Hydropower generation is world best source of renewable energy and is continuously playing a tremendous job in energy requirement. To cope up with day by day increasing demand in electric energy, it is very essential to keep the hydropower plants in sustainable operational condition. To achieve this, the maintenance strategies based on Reliability, Availability and Maintainability (RAM) analysis play a pivotal role in sustainability of the hydropower plants. In our case study various critical components of hydropower plant were identified such as turbine, governor and generator. The causes and effects of various failures in the critical components were identified using Failure Mode Effect Analysis (FMEA) and Fault tree Analysis (FTA). The main role in the study is presenting a RAM model which helps in evaluating different levels of performance in Hydropower plant. Finally, the RAM parameters were evaluated and their outcomes were suggested to find the convenient maintenance intervals. The results of the proposed model revealed that the critical flaws of the hydrogenation process such as turbines, governors and generator should to be taken on priority basis to achieve the high level of reliability and safety. The results of present framework is to achieve the high level of reliability, hence, the main flaws of the hydrogenation process such as turbines, governors and generator should to be taken on priority basis. The proposed study could be valuable for improving the availability and safety of the critical equipment in hydropower plant. As a consequence, the results of present study could be beneficial for other hydro generating facilities to achieve the sustainable maintenance and operation programs in electricity generation systems. 

The energy power supply infrastructure of a hospital, to function correctly, needs to be well maintained to ensure its reliability and, by consequence, the maximum integrated availability. In this paper, the authors propose the use of Petri Nets to help the improvement of the electric power system reliability, having as a case study a big European Hospital. The purpose of the research is to identify and analyse the potential failures of the system and to suggest solutions to improve the operations and maintenance to maximise the availability and reliability of those assets through possible and objective answers. It was necessary to develop a diagnosis and planning methodology to assess the reliability of several components of the energy power supply system. It is dynamic modelling based on a block diagram of the system and transposed to representation by Petri Nets. The analysis and the simulation of the discrete events of the system, as well as the visualisation of the process functioning and the communications inside, was made. Additionally, they were referred to other approaches, like the Fuzzy Petri Nets and Stochastic Petri Nets, as well as a future balance about its application in a situation like the analysed in this paper.

Purpose - The paper aims to convert into useable guidelines, the knowledge related to human factors and tasks' organisation, which are embedded in one of the most exciting maintenance actions that are carried out, the pitstop in Formula 1 races.

Design/methodology/approach - The paper opted for a fault tree analysis (FTA) to de-construct all the sub-tasks and their possible deviations from desirable situations and to evaluate the most relevant information needed for carrying out the pitstop operation. Besides, the SHELL model was applied in a second stage to evaluate the interaction between human being and human interfaces with other components of the system. Once this set of information was crystallised, the research translated it into useable guidelines for organising industrial maintenance actions using the same approach and possible reaching the same results.

Findings - The results of this study is a structured set of guidelines that encompasses the most paramount aspects that should be considered for setting correct maintenance actions. They represent a “guide” for including the different angles that are included during these operations.

Research limitations/implications - The guidelines are potentially applicable to every maintenance operation. The guidelines should be tested on different working domains to check their applicability besides the racing world.

Practical implications - This study is a reverse engineering work for creating a scheme to include into maintenance operations aspects such as crew athlete-like fitness, training, technology, organisational issues, safety, ergonomics and psychology.

Originality/value - The value of the paper is deconstructing the results of one of the most successful and prepared maintenance action. The paper takes a different approach in proposing how to structure and create maintenance solutions. The difference in approaches between the maintenance during the pitstop of Formula 1 car and industrial applications enhances the gap that needs still to be filled for further improving maintenance actions out of the racing world.

The big hospitals’ electricity supply system’s reliability is discussed in this article through Petri nets and Fuzzy Inference System (FIS). To simulate and analyse an electric power system, the FIS Mamdani in MATLAB is implemented. The advantage of FIS is that it uses human experience to provide a faster solution than conventional techniques. The elements involved are the Main Electrical Power, the Generator sets, the Automatic Transfer Switches (ATS), and the Uninterrupted Power Supply (UPS), which are analysed to characterize the system behaviour. To evaluate the system and identified the lower reliability modules being proposed, a new reliable design model through the Petri Nets and Fuzzy Inference System approach. The resulting approach contributes to increasing the reliability of complex electrical systems, aiming to reduce their faults and increase their availability.

