Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

From history to the present day, especially in the field of technology is provided a wide range of developments. The most important of these developments has achieved the realization of the industrial revolution, has led to play a leading role in the production is done by human beings. The effects on production of industry 4.0 emerging with this mind is very important. The qualified employees should be trained to make the necessary preparations for the company in this period, it is experiencing the fourth industrial revolution. Therefore, the impacts on higher education of industry 4.0 are examined in this paper. The importance in education of industry 4.0 has revealed with statistical data presented in this study.

The periodical maintenance of railway systems is very important in terms of maintaining safe and comfortable transportation. In particular, the monitoring and diagnosis of faults in the pantograph catenary system are required to provide a transmission from the catenary line to the electric energy locomotive. Surface wear that is caused by the interaction between the pantograph and catenary and nonuniform distribution on the surface of a pantograph of the contact points can cause serious accidents. In this paper, a novel approach is proposed for image processing-based monitoring and fault diagnosis in terms of the interaction and contact points between the pantograph and catenary in a moving train. For this purpose, the proposed method consists of two stages. In the first stage, the pantograph catenary interaction has been modeled; the simulation results were given a failure analysis with a variety of scenarios. In the second stage, the contact points between the pantograph and catenary were detected and implemented in real time with image processing algorithms using actual video images. The pantograph surface for a fault analysis was divided into three regions: safe, dangerous, and fault. The fault analysis of the system was presented using the number of contact points in each region. The experimental results demonstrate the effectiveness, applicability, and performance of the proposed approach.

With the development of technology, smart devices are becoming more common in everyday life. The development of devices that can connect to the Internet and transmit data has been a source of inspiration for smart city designs. The common problem in our cities is the difficulty of finding free parking slots. The parking problem causes traffic to congest and people who go to work are looking for a place. In this study, a navigation and reservation based parking proposal system was developed for smart cities. The proposed method involves the development of small devices that send data to the internet using the internet of things (IoT) technology. The free parking space closest to the current location is found by genetic algorithm. The proposed method is tested for different scenarios and accurate results are obtained.