5 Benefits of Predictive Maintenance
In recent years, the term Predictive Maintenance has risen to prominence in a number of industries...
HVAC maintenance is key to optimizing energy efficiency. Older studies showed that best practices in building maintenance and operations reduce energy use by 10-20% and cut energy bills by 15-20% in commercial properties. With advances in smart technology, those numbers can be significantly higher.
Preventive maintenance has long been the most common approach for HVAC systems. Though preventive, or planned maintenance, is a step up from reactive maintenance, it has major drawbacks. These include:
With HVAC systems accounting for 40-60% of total energy consumption in commercial buildings, effective maintenance is essential to keeping energy use and expenses under control. An intelligent building management platform (IBMP) makes it possible to go beyond preventive measures and implement predictive maintenance for HVAC. With this innovative approach, you can take efficiency, performance, and savings to the next level.
Predictive maintenance utilizes sensors to gather information about equipment performance. An IBMP uses advanced machine learning algorithms to analyze sensor data, detect anomalies, and predict the likelihood of failure of various HVAC components. This data-driven approach of predictive maintenance for HVAC systems results in greater cost savings and offers many important benefits over conventional preventive maintenance.
Poor energy efficiency and improper functioning of HVAC systems can result in enormous waste. And the problem doesn’t have to be major; over time, even small performance deficits can add to serious efficiency losses.
Predictive maintenance of HVAC, including multi-zone variable air volume (VAV) systems, proactively identifies operational issues leading to energy waste and notifies stakeholders of maintenance needs as soon as potential problems are identified. But a cutting-edge IBMP doesn’t just point out problems, it also isolates the cause of those problems and recommends meaningful solutions. As a result, restoring optimal performance and efficiency is faster and easier.
HVAC is a complex system with multiple components that face leaks, blockages, and deterioration on a day-to-day basis. Poor performance of a single component can impact the overall performance of the equipment. Even a dirty air filter forces the entire system to work harder to compensate for the diminished airflow, increasing the risk of system breakdown.
Predictive maintenance utilizes machine learning algorithms to evaluate the series of interrelated data points collected by HVAC systems and identify potential issues that may impact future equipment performance. This extends the HVAC lifespan, ensures you get the most value out of your equipment, and reduces the frequency of costly capital expenses.
Smart HVAC controls optimize environmental conditions within a building. Critical air quality data including carbon-dioxide level, humidity, and temperature, along with equipment data such as duct static pressure, can be integrated to modulate the airflow in one area without over-ventilating or starving the rest of the building.
When such a system is working properly, occupant safety and comfort are greatly enhanced. Predictive maintenance based on historical and real-time data ensures any aberrations are dealt with promptly, long before occupants notice problems or make complaints.
Predictive maintenance of HVAC systems driven by an IBMP fundamentally transforms a building’s maintenance program and dramatically lowers maintenance costs.
Combined with reduced energy costs, these savings make predictive maintenance