The Importance of Predictive Maintenance in Infrastructure Management

As the global critical infrastructure continues to age, governments are increasingly turning to predictive maintenance to address maintenance needs and enhance safety measures. The preference for predictive maintenance over traditional approaches has been amplified by catastrophic incidents such as the 2018 Morandi Bridge collapse, which resulted in 43 casualties and extensive damages. Additionally, the impact of extreme weather events linked to climate change has further exposed vulnerabilities in aging infrastructure.

Structural Health Monitoring (SHM) has experienced a significant surge in recent years, owing to advancements in sensor and connectivity technology, as well as more sophisticated data analytics software platforms. According to ABI Research, SHM sensors are projected to reach 22.9 million connections by 2030, with significant growth expected in both wired retrofitted sensors and wireless retrofitted sensors.

Maryam Zafar, an IoT markets analyst at ABI Research, emphasized the increased ease of integrating sensors into operations, thanks to a wider variety of IoT sensor hardware. This shift has allowed asset owners to move away from costly and cumbersome implementations to more cost-effective and easily-installed solutions. Zafar also highlighted the growing focus on software and analytics platforms by vendors, citing the need for enhanced software intelligence to extract valuable insights from large volumes of data.

In the SHM market, innovation is taking place on both hardware and software fronts. Hardware advancements include smaller data loggers, greater edge processing capabilities, and a wider variety of sensors and technologies. On the other hand, software innovation centres on the development of analytics platforms and the integration of Artificial Intelligence (AI) to improve predictive capabilities and offer more value to asset owners and managers.

The rail industry is expected to become one of the most significant markets for SHM, particularly due to the projected doubling of demand for rail transport in the next two decades. To address this anticipated surge in demand, rail operators are turning to digitisation and wireless sensor technology to monitor critical aspects of rail infrastructure, such as tracks, switches, and slopes, in order to predict failures and implement more efficient maintenance strategies.

According to Zafar, the lack of awareness in the market, combined with expensive technologies, has resulted in low market penetration for SHM. However, with the introduction of new technologies, this is expected to change, transitioning from end-of-life maintenance to solutions that are designed into projects from the outset. Zafar also advised technology vendors to seize new opportunities and position themselves strategically to target the various markets within the SHM ecosystem.

In conclusion, the demand for predictive maintenance in infrastructure management is on the rise, driven by safety concerns, climate change-related vulnerabilities, and advancements in sensor and connectivity technology. As governments and industries seek to address the maintenance needs of aging critical infrastructure, the adoption of predictive maintenance approaches, supported by innovative hardware and software solutions, is set to play a pivotal role in ensuring the safety and efficiency of infrastructure worldwide.