Transformers are critical assets in industrial and electrical processes. According to the CIGRE transformer reliability survey, 1 out of every 200 transformers fails annually. Thus, there is a higher demand for online and continuous monitoring systems to prevent breakdowns, reduce unplanned downtime and mitigate safety hazards. According to a media report the transformer monitoring system market could grow at a rate of 8.9% in the next decade. Does that mean the future of condition monitoring is synonymous to the future of monitoring system? This article brings a well-rounded discussion on the emerging trends in condition-based monitoring and its role in achieving sustainable operation beyond the hardware advancement.
What is condition-based monitoring?
Condition-based monitoring is described as a method to optimize equipment performance and lifecycle by using various measurement techniques. It promotes predictive maintenance and thus, reduces risk of abrupt failure and safety hazards by analysis of suitable data on the performance, health, and condition. Additionally, it supports meeting sustainable asset management KPIs viz., efficient gains and lower operational costs for the utilities. As a result, it ensures proactive measures to optimize asset efficiency, availability, safety, sustainability and reliability.
In today’s industrialized world, automation, big data and Internet of Things (IoT) drives the innovations in condition monitoring systems. The development and widespread use of sensors and IoT devices allows bi-directional and real-time data interaction with asset. These devices can detect a wide range of characteristics to evaluate incipient faults in assets. Moreover, combining them with wireless networks, cloud platforms, and edge computing enables distant data processing, archiving, and access.
What are the emerging trends in condition-based monitoring?
In our previous blog, data-driven actions emerges at the core of all innovative trends in condition-based monitoring strategies. From a transformer prospective, storing essential data is one of the best practices of digital condition monitoring. Not only this decreases the cost of testing, but also it reduces complexities in navigating and modeling when necessary. Critically-based data segregation is an effective way to adapt to this strategy. However, sufficient knowledge on data storage, management and analysis is equally pivotal. A strong infrastructure can support massive data storage to ensure that no operational anomalies are unnoticed. In fact, artificial intelligence (AI) has emerged as a strong tool to manage and analyze data to leverage and maximize the benefits of asset management strategies.
Data analytics and AI
With industry 4.0 at our doorstep, the use of data analytics and AI to extract useful information and trends from condition monitoring data and improved data-driven decision-making is revolutionary. AI tools can help with data purification, feature extraction, anomaly detection, problem diagnosis, root cause analysis, and predictive modelling. For instance, the use of machine learning, deep learning, and neural network models is quite popular in these cases.
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 the performance of an asset. With digital twins and simulation, one can virtualize their equipment and monitor its condition and behavior in real time, using data from sensors and IoT devices. The current application of digital twins in transformer industry is one of our ongoing research project.
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. It enables you to overlay digital information and graphics on your physical environment, and interact with your equipment using voice, gesture, or touch. Research of the use of AR technology is underway for remote monitoring and diagnosis of operational parameters in transformer viz., current, voltage, oil temperature etc.
Cloud-based and mobile solutions
Another trend is the use of cloud-based and mobile solutions to achieve more flexibility, scalability, and accessibility between data and asset. It allows you to store, process, and manage your data in the cloud, and access it from anywhere and any device. Moreover, data integration with other sources and computing platforms can better leverage the 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.
Standards and regulations
Apart from these measure, secure investments and policy regulations are also necessary to ensure best implementation of sustainability principles, reducing environmental impact and promoting long-term viability in transformers. 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 is the key to implement these changes. It can provide guidelines and best practices for selecting, implementing, and evaluating suitable condition monitoring methods and technologies that comply with industry norms and legal requirements. It can also facilitate the communication and interoperability between different stakeholders and systems, and promote the innovation and improvement of condition monitoring solutions.
How can we ensure sustainability with these practices?
Reliability and sustainability always go hand-in-hand. Ensuring sustainability in transformer condition monitoring involves implementing practices that minimize environmental impact, promote resource efficiency, and support long-term viability. Use low-power sensors, automated data acquisition systems, and efficient data transmission methods to minimize energy requirements. Consider utilizing renewable energy sources, such as solar or wind power, to meet the energy needs of the monitoring system. Perform a lifecycle assessment of the monitoring equipment along with the infrastructure.
Implement efficient data management strategies to reduce the storage and processing requirements. Utilize remote monitoring technologies to minimize the need for physical site visits. Use data analytics, machine learning, and AI algorithms to identify patterns, predict failures, and schedule maintenance activities when needed, avoiding both unnecessary downtime and premature maintenance. Develop proper procedures for recycling and disposing of monitoring equipment, batteries, and other components. Stay updated on industry advancements and collaborate with experts and vendors to implement innovative solutions. Encourage energy-saving habits, waste reduction, and environmentally friendly practices. Engage with stakeholders, including manufacturers, suppliers, and regulatory bodies, to collectively work towards sustainable practices in transformer condition monitoring.
Try Ronin AI for improved experience on transformer condition monitoring
A robust analytics software in conjunction with digital sensors and IoTs will maximize the benefits of online condition monitoring and ensure effective maintenance practices. Ronin AI is a huge, interactive, AI-powered framework that allows one to reduce the complexity behind data. For a non-expert on condition monitoring and diagnostics, Ronin AI helps to make decisions. These help in sorting machines as per urgency and logic. Ronin AI works best with CSV files containing at least the dissolved gases in oil of transformers. The accuracy and precision moves multifold if all data is available. Ronin AI uses the best recognition models to curate the most potential health index scenario and predicts the score.
So, don’t wait and grab your free trial right away!