Smart Maintenance and Human Factor Modeling for Aircraft Safety

The global pandemic has significantly accelerated the need for remote monitoring and diagnostics of airline operations and assets. As passenger and cargo flights are impacted from all directions, maintenance can be the steady, reliable part of the puzzle that helps get things back on track. This chapter explores the aircraft safety challenges that can be addressed with better maintenance technology and human factor modeling. Aircraft safety relies heavily on maintenance. During the COVID-19 recovery phase, airline operators need to focus on the application of a robust management of change process to implement better maintenance technology, identify new aircraft safety risks, determine effective mitigation measures, and implement strategies for deploying changes accordingly. For years aircraft maintenance routines have been carried out in the same manner without change, now with international travel restrictions, social distancing, reduced staff, and limited maintenance funding, the need for smarter ways of doing maintenance is obvious. In this regard smart technology has an important role to play. For instance, IoT data generates the capacity for predictive aircraft maintenance, AI introduces the capacity for smart, deep-learning machines to make predictive maintenance more accurate, actionable, and automatic. AI-enabled predictive maintenance leverages IoT data to predict and prevent aircraft failures. While smart technology enhances aircraft safety through better maintenance performance on the one hand, there are technical and human factor problems induced by COVID-19 on the other. The Safe Aircraft System (SAS) model, based on the Dirty Dozen and SHELL human factor models, is an initiative proposed to minimize such COVID-19 problems. This work shows through a case illustration that SAS modeling is a useful tool in identifying potential hazards/consequences associated with any major or minor changes in flight operations. Hence the synergistic effect of smart maintenance and the SAS model in enhancing aircraft system safety are demonstrated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Systematic Literature Review

Predictive maintenance plays an important role in managing commercial buildings. This article provides a systematic review of the literature on predictive maintenance applications of chilled water systems that are in line with Industry 4.0/Quality 4.0. The review is based on answering two research questions about understanding the mechanism of identifying the system’s faults during its operation and exploring the methods that were used to predict these faults. The research gaps are explained in this article and are related to three parts, which are faults description and handling, data collection and frequency, and the coverage of the proposed maintenance programs. This article suggests performing a mixed method study to try to fill in the aforementioned gaps.

Data driven health monitoring of Peltier modules using machine-learning-methods.

Thermal cyclers are used to perform polymerase chain reaction runs (PCR runs) and Peltier modules are the key components in these instruments. The demand for thermal cyclers has strongly increased during the COVID-19 pandemic due to the fact that they are important tools used in the research, identification, and diagnosis of the virus. Even though Peltier modules are quite durable, their failure poses a serious threat to the integrity of the instrument, which can lead to plant shutdowns and sample loss. Therefore, it is highly desirable to be able to predict the state of health of Peltier modules and thus reduce downtime. In this paper methods from three sub-categories of supervised machine learning, namely classical methods, ensemble methods and convolutional neural networks, were compared with respect to their ability to detect the state of health of Peltier modules integrated in thermal cyclers. Device-specific data from on-deck thermal cyclers (ODTC®) supplied by INHECO Industrial Heating & Cooling GmbH (Fig 1), Martinsried, Germany were used as a database for training the models. The purpose of this study was to investigate methods for data-driven condition monitoring with the aim of integrating predictive analytics into future product platforms. The results show that information about the state of health can be extracted from operational data - most importantly current readings - and that convolutional neural networks were the best at producing a generalized model for fault classification.

Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments.

Pandemic events, particularly the current Covid-19 disease, compel organisations to re-formulate their day-to-day operations for achieving various business goals such as cost reduction. Unfortunately, small and medium enterprises (SMEs) making up more than 95% of all businesses is the hardest hit sector. This has urged SMEs to rethink their operations to survive through pandemic events. One key area is the use of new technologies pertaining to digital transformation for optimizing pandemic preparedness and minimizing business disruptions. This is especially true from the perspective of digitizing asset management methodologies in the era of Industry 4.0 under pandemic environments. Incidentally, human-centric approaches have become increasingly important in predictive maintenance through the exploitation of digital tools, especially when the workforce is increasingly interacting with new technologies such as Artificial Intelligence (AI) and Internet-of-Things devices for condition monitoring in equipment maintenance services. In this research, we propose an AI-based human-centric decision support framework for predictive maintenance in asset management, which can facilitate prompt and informed decision-making under pandemic environments. For predictive maintenance of complex systems, an enhanced trust-based ensemble model is introduced to undertake imbalanced data issues. A human-in-the-loop mechanism is incorporated to exploit the tacit knowledge elucidated from subject matter experts for providing decision support. Evaluations with both benchmark and real-world databases demonstrate the effectiveness of the proposed framework for addressing imbalanced data issues in predictive maintenance tasks. In the real-world case study, an accuracy rate of 82% is achieved, which indicates the potential of the proposed framework in assisting business sustainability pertaining to asset predictive maintenance under pandemic environments.

Applications of Digital Twin across Industries: A Review

One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries.

Watchful Eye

"Adapting strategies for the future of work, supply chain resilience and digital maturity can help manufacturers keep pace and drive performance amid strong economic demand," said authors of one U.S. manufacturing industry research report developed by Deloitte. Predictive maintenance Automating the process of predicting problems during the manufacturing process is certain to reduce downtime and increase efficiencies on the shop floor. Sustainability Economically and environmentally sustainable business practices have become a business imperative as energy and materials costs rise, regulations tighten, and consumers and investors are keyed in on sustainable brands and business practices.

Educational experiences on virtual teaching of electric motors condition monitoring courses

Electric motors condition monitoring courses, due to their eminently practical nature, have been seriously affected by the irruption of the COVID-19 pandemic. Classical laboratory sessions, face-to-face demonstrations and seminars and student visits to real industrial facilities have had to be replaced by other learning mechanisms adapted to the new context of virtual teaching. Instructors of these courses have been forced to devise, often in a very short time, suitable strategies to replace the aforementioned presential mechanisms, by substituting them by solutions adapted to the virtual learning that, on the one hand, guarantee the acquisition of the necessary competencies and skills, and, on the other hand, maintain the student interest and motivation. This paper explains the strategies adopted in the context of two courses related to electric motors condition monitoring that are taught at the authors' University. The described strategies have not only shown satisfactory results to transmit the regulated contents but also open new perspectives to enhance the future teaching in those courses. The ideas presented here may be useful for other instructors teaching similar courses or other subjects related to the considered area.