Artificial intelligence (AI) technologies are revolutionizing the way electrical assets are maintained. AI enables organizations to gain a competitive advantage by leveraging condition monitoring data to make better, data-driven decisions, minimize downtime, optimize resources, and improve operational effectiveness.
Cloud computing, IoT, and broadband capabilities have made these AI-enabled advantages possible.
Today, the world continues to evolve digitally due to the rise of remote work and growing technological advancements in areas such as robotics and artificial intelligence. The need for digitalization has become more significant in many industrial sectors, regardless of the competitive landscape.
Digitalization is a prerequisite for every organization as they strive to become more efficient and achieve sustainability goals. This evolution fosters the importance of improving thinking, structure, operations, and development, proactively moving toward more innovative ways to solve problems and adapt to the ever-evolving digital landscape.
An increasing shift to electric power is inevitable for every organization, and to ensure optimal operation, reliability, and personnel safety, proper maintenance of electrical infrastructure is as essential as maintaining a stable power supply.
In many industries, organizations have relied on traditional methods of electrical asset maintenance. Traditional asset maintenance revolves around periodic inspections, preventive maintenance, reactive repairs, or failure response.
Traditional asset maintenance approaches have historically had flaws, which can result in unplanned downtime and higher maintenance costs. These flaws negatively impact continued operations in industries compared to modern maintenance approaches powered by IIoT and AI.
With the advent of Smart Industrial IoT, the landscape of critical asset maintenance has undergone a transformative shift, also evident in electrical assets. These assets are now leveraging the predictive capabilities of sensor-connected platforms for better asset monitoring, providing alarm notifications and data-driven insights, resulting in improved efficiency.
The role of Intelligence in the maintenance of electrical assets
AI is playing a significant role in addressing electrical asset maintenance, positively transforming organizations that rely on critical infrastructure to deliver products and services. Here, we explore the role and impact of AI on the maintenance of these assets.
Platforms equipped with intelligence can perform some tasks automatically, thus eliminating the need for interpretation of results and manual intervention.
AI in predictive maintenance
The development of Industry 4.0, which connects manufacturing technology through the Industrial Internet of Things (IIoT), is closely linked to AI's capabilities for predictive maintenance.
Predictive maintenance is one of the most prominent areas in electrical asset maintenance, where technological advances such as AI are present. Sensors inside electrical equipment enable 24/7 predictive maintenance and continuous equipment monitoring. Temperature, vibration, energy consumption, and other metrics are among the parameters that can be collected.
The 'brain' of the intelligent system receives condition monitoring data. It searches for patterns that may indicate potential deterioration, anomaly, or worse, failure, using data related to environmental conditions and performance metrics. With data collected in real time, the always-on predictive maintenance approach provides continuous feedback on critical electrical assets.
Reliance on periodic or reactive maintenance approaches across industries is the typical approach to asset maintenance. In today's globalized business landscape, preventive maintenance may no longer be sufficient as an asset management strategy.
Regularly inspecting the condition of electrical equipment doesn't guarantee good asset performance. There are now better, more efficient methods to reduce unplanned downtime and lost productivity.
Companies whose operations depend significantly on the distribution of electrical power in their facilities frequently need to perform maintenance to ensure that their equipment is capable of withstanding the demands of daily operations, especially when these assets are in use for extended periods. Studies on the impact of electrical equipment aging on the occurrence of failures demonstrate a direct relationship with adopted maintenance practices. A notable disadvantage of reactive or periodic maintenance is its potentially high cost, particularly when robust maintenance processes are not implemented, especially if precision maintenance strategies are not adopted.
The prevalence of automation and digital tools, such as IIoT platforms, has accelerated the paradigm shift in asset maintenance and facility management over the past decade. With intelligence and predictive maintenance, strategically placed sensors within equipment enable continuous monitoring of viscosity, energy consumption, vibration, and temperature. By leveraging real-time data, analytics, and intelligent algorithms, AI can alert to potential failures before they occur and provide actionable insights. The system examines temperature trends, load patterns, thresholds, and other parameters in electrical assets such as transformers, circuit breakers, and cables. This allows the platform to predict potential problems before anomalies escalate into asset failures.
