Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

Abstract

This work delves into the pressing need for more efficient Predictive Maintenance solutions in the context of IoT-enabled Smart Cities. Existing methodologies often fall short, lacking the precision and accuracy required to keep these rapidly evolving urban environments running smoothly for different use cases. The limitations of current approaches become apparent when considering their inability to cope with the intricacies of IoT data. They struggle to harness the wealth of information generated by countless interconnected devices and systems, resulting in suboptimal performance. This issue is further compounded by their relatively sluggish response times, hindering the timely detection of critical maintenance needs. In response to these challenges, this paper presents an innovative approach that leverages the fusion of Bidirectional Long Short-Term Memory (BiLSTM) with Autoencoders in conjunction with Recurrent Neural Networks (RNNs). This combination of advanced techniques brings forth a powerful predictive maintenance model process. The utilization of BiLSTM and Autoencoders adds a layer of sophistication, allowing for a deeper understanding of the underlying data patterns. BiLSTM's ability to capture contextual information from both past and future data points enriches the model's predictive capabilities. The inclusion of Autoencoders aids in feature extraction and reconstruction, enhancing the model's ability to discern relevant information sets. The advantages of this proposed model are profound. It exhibits 4.9% higher precision, ensuring that maintenance actions are precisely targeted, thus reducing unnecessary interventions. Moreover, the model achieves a remarkable 5.5% increase in accuracy, guaranteeing more reliable predictions. Its 4.5% boost in recall ensures that potential issues are identified promptly. The model's 3.9% increase in speed means that maintenance responses are faster and more effective, minimizing downtemporal instance sets. Lastly, an 8.5% improvement in the Area Under the Curve (AUC) underscores its superior performance compared to existing methodologies. The impacts of this work extend far beyond the realm of predictive maintenance levels. It contributes significantly to the realization of truly smart and efficient cities, where resources are optimized, disruptions are minimized, and the quality of life for citizens is enhanced for different use cases. This research marks a pivotal step towards the seamless integration of IoT technologies into urban environments, with the potential to revolutionize the way we manage and maintain our cities in the future scenarios.