Operations
EKO shifts current facilities maintenance practices from reactive to predictive by harnessing the power of a facility’s real-time data through machine learning and AI models, providing building operators with timely insights and workflows to effectively maintain and utilize their smart buildings - all while maintaining a true “living” replica of their facility. Our platform provides a number of key outputs to improve the facilities maintenance process:
Predictive Maintenance
EKO analyzes operational data from building assets and past alarms and failures to forecast potential failures and recommend maintenance procedures in advance. This component helps minimize downtime, extend the life cycle cost of assets, and reduce both the use of consumables and energy.
Automated Fault Detection and Diagnosis (FD&D)
EKO applies unsupervised time-series clustering and anomaly detection algorithms to identify potential faults in assets using streaming sensor data. This component helps reduce false-positive alarms and prioritize urgent issues, helping to reduce the alarm fatigue that maintenance staff currently experience.
Root Cause Analysis
EKO predicts the root cause of issues detected by the FD&D component and advises on the optimal actions to take to solve the problem by recommending work orders for the computerized maintenance management system (CMMS). This component allows operators to focus on the root cause of an issue for faster resolution.
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