Dataset for assessing HVAC predictive maintenance system performance
Description
Heating, Ventilation and Air Conditioning (HVAC) are crucial installations in the hotel sector. The cost of these facilities often represents a significant percentage of the total building maintenance expenses. One way to reduce these costs is through predictive maintenance. Predictive maintenance aims to keep system components operating optimally and schedule inspections before failures occur. In this paper, we present an Artificial Intelligence-based flexible system for the predictive maintenance of HVAC facilities in hotels. To leverage the advantages of both data-driven models and rule-based models, we propose a model that combines Artificial Neural Networks (ANNs) with a Fuzzy Logic-based expert system. The Fuzzy Logic-based system estimates the probability of upcoming failures in the HVAC based on expert knowledge. The ANN-based system is trained using data generated by the Fuzzy Logic system and then learns adaptively according to the context. This is achieved using a variable number of non-invasive sensors within the HVAC system, providing the Fuzzy Logic (FL) system with the necessary flexibility for accurate operation. Simulations demonstrated strong performance, and the system was successfully tested in a five-star hotel in Seville, Spain. A total of 10000 samples with corresponding input and output values from the FL system were saved in a spreadsheet. These values were used to create and train the ANNs in MATLAB, establishing the dataset. 6010 samples were generated with standard values of all input variables, varying only one of them randomly. This method creates outputs both without faults and with single faults. An additional 3990 samples were generated using random inputs, allowing for the generation of different possible faults for various input sets. This dataset was divided into three parts: 70% of the samples were used as the training set, 15% as the validation set, and the remaining 15% (675 samples) as the test set This dataset also contains failure descriptions and the generated code in MATLAB used for simulations