Dataset Reduction Framework For Intelligent Fault Detection In IoT-based Cyber-Physical Systems Using Machine Learning Techniques

Intelligent Fault Detection (IFD), the use of machine learning-based methods and algorithms for the fault detection in modern systems becomes nowadays important due to the large number of data being generated by devices embedded in such systems. A typical example of such systems is Internet of Things (IoT)-based Cyber-Physical Systems (CPS) where IoT devices are used for better monitoring and control of such systems but at the same time due to their nature are susceptible to component faults. IFD depends on the number of data generated in such systems and their representation using system characteristics (features). Instance-based dataset reduction schemes used in Machine Learning (ML) aim to reduce the volume of data required during training while maintaining or preserving testing accuracy. Such reductions lead to less storage and processing time required for the trained models, which enables the use of lightweight IFD approaches in embedded devices found in the core of IoT-based CPS systems. In this work, we propose a machine learning-based framework for instance-based dataset reduction applied for IFD models. Our proposed framework is experimentally evaluated over two datasets. Results show that reduction is possible for up to 15.51% with an average accuracy improvement of 17% on the set of evaluated classification algorithms.