The impact of AI on electrical asset maintenance continues to evolve, with more areas for improvement as technology develops. Some of the benefits of AI on electrical asset maintenance include:
Enhanced security and risk mitigation
Electrical asset management must prioritize safety, both for personnel and property. The predictive capabilities of the IIoT help identify safety hazards related to potential asset failures. Intelligent IIoT can analyze data from multiple sensors on electrical assets to identify anomalies and safety concerns. Safety risks are mitigated by early detection of issues, preventing accidents and providing maintenance personnel and other stakeholders with a safer working environment.
Improved equipment efficiency and reliability
For manufacturing, logistics, and operations, techniques like periodic maintenance checks may not be sufficient to verify asset reliability in a complex, fast-paced environment. Through continuous monitoring and analysis of real-time data, electrical equipment will become more reliable. Large volumes of sensor data collected from electrical assets can be monitored by intelligent algorithms to find the connections and patterns that humans often miss using thermal imaging. Maintenance can be precisely scheduled to minimize outages, help prevent catastrophic failures, and maximize asset longevity by identifying anomalies and early indicators of deterioration. By ensuring assets are operating within optimal parameters, this proactive strategy reduces the chance of unplanned failures and increases reliability.
Cost reduction and resource optimization
With early failure detection, organizations can optimize their costs by applying accurate corrective measures as soon as an anomaly is identified. This significantly reduces the need for unnecessary periodic maintenance and time-consuming checks, which often fail to ensure asset efficiency when compared to the predictive approach enabled by artificial intelligence in the IIoT. Unplanned downtime, which can be costly for an organization, is mitigated by AI-driven predictions that optimize resource allocation and reduce operating costs.
Data-driven decision making
Integrating intelligence into electrical asset maintenance provides predictive capabilities by analyzing large amounts of data from multiple sensors, historical records, and real-time monitoring systems to identify anomalies, facilitating data-driven decision-making processes. Insights from this data generate tasks that help organizations monitor asset health and implement precise interventions such as maintenance schedules, part replacement, and performance assessment, guiding informed corrective actions.
Fault detection and proactive maintenance
Algorithms identify potential failures in real time by analyzing sensor data. Identifying anomalies and predictable failures in electrical equipment helps organizations take proactive action to prevent failures and unplanned downtime, improving asset efficiency and optimizing operations. Continuous monitoring of electrical assets eliminates unnecessary maintenance, optimizing operational efficiency and avoiding additional maintenance costs.
Conclusion
In conclusion, the electrical asset maintenance landscape is constantly changing due to the development of AI in the IIoT. The ideal approach involves progressive implementations, starting with parameterizing operations through intelligent rules. This allows the system to quickly identify operational issues, providing considerable gains in a short period of time. With the initial implementation of minimal and basic operational intelligence, the benefits are already impressive.
In a second stage, progress can be made toward implementing an AI system capable of analyzing and identifying dynamic behavioral changes. While there are initial costs involved in integrating AI and considerable time required to train the system in predictive maintenance, the long-term benefits significantly outweigh the operational costs (OPEX) and capital costs (CAPEX). This advancement is particularly crucial for industries that frequently face resource constraints and budget cuts.
Innovative computing, the Industrial Internet of Things, data analytics, and advanced predictive models are completely redefining the maintenance environment. Integrating AI into a predictive maintenance model for assets promises a brighter future for a highly responsive and efficient maintenance ecosystem.
These technological changes are opening the door to more proactive and efficient maintenance, allowing organizations to anticipate problems, reduce costs, and maximize the availability of their electrical assets.
Application
Ensure energy efficiency and prevent fires with Electrical and Control Panel Monitoring
The Electrical and Control Panel Monitoring catalog shows how Bridgemeter, Above-Net 's Industrial IoT predictive analytics solution, enables intelligent remote monitoring of any electrical and control panel in real time.
With information: Exertherm
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