical Infrastructure 5.6.3 Specific Protective Shell Mitigation Mechanisms 5.6.4 Protective Shell Evaluation 5.7 Airbus A400M FADEC 5.7.1 Introduction 5.7.2 Vulnerabilities within CPS 5.7.3 Specific Protective Shell Mitigation Mechanisms 5.7.4 Protective Shell Evaluation 5.8 Similarities between Specific Protective Shells 5.8.1 Mitigation Mechanisms Categories 5.8.2 Explanation 5.8.3 Conclusion 6 AI 6.1 Explainable AI (XAI) for Anomaly Detection 6.1.1 Anomaly Detection 6.1.2 Explainable Artificial Intelligence 6.2 Intrinsic Explainable ML Models 6.2.1 Linear Regression 6.2.2 Decision Trees 6.2.3 K-Nearest Neighbours 6.3 Example Use Case - Predictive Maintenance 7 Generic Protective Shell 7.1 Architecture 7.1.1 MAPE-K 7.1.2 Human Machine Teaming 7.1.3 Protective Shell Plugin Catalogue 7.1.4 Architecture and Design Principles 7.1.5 Conclusion Architecture 7.2 Implementation Details 7.3 Evaluation 7.3.1 Additional Vulnerabilities introduced by the Protective Shell 7.3.2 Summary 8 Conclusion 8.1 Summary 8.2 Research Questions Evaluation 8.3 Contribution 8.4 Future Work 8.5 Recommendatio