Hydrogen storage and delivery: Review of the state of the art technologies and risk and reliability analysis

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

A review on prognostic techniques for non-stationary and non-linear rotating systems

The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.

Manufacturing companies are continuously facing the challenge of operating their manufacturing processes and systems in order to deliver the required production rates of high quality products, while minimizing the use of resources. Production quality is proposed in this paper as a new paradigm aiming at going beyond traditional six-sigma approaches. This new paradigm is extremely relevant in technology intensive and emerging strategic manufacturing sectors, such as aeronautics, automotive, energy, medical technology, micro-manufacturing, electronics and mechatronics. Traditional six-sigma techniques show strong limitations in highly changeable production contexts, characterized by small batch productions, customized, or even one-of-a-kind products, and in-line product inspections. Innovative and integrated quality, production logistics and maintenance design, management and control methods as well as advanced technological enablers have a key role to achieve the overall production quality goal. This paper revises problems, methods and tools to support this paradigm and highlights the main challenges and opportunities for manufacturing industries in this context.

Design and management of manufacturing systems for production quality

The aeronautics industry is attempting to implement important changes to its maintenance strategy. The article presents a new framework for making final decision on aeroplane maintenance actions. It emphasizes on the use of prognostics within this global framework to replace corrective and Preventive Maintenance practise for a predictive maintenance to minimize the cost of the maintenance support and to increase aircraft/fleet operability. The main objective of the article is to show the Bayesian network model as a useful technique for prognosis. The specific use case for predicting brake wear on the plane is developed based on this technique. The network allows estimate brake wear from the aircraft operational plan. This model, together with other models to make predictions for various components of the aeroplane (that should be monitored) offers a forward-looking approach of the status of the plane, allowing later the evaluation of different operational plans based on operational risk assessment and economic cost of each one of them depending on the scheduled checks.

Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept

As worldwide awareness about global climate change spreads, green electronics are becoming increasingly popular as an alternative to diminish pollution. Thus, nowadays energy efficiency is a paramount characteristic in electronics systems to obtain such a goal. Harvesting wasted energy from human activities and world physical phenomena is an alternative to deal with the aforementioned problem. Energy harvesters constitute a feasible solution to harvesting part of the energy being spared. The present research work provides the tools for characterizing, designing and implementing such devices in electronic systems through their equivalent structural models.

https://www.mdpi.com/2079-9292/8/5/486/pdf

Energy Harvesting Technologies and Equivalent Electronic Structural Models—Review

Data mining for quality control: Burr detection in the drilling process

In manufacturing, users increasingly demand comprehensive maintenance service in their production equipment in order to ensure high availability and to prevent downtimes in critical phases of the production processes, affecting customer delivery times. From the manufacturer’s point of view, it is vital to optimize and to improve the service provided to the final users, allowing appropriate maintenance planning and responding to the demand. Contrary to the classic preventive maintenance programs in use today, predictive maintenance improves the performance of the equipment, strengthening the business model of companies. Thanks to the inclusion of a set of sensing, condition monitoring, predictive analytics and distribute systems technologies, it is possible to perform and provide a remote technical assistance based on continuous monitoring and maintenance support from a distance. This paper shows the benefits and advantages to be achieved by the development of a comprehensive predictive maintenance, through the concept of Industry 4.0, and focuses on remote monitoring and self-diagnosis function of health condition for the equipment. However, the main emphasis of the work presents the data acquisition and analysis processes to develop predictive algorithms for machines in production.

/pdf/industry-4-0-predictive-intelligent-maintenance-for-qr3fy6ie43.pdf

INDUSTRY 4.0: Predictive Intelligent Maintenance for Production Equipment

One of the greatest challenges of optimising the correct operation of wind turbines is detecting the health status of their core components, such as gearboxes in particular. Gearbox monitoring is a widely studied topic in the literature, nevertheless, studies showing data of in-service wind turbines are less frequent and tend to present difficulties that are otherwise overlooked in test rig based works. This work presents the data of three wind turbines that have gearboxes in different damage stages. Besides including the data of the SCADA (Supervisory Control And Signal Acquisition) system, additional measurements of online optical oil debris sensors are also included. In addition to an analysis of the behaviour of particle generation in the turbines, a methodology to identify regimes of operation with lower variation is presented. These regimes are later utilised to develop a health index that considers operation states and provides valuable information regarding the state of the gearboxes. The proposed health index allows distinguishing damage severity between wind turbines as well as tracking the evolution of the damage over time.

https://www.mdpi.com/1996-1073/12/17/3373/pdf

Susana Ferreiro

Papers

Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept

Energy Harvesting Technologies and Equivalent Electronic Structural Models—Review

Data mining for quality control: Burr detection in the drilling process

INDUSTRY 4.0: Predictive Intelligent Maintenance for Production Equipment

A Context-Aware Oil Debris-Based Health Indicator for Wind Turbine Gearbox Condition Monitoring