The traditional manufacturing system is going through a rapid transformation and has brought a revolution in the industries. Industry 4.0 is considered to be a new era of the industrial revolution in which all the processes are integrated with a product to achieve higher efficiency. Digitization and automation have changed the nature of work resulting in an intelligent manufacturing system. The benefits of Industry 4.0 include higher productivity and increased flexibility. However, the implementation of the new processes and methods comes along with a lot of challenges. Industry 4.0. requires more skilled workers to handle the operations of the digitalized manufacturing system. The fourth industrial revolution or Industry 4.0 has become the absolute reality and will undoubtedly have an impact on safety and maintenance. Hence, to tackle the issues arising due to digitization is an area of concern and has to be dealt with using the innovative technologies in the manufacturing industries.

Aiming to remain competitive, companies from diverse industries are paying more attention to Industry 4.0 concept and its benefits. Maintenance is seen as a specific area of action to successfully sustain a competitive leverage, and its fusion with Industry 4.0 is perceived to revolutionize the whole maintenance concept. Maintenance 4.0 emerges as a subset of Industry 4.0 in the form of self-learning and smart system that predicts failures, makes diagnoses, and establishes maintenance actions. This chapter presents a systematic literature review (SLR) on Maintenance 4.0, with the aim of outlining the current achievements as well as limitations of maintenance meeting Industry 4.0 demands. The analysis included 90 papers selected as being the most suitable to reach the proposed goal. A state of the art on Maintenance 4.0 is performed, followed by an analysis ambitioning the delineation of what future holds on this topic.

Operator 4.0 is a smart and skilled operator who augments the symbiosis between intelligent machines and operators. Better integration of Operator 4.0 in Industry 4.0 can bring emphasis on the human-centric approach, allowing for a paradigm shift towards human-automation cooperation for inspiring the compulsion of human-in-the-loop. This further enhances the domain knowledge for the improvement of human cyber-physical systems for new generation automated systems. This cooperation of humans and automation makes stability in socio-technical systems with smart automation and human-machine interfacing technologies. This chapter discusses the design principles of Industry 4.0 and Operator 4.0 human-cyber physical systems.

Big Data Analytics (BDA) is becoming a research focus in transportation systems, which can be seen from many projects within the world. By using sensor and Internet of Things (IoT) technology in transportation system, huge amount of data is been generated from different sources. This data can be integrated, analyzed and visualized for efficient and effective decision-making for maintaining transportation systems. The key challenges that exist in managing Big Data are the designing of the systems, which would be able to handle huge amount of data efficiently and effectively and to filter the most significant information from all the collected data. This chapter will draw attention towards the present scenario and future projections of big data in transportation systems. It also presents big data tools and techniques and then presents one brief case study of BDA in each type of transportation system. In this chapter, a broad overview of Big Data definitions, its history, present, and future prospects are briefed. Several tools and technologies especially for transportation are pointed out for maintaining transportation systems. At the end of the chapter, a definitive case studies on each transportation area is demonstrated.

Railway will continue to adapt new digital solutions which are necessary and vulnerable to cyber threats. The history of cyber-attacks on critical infrastructures including railway suggests that there is a need for cybersecurity awareness. Both for employees and the general public. The very first step in cyber hygiene is cybersecurity training and awareness for the workforce. A well-educated workforce plays a vital role in building more cyber resiliency across the organization's operation and maintenance. The objective of this research is to evaluate the cybersecurity maturity level for workforce management in three railway organizations. The results show that there is a cybersecurity workforce gap and there is a need to eliminate this gap by enhancing cybersecurity workforce culture. Henceforth, this gap can be improved by developing cybersecurity culture, including cybersecurity training and awareness and by following recommendations provided in this paper.