What are the current trends and innovations in condition monitoring methods and technologies?

Vibration sensors are crucial for Pdm, especially for rotating machinery. Choosing the right sensor goes beyond trends or technology, it’s about understanding your application’s specific needs. Whether it's MEMS or piezoelectric, wireless or wired, using LoRaWAN, BLE, or Zigbee, each option has its strengths and trade-offs. Key factors like measurement range, frequency, sensitivity, and dynamic range impact the quality of data you’ll capture and how effectively you can act on it. From my experience, the right sensor isn’t just about specs & tech, it's about how well it aligns with your maintenance goals and drives actionable insights.

The emergence of affordable, compact sensors has democratized the monitoring of vital parameters like vibration, temperature, and pressure. These sensors integrate with wireless networks for real-time data transmission, which is crucial for predictive maintenance. Additionally, developments in low-power wide-area networks (LPWAN), such as LoRaWAN, offer long-distance, cost-effective connectivity in harsh conditions, ensuring continuous monitoring beyond traditional limits.

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Current trends in Condition Monitoring Systems (CMS) are integrated with IoT that include AI-driven data analytics for predictive maintenance (PdM), enabled with real time failure detection. Edge computing allows faster, localized data processing, while IoT integration provides continuous monitoring through sensors. Wireless monitoring improves accessibility and PdM strategies shift from reactive to proactive approaches. AIoT is explored and evaluated more on CMS data to shift scheduled maintenance to predictive maintenance for better prescriptive insights and recommended actions.

Such technologies will enable the transition of proactive strategies from reactive ones by cleansing data and analyzing millions of events to extract valuable features for predictive models. Edge computing came along, further reducing latency and bandwidth issues for effective data preliminary processing right at the source. An important innovation is the introduction of hybrid models, which combine strengths from physics-based and data-driven models. The modeling shows an innovative tool to provide better and, most importantly, more accurate and reliable predictive capability over using either philosophy.

Wearable devices and augmented reality (AR) have fused with precision in the digital and physical worlds to add another layer of dimension. Some of these practical applications in maintenance and repair include hands-free visualizations worn by respective technicians, and real-time data provided for the equipment such as AR smart glasses and AR wearables. However, I don't see an imminent application in condition monitoring route execution. Up to now, digitalized visual inspections have been carried out with tablets or cell phones without the need for AR.

Power-MI exemplifies cloud-based digital platforms for predictive maintenance, highlighting the distinction between data analysis and management software types. Cloud and mobile advancements make condition monitoring more flexible, enabling data aggregation from diverse sources for comprehensive analysis. This facilitates real-time monitoring of assets across locations, with mobile apps delivering instant alerts and insights to field technicians, enhancing prompt response capabilities.

In my experience of the last 3 years with over ten energy companies in Chile, a new national law required them to use predictive maintenance inspections to feed data-driven investment (CAPEX) decisions, not just financial analyses. This regulation emphasized the strategic role of condition monitoring in ensuring compliance and enhancing equipment reliability. Such standards not only improve safety and quality but also foster innovation and better stakeholder communication, fundamentally changing investment and maintenance strategies.

In my experience in Power-MI, companies are increasingly requesting that we integrate sustainability practices into our predictive maintenance plans, aligning with their existing environmental/sustainability policies. This reflects a growing commitment to environmental responsibility and energy efficiency. Additionally, the demands for data cybersecurity have significantly intensified. Companies are not just looking for compliance with personal data protection (GDPR) but are also demanding robust measures to secure all relevant industrial information.