Intelligent Predictive Maintenance for Smart Building Systems

An innovative method for streamlining building maintenance and operations in smart buildings is intelligent predictive maintenance, or IPM. This thorough analysis looks at the main advantages, difficulties, and cutting-edge methods for integrating IPM in smart building settings. Early failure identification, decreased downtime, longer equipment lifespans, and optimised maintenance plans are among the main advantages of IPM. These benefits have the potential to significantly reduce costs while also enhancing building performance and occupant comfort. IPM must also overcome a number of significant obstacles, including issues with data quality, security and privacy, complexity of the model, and interpretability. The paper examines the most recent cuttingedge methods and algorithms being created and used for IPM in smart buildings in order to address these issues. These encompass machine learning, deep learning, statistical techniques, and hybrid methodologies. The assessment, for example, emphasises the potential use of federated learning-a machine learning technique that protects privacy-for anomaly detection in smart buildings. The review also explores the particular difficulties associated with maintaining smart buildings, such as system complexity, occupant behaviour, and maintenance strategy and costs. To overcome these obstacles, IPM implementation must take a deliberate and comprehensive approach. Through the development and implementation of efficient IPM solutions to optimise building operations and maintenance, practitioners and researchers can benefit from the deep understanding gained from this thorough assessment, which will ultimately progress sustainability and smart building technologies.