Predictive maintenance policy adopted by many industries necessitates an absolute reliability across power plants, utility, transport systems, and emergency services. Maintenance is a critical activity that takes place in production. Machine failures during production can lead to adverse effects on the production schedule, delivery delays, or employee overtime to compensate for the loss.
Digital Blanket® allows thresholds to be set for all the key parameters and warning or alarms are generated when threshold is breached. This provides unified alerts and ticket management for proactive management of energy consumption and equipment across multiple factory sites and ensures that any issues are responded to in structured and time-bound manner. This also helps in smoother functioning of the equipment and improved life
Real-time tracking can avoid potentially critical situations (e.g., voltage surge) due to timely action and can increase equipment life. This means reduction in on-site service costs, reduced down-time and equipment change.
The trends, seasonality over the period (daily, weekly, monthly etc.) can provide good insights to the business and help optimize energy costs. Thresholds can be set for various parameters and whenever there is a breach, an immediate alert can be sent to concerned people/teams. This can be further integrated into a helpdesk tool for SLA tracking.
Energy monitoring is mostly a manual activity, which is prone to errors. Energy bills are available, but they only act as a ‘rear view mirror’. Digital Blanket® provides a very structured approach to Energy Management.
Energy Metering, Source and Load, Trend energy consumption by hour, day, week, month across system, zone, floor, building etc.
Qualify all collected energy data for data quality and reject outliers due to faulty systems. Prepare data for energy analysis.
Analyse energy consumption patterns by site, machine or project. Identify peak loads and factors, power quality issues, compare actual usage vs set targets.
Baseline energy usage for each site / process / shift etc. Benchmark energy usage with industry benchmarks for the specific city / segment / weather. Compare buildings / sites / floors through Energy Performance Indices – per unit area or per occupant or per system.
Use statistical models to predict energy consumption and hence prepare for proactive actions.
Allocate energy cost by business group, department, area, building, grid, green energy, diesel generator and by load – HVAC, Lighting, UPS, Machinery etc. Gain understanding of energy costs and what can be optimized.
Energy consumption reported against equivalent CO2 emissions or trees saved. Report against set targets.
Review energy usage and identify opportunities for reducing wastage including better scheduling, better fault detection, load optimization etc.