Last updated on May 1, 2024

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

Condition monitoring is a key component of predictive maintenance, as it allows you to detect and diagnose faults in your equipment before they lead to failures or downtime. By using various methods and technologies, you can collect and analyze data on the performance, health, and condition of your assets, and take proactive actions to optimize their efficiency and reliability. In this article, we will explore some of the current trends and innovations in condition monitoring methods and technologies, and how they can benefit your predictive maintenance strategy.

1 Sensors and IoT

One of the main drivers of condition monitoring is the advancement and proliferation of sensors and IoT devices, which enable you to collect and transmit data from your equipment in real time. Sensors and IoT devices can measure various parameters, such as vibration, temperature, pressure, humidity, sound, and more, and provide insights into the operating conditions and potential issues of your assets. Moreover, sensors and IoT devices can be integrated with wireless networks, cloud platforms, and edge computing, which allow you to store, process, and access your data remotely and securely.

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Carlos E. Torres

Transforming predictive maintenance to boost industrial productivity with proven savings | CEO
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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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2 Data analytics and AI

Another trend that is transforming condition monitoring is the application of data analytics and AI techniques, which help you to extract meaningful information and patterns from your data, and make better decisions based on your data. Data analytics and AI can enable you to perform various tasks, such as data cleansing, feature extraction, anomaly detection, fault diagnosis, root cause analysis, and predictive modeling, and provide you with actionable recommendations and alerts. Furthermore, data analytics and AI can leverage machine learning, deep learning, and neural networks, which allow you to learn from your data and improve your models over time.

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Carlos E. Torres

Transforming predictive maintenance to boost industrial productivity with proven savings | CEO
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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.

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3 Digital twins and simulation

A digital twin is a virtual representation of a physical asset, system, or process, which can be used to simulate and test different scenarios and outcomes, and optimize your performance and maintenance. By using digital twins and simulation, you can create a digital copy of your equipment, and monitor its condition and behavior in real time, using data from sensors and IoT devices. You can also use digital twins and simulation to predict the future state and performance of your equipment, and evaluate the impact of various factors, such as environmental conditions, operational parameters, and maintenance actions.

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Carlos E. Torres

Transforming predictive maintenance to boost industrial productivity with proven savings | CEO
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Despite seeing much research on technologies like digital twins, I don't find them as applicable to predictive maintenance as existing models. The concept seems abstract, with no direct application in PdM. However, digital twins and simulation seem to revolutionize how we visualize and interact with assets, allowing real-time monitoring of future performance.

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4 Augmented reality and wearable devices

Augmented reality (AR) and wearable devices are emerging technologies that can enhance your condition monitoring capabilities and user experience, by providing you with interactive and immersive ways to access and visualize your data. AR and wearable devices can enable you to overlay digital information and graphics on your physical environment, and interact with your equipment using voice, gesture, or touch. You can also use AR and wearable devices to perform remote inspections, diagnostics, and repairs, and collaborate with experts and technicians in real time.

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Carlos E. Torres

Transforming predictive maintenance to boost industrial productivity with proven savings | CEO
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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.

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5 Cloud-based and mobile solutions

Cloud-based and mobile solutions are another trend that is shaping the future of condition monitoring, as they offer you more flexibility, scalability, and accessibility to your data and applications. Cloud-based solutions allow you to store, process, and manage your data in the cloud, and access it from anywhere and any device. You can also use cloud-based solutions to integrate your data with other sources and platforms, and leverage advanced analytics and AI capabilities. Mobile solutions allow you to use your smartphone or tablet as a condition monitoring device, and receive notifications, reports, and dashboards on your device.

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Carlos E. Torres

Transforming predictive maintenance to boost industrial productivity with proven savings | CEO
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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.

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6 Standards and regulations

The last trend that we will discuss is the development and adoption of standards and regulations for condition monitoring, which aim to ensure the quality, safety, and reliability of your equipment and processes. Standards and regulations can provide you with guidelines and best practices for selecting, implementing, and evaluating your condition monitoring methods and technologies, and help you to comply with industry norms and legal requirements. Standards and regulations can also facilitate the communication and interoperability between different stakeholders and systems, and promote the innovation and improvement of condition monitoring solutions.

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Carlos E. Torres

Transforming predictive maintenance to boost industrial productivity with proven savings | CEO
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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.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Carlos E. Torres

Transforming predictive maintenance to boost industrial productivity with proven savings | CEO
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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.

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

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1 Sensors and IoT

2 Data analytics and AI

3 Digital twins and simulation

4 Augmented reality and wearable devices

5 Cloud-based and mobile solutions

6 Standards and regulations

7 Here’s what else to consider

Predictive Maintenance

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