Applied Artificial Intelligence for Data-Driven Predicative Maintenance of Equipment

Dear Colleagues,

In the modern era, sophisticated and complex systems are part of modern industry—for instance, heavy machines, engines, electronic circuits and other complex structures. The failure of industrial equipment leads to unwanted downtime, production loss, economic damage, and it also put the safety of the workers at risk. These issues can be mitigated if the maintenance of such assets is performed on a timely basis. This is only possible if well before the failure of a component, the issues related to it are identified. In general, the maintenance of an object can be defined as servicing, functional checks, and repairing or replacing required components, machinery, artificial infrastructures, and supporting utilities in industrial, residential, business installations. In other words, the maintenance process can be defined as maintaining the equipment by troubleshooting problems either manually or through computerized diagnostic tools. Nowadays, due to the availability of different types of sensors, data recording has become an easy task. So, huge amounts of data related to different types of mechanical, electronic, and other complex systems are already available in the form of public repositories. Moreover, if desired data are not publicly available, they can be collected through the deployment of an appropriate sensory system and following standard data collection techniques. Later on, these data can be analyzed using different artificial intelligence techniques to develop the intelligent data-driven predictive maintenance of equipment. The devised strategies may consist of simple statistical analysis of the data to perform a cost–benefit analysis and forecasting or address the technical issue, for instance, fault detection and diagnosis of equipment. 

This Special Issue intends to present original research papers with high quality and novelty as well as review papers on “Applied Artificial Intelligence for Data-Driven Predictive Maintenance of Equipment”.

Topics of interest include, but are not limited to:

• Artificial intelligence, machine learning and deep learning for predictive maintenance;
• Data analytics for system operation and control;
• Multimodal data analytics and fusion;
• Distributed data mining;
• Cloud computing for data analytics and predictive maintenance;
• Data analytics for a resource demand forecasting;
• Data collection, visualization, statistical analysis, storage, and information management in industrial systems;
• Fault detection and diagnosis for energy systems.

Dr. Sana Ullah Jan
Dr. Muhammad Sohaib
Prof. Dr. Naeem Ramzan
Guest Editors

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• machine diagnostics and prognostics (condition monitoring)
• applications of automation
• computer engineering
• mechanical systems, machines and related components