How Predictive Maintenance can lead to reduced energy consumption and increased sustainability.
Predictive maintenance (PdM) is defined as a “proactive” approach that leverages advanced technologies and data to monitor equipment condition and predict potential failures before they occur.
This methodology significantly improves the overall functionality of any industrial plant or system and can help reduce energy consumption in every process.
There are different levels in which Predictive Maintenance can benefit energy consumption:
- Improve energy efficiency: By monitoring equipment condition and performance, PdM can optimize energy usage. This involves detecting inefficiencies and correcting them before they lead to excessive energy consumption. According to studies, predictive maintenance can also reduce energy usage by minimizing production waste (IBM – United States).
- Extend equipment lifespan: Regular, data-driven maintenance helps extend the lifespan of any piece of machinery. This not only saves repair and replacement costs but also ensures equipment remains energy-efficient throughout its lifecycle (Deloitte United States).
- Optimized maintenance scheduling: Predictive maintenance based on effective data and innovation technologies schedules maintenance tasks. This means maintenance is performed only when necessary (IBM – United States).
- Reduce maintenance costs: PdM can reduce overall maintenance costs by 5-10%. These savings, which are reflected in overall energy consumption, are achieved through improved planning, efficient use of resources, and avoiding high costs of emergency repairs (Deloitte United States).
Energy variation and sustainability under control with D.gree Hype Technologies
D.gree, supported by its mother-company SailADV with over 20 years of experience, offers an ecosystem of technologies called Hype Technologies, designed to effectively manage predictive maintenance and, as a consequence, overall energy consumption of the manufacturing industry.
Key features of D.gree Hype Technologies for energy consumption control:
- Real-Time Monitoring: The H-Log data collection of energy consumption ensures immediate identification of unnecessary consumption peaks and energy waste.
- Predictive Analysis: Utilizing cutting edge computing, operational technology (OT), AI, and Machine Learning, D.gree can analyze energy consumption patterns and predict future trends based on historical data. This helps optimize machinery use and maintenance scheduling to minimize energy consumption and production impact.
- Load Optimization: Thanks to the H-System platform which allows full supervision, energy loads can be balanced and divided in intelligent ways, preventing overloads and ensuring efficient energy use.
- Anomaly Detection: The advanced data management methodology allows D.gree to detect inefficiencies and anomalies, triggering corrective actions promptly.
- Automation and Control: Integrating specific systems to regulate energy consumption based on actual production reduces energy waste, especially during low-demand periods.
- Reports and Feedback: D.gree provides detailed energy usage reports, essential feedback for strategic planning and operational decisions aimed at reducing energy consumption.
By adopting predictive maintenance and leveraging D.gree advanced technologies, significant cost savings, enhanced efficiency and improved system performance can be achieved.