In the growing context of digitalization and industrial innovation, the efficient management of water and gas distribution networks represents a crucial challenge for companies and utilities. Predictive monitoring, also known as Early Monitoring, is revolutionizing the sector, allowing advanced and continuous control of infrastructures.
The term “Early Monitoring” refers to an advanced preventive system that integrates IoT technologies, Artificial Intelligence (AI) and cloud platforms to collect, analyze and interpret data, allowing the early identification of anomalies and the improvement of operational efficiency. According to a report by The Insight Partners, the adoption of predictive maintenance strategies will register a 27.4% annual growth rate between now and 2028 (Source: corrierecomunicazioni.it).
However, despite the potential of these technologies, many companies in the Oil & Gas and Water sectors have not yet adopted digital solutions for the control and predictive management of their networks. This “innovation delay” leads to operational inefficiencies, high management costs and a lower quality of service, undermining the competitiveness and sustainability of the sector. Furthermore, to effectively implement Early Monitoring operations, it is necessary to continuously collect data and train a digital twin of the network, a process that requires time before it can generate concrete benefits.
Challenges of Early Monitoring for distribution networks
Water and gas distribution networks are subject to critical issues such as leaks, corrosion, pressure drops and anomalies in operating parameters. Delayed detection of these problems can lead to high costs, inefficiencies and safety risks. Reactive maintenance strategies, still widespread in the sector, are often insufficient to prevent failures and optimize resources.
Implementing an Early Monitoring system helps overcome these difficulties, transforming management from reactive to proactive. Through real-time monitoring and predictive analysis, anomalies can be identified before they become failures, reducing emergency interventions and improving service quality.
Using IoT and AI as enabling technologies
The Internet of Things (IoT) and Artificial Intelligence (AI) are the key technologies behind Early Monitoring. IoT devices, equipped with advanced sensors, collect data on pressure, temperature, vibrations and other critical parameters of infrastructure. This data is transmitted in real time to cloud platforms, where AI algorithms analyze the information to identify anomalies and predict possible failures.
AI, through machine learning and deep learning, allows to identify anomalous patterns in data, suggesting maintenance interventions before failures occur. This approach ensures a more intelligent management of networks, with a positive impact on service continuity and resource optimization.
Technological and sustainable advantages
The adoption of Early Monitoring in water and gas distribution networks offers numerous advantages:
- Reduction of maintenance costs: the prevention of failures allows to limit corrective interventions and associated costs, reducing the need for frequent replacements of components and optimizing the management of resources.
- Improved safety: continuous monitoring ensures timely identification of anomalies to prevent critical situations, protecting infrastructures and reducing risks for operators and users.
- Operational efficiency: predictive analytics optimizes the management of resources and operational flows, improving service continuity and minimizing plant downtime.
- Environmental sustainability: accurate control of networks reduces waste and negative environmental impacts, limiting losses of water resources and reducing emissions related to infrastructure management.
Onyax’s solution with IoT devices and AI platform
In a constantly evolving sector, Onyax positions itself as a strategic partner for companies that want to innovate the management of their infrastructures. Onyax, thanks to its experience in electronic engineering and telecommunications, has developed integrated solutions for predictive monitoring of distribution networks. Its IoT devices, such as TUBE-T3 and BLACKBOX, are essential tools for data collection and analysis.
- TUBE-T3: advanced sensor and datalogger for monitoring pressure, temperature, vibrations and network leak detection with simple installation and without complex wiring.
- BLACKBOX: cathodic protection system, capable of collecting and analyzing electrical parameters to prevent corrosion of pipes.
These devices integrate with ACE (Acquisition Control Ecosystem), the AI platform developed by Onyax for advanced data analysis. ACE uses machine learning algorithms to transform raw data into insights useful for predictive maintenance, offering intuitive dashboards, synoptic visualization tools and interactive reporting.
