Sustainability

Urban mobility systems are the lifelines of modern cities, encompassing infrastructure such as roads, bridges, tunnels, and transit networks. Maintaining these systems efficiently is paramount to ensuring uninterrupted operations, safety, and cost-effectiveness. Predictive maintenance, powered by Artificial Intelligence (AI), has emerged as a game-changer, enabling infrastructure managers to anticipate failures and optimize maintenance schedules.

Predictive maintenance involves the use of advanced analytics, machine learning (ML), and IoT sensors to monitor the condition of assets in real-time. By analyzing historical and real-time data, predictive models identify patterns and anomalies that signal potential failures. This approach contrasts with traditional reactive maintenance, which addresses issues after they occur, and preventive maintenance, which relies on fixed schedules irrespective of actual asset conditions.

Core Components of AI-Driven Predictive Maintenance

• IoT Sensors and Data Collection: 

  Sensors capture diverse data streams such as temperature, vibration, pressure, and structural stress in real-time. For example, accelerometers and strain gauges embedded in bridges detect vibrations that could indicate structural weaknesses.
• Machine Learning Algorithms: ML models process and analyze data to identify degradation patterns and predict time-to-failure. For example, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are used for time-series analysis in dynamic systems like escalators or traffic signals.
• Digital Twins: Digital twins—virtual replicas of physical assets—simulate operational conditions to forecast failures and test maintenance strategies. For example: A digital twin of an urban rail system can model wear and tear based on real usage patterns.
• Centralized Monitoring Platforms: SCADA and cloud-based platforms aggregate and visualize data, allowing operators to monitor infrastructure health remotely. Integration with AI enhances decision-making by providing actionable insights.

Applications in Urban Mobility Systems

AI-powered sensors monitor structural integrity, detecting cracks or corrosion before they escalate in the wide spectrum of urban infrastructure, including tunnel and bridges, traffic signal systems, public transit and roadway infrastructure. Predictive models optimize inspection schedules, reducing costly manual checks, downtime. Anomaly detection algorithms predict hardware failures, enabling timely replacements. Pavement sensors and ML algorithms can also predict pothole formation based on traffic load and environmental conditions for proactive repair schedules.

A Technical Glance at AI in Predictive Maintenance

The anomaly detection is usually carried out by identifying the outliers in sensor data using techniques like k-means clustering or autoencoders. For predictive modelling, failure probabilities can be forecasted through regression models, decision trees, and neural networks. The maintenance schedules can be further optimized by reinforcement learning from simulated environments.

However, the high frequency sensor data requires scalable storage solutions, which are often cloud based. The data quality remains as one of the biggest challenges which needs to be addressed by preprocessing techniques such as imputation and smoothing. The learning from the predictive models must integrate seamlessly with legacy systems and SCADA platforms, requiring robust APIs and middleware, which must be planned in advance.

“AI in predictive maintenance isn’t just about anticipating failures – it’s about redefining the way we maintain, operation, and extend the life of critical infrastructure.”
Head of ITS & TECH, MWB

What we can expect in future

Based on the findings from the research and prototype of predictive maintenance project through a strategic collaboration of MWB, we can expect significant accuracy (>80%) in predicting motor failures to reduce downtime (<20%) in addition to a estimated lower operational costs by 15% within the first year itself.