Middle East-based HITEK AI, a part of the Farnek group of companies, has launched its latest innovative solution, the Predictive Maintenance and Anomaly Detection System, which is now available within its CAFMTEK platform.
This AI-driven tool is designed to empower facilities managers, engineers, and maintenance professionals by enhancing the operational efficiency and reliability of MEP assets, including HVAC, Main Distribution Board (MDB) assets as well as many others.
“By adopting CAFMTEK’s Predictive Maintenance and Anomaly Detection System, businesses can transform their maintenance strategies from reactive to predictive. This innovative approach empowers professionals with the tools needed to reduce breakdowns, optimise operations, increase residents’ comfort, and drive cost-effective results,” said Javeria Aijaz, Managing Director of HITEK AI.
Using cutting-edge machine learning models, the system predicts potential failures, detects anomalies, and provides actionable insights that enable predictive maintenance. It focuses on critical metrics unique to each asset type, providing facility managers with clarity and control over building operations.
For HVAC, the platform analyses supply temperature and supply fan data in real-time to anticipate potential failures. It also provides early alerts when failure probability scores trigger alerts when thresholds are breached, enabling timely intervention.
When it comes to MDB assets, real-time data for metrics such as total kWh, power factor, voltage, and current are compared against dynamic thresholds derived from historical data.
In addition, alerts highlight specific metrics causing anomalies, aiding faster and more accurate diagnostics. And monthly energy consumption trends are evaluated, revealing inefficiencies or unexpected changes in performance.
The system has also introduced a tailored Preventive Planned Maintenance (PPM) strategy by analysing failure histories and patterns. It dynamically recommends maintenance cycles based on equipment performance, from monthly schedules for frequent issues to annual checks for occasional or ad-hoc failures.
“This comprehensive approach significantly reduces unexpected failures while increasing the longevity of critical assets. By ensuring maintenance is based on actual asset conditions rather than rigid schedules, organisations benefit from cost savings, improved performance, and operational stability,” commented Aijaz.
The system’s machine learning models continually adapt, incorporating new data to improve prediction accuracy and optimise dynamic thresholds. Each incident informs future updates, ensuring the tool evolves alongside changing operational demands.
“With the Predictive Maintenance and Anomaly Detection System, CAFMTEK is revolutionising how facility managers maintain their HVAC and MDB assets. Our goal has always been to combine data-driven insights and AI to reduce downtime and maximise performance,” added Aijaz.
