Dear Colleagues,

Almost all infrastructure and systems today are repairable systems. Proper maintenance plays an important role in maintaining the system’s operational efficiency and achieving the required performance. However, maintenance costs usually do not represent a small fraction of the total lifecycle cost (LCC) of the systems. The operation and maintenance cost of wind turbines, for example, is 15~30% of the lifecycle cost. For some other systems, the maintenance costs could be up to 50% of the LCC. Therefore, controlling and reducing maintenance costs has increasingly been of interest to researchers, asset owners, and operators.

Over the last 10 years, with the advancement of new technologies in sensing networks, the cost of the implementation of condition-monitoring systems has been driven continuously down, and at the same time, huge amounts of data have been collected in day-to-day operation. The data bring new value to the asset owners and operators if properly utilized. Therefore, there is a tremendous opportunity for condition-based maintenance (or predictive maintenance) of systems. Various new techniques have been applied to system maintenance for multi-objective optimization. These include techniques using machine learning for fault diagnosis and prediction, health-condition prediction, degradation modelling, maintenance scheduling, optimization of maintenance and operation, etc. This results in a growing trend in using machine learning for system predictive maintenance, for which a number of scholars are making efforts to search for suitable solutions. To cope with the current research trend, this Special Issue is proposed to serve as a forum for researchers to circulate and discuss their research outcomes in system maintenance using advanced technologies including machine learning. This Special Issue proposes to cover, but is not limited to, topics in the following research areas:  

• Sensing network for data collection
• Data collection, storage, and management
• Data processing techniques
• Data quality assessment
• Technologies for imputation of missing data
• Condition monitoring techniques
• Big data analytics for system operation and maintenance
• Condition monitoring and condition prediction
• Degradation modelling
• Multi-objective optimization
• Multi-criteria decision-making
• Fault detection, diagnosis, and prediction
• Reliability-centered maintenance
• Determination of remaining useful lifetime
• System reliability and maintainability modelling
• Determination of lifecycle cost
• AI-based models and algorithms for maintenance
• Application of Internet of Things (IoT) technology for maintenance
• Decision-making models for maintenance and operation
• Simulation-based decision-making
• New models applied to failure and maintenance data
• Development of time series models
• Maintenance strategy development
• Digital twin development for condition monitoring and predictive maintenance
• Determination of system performance and reliability indices

We warmly welcome your contribution to this Special Issue. If you have any inquiries, please contact Dr Tieling Zhang by email at tieling@uow.edu.au.

Dr. Tieling Zhang
Guest Editor

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