A more predictive maintenance approach for railway point machines can lead to fewer delays, lower operating costs, and improved safety for the railway industry. However, achieving this improvement in reliability, availability, and maintainability, necessitates the development of a predictive monitoring system for the fleet of point machines. This monitoring system would include acquiring the appropriate signals, the use of advanced pattern recognition and statistical methods for finding the early symptoms of point machine degradation, and a user interface for displaying and reporting the health results. This research effort focuses on the development of the statistical and pattern recognition tools for point machine health monitoring. The health monitoring algorithm development was performed for an MET electromechanical point machine, in which a test-bed was constructed for the data collection and algorithm development and validation. The introduction of seeded faults and degraded conditions were used on the test-bed to determine the level of accuracy, false alarm rate, and sensitivity of the health-monitoring algorithm to various failure modes. A series of algorithm processing steps, which include segmenting the point machine movements, statistical feature extraction, and multivariate health assessment tools such as principal component analysis and self-organizing maps were used in this study. The results showed significant promise, and it appears that the health condition assessment is quite good for various levels of friction degradation, obstacle detection, and detecting the higher levels of misalignment. From a technical perspective, future work could include a more robust and general approach for segmentation, along with an improved method for threshold setting. From an implementation perspective, there is still additional work needed for selecting the appropriate hardware and software platform for deploying this health monitoring system for point machines.

Digitalisation within industries has many positive opportunities but poses many cybersecurity-related threats. Cybersecurity is a critical concern for many industries, such as railway, aviation, mining, construction, healthcare, and finance, where vital information and operational security are at risk of being compromised. Today, researchers are looking into various solutions to tackle cybersecurity risks while still retaining the desired functionality of the system. However, it is believed that these challenges can be approached by integrating game theory and the cyber kill chain (CKC), which describes the different stages of a cyberattack, to understand the complexities of cybersecurity. Thus, the primary objective of this paper is to demonstrate the application of a strategic game model within the context of cybersecurity, specifically using the cyber kill chain (CKC) model. The focus is on validating the proposed strategic game model through a case study. The case study involves a scenario where defenders select strategies such as “monitor system,” “detecting system,” and “respond to attack,” while attackers choose various attack strategies like “monitoring attack,” “scan the organization's website,” and “develop malicious payloads” within the stages of the CKC. This approach aims to enhance understanding of the complex challenges and facilitate the development of effective cybersecurity solutions. This approach will help in evaluating the effectiveness of different security strategies. The proposed strategic approach uses a non-cooperative game based on mixed strategies. The authors have defined a scenario for simultaneous-move games by estimating values for various elements of the game. By analysing the behaviour of both attacker and defender, the proposed game-based model can help industries develop more effective and efficient security strategies. Further, the proposed model will provide a better understanding of the complex challenges of cybersecurity in industrial contexts. It can also be used to develop appropriate strategies to mitigate cybersecurity risks.

Asset Management of a complex technical system-of-systems needs cross-organizational operation and maintenance, asset data management and context-aware analytics. Emerging technologies such as AI and digitalisation can facilitate the augmentation of asset management (AAM), by providing data-driven and model-driven approaches to analytics, i.e., now-casting and forecasting. However, implementing context-aware now-casting and forecasting analytics in an operational environment with varying contexts such as for fleets and distributed infrastructure is challenging. The number of algorithms in such an implementation can be vast due to the large number of assets and operational contexts for the fleet. To reduce the complexity of the analytics, it is required to optimize the number of algorithms. This can be done by optimizing the number of operational contexts through a generalization and specialization approach based on both fleet behaviour and individual behaviour for improved analytics. This paper proposes a framework for context-aware now-casting and forecasting analytics for AAM based on a top-down, i.e., Fleet2Individual and bottom-up, i.e., Individual2Fleet approach. The proposed framework has been described and verified by applying it to the context of railway rolling stock in Sweden. The benefits of the proposed framework is to provide industries with a tool that can be used to simplify the implementation of AI and digital technologies in now-casting and forecasting.

This book applies traditional reliability engineering methods to prognostics and health management (PHM), looking at remaining useful life (RUL) and its dynamics, to enable engineers to effectively and accurately predict machinery and systems useful lifespan. One of the key tools used in defining and implementing predictive maintenance policies is the RUL indicator. However, it is essential to account for the uncertainty inherent to the RUL, as otherwise predictive maintenance strategies can be incorrect. This can cause high costs or, alternatively, inappropriate decisions. Methods used to estimate RUL are numerous and diverse and, broadly speaking, fall into three categories: model-based, data-driven, or hybrid, which uses both. The author starts by building on established theory and looks at traditional reliability engineering methods through their relation to PHM requirements and presents the concept of RUL loss rate. Following on from this, the author presents an innovative general method for defining a nonlinear transformation enabling the mean residual life to become a linear function of time. He applies this method to frequently encountered time-to-failure distributions, such as Weibull and gamma, and degradation processes. Latest research results, including the author’s (some of which were previously unpublished), are drawn upon and combined with very classical work. Statistical estimation techniques are then presented to estimate RUL from field data, and risk-based methods for maintenance optimization are described, including the use of RUL dynamics for predictive maintenance. The book ends with suggestions for future research, including links with machine learning and deep learning. The theory is illustrated by industrial examples. Each chapter is followed by a series of exercises. FEATURES Provides both practical and theoretical background of RUL Describes how the uncertainty of RUL can be related to RUL loss rate Provides new insights into time-to-failure distributions Offers tools for predictive maintenance This book will be of interest to engineers, researchers and students in reliability engineering, prognostics and health management, and maintenance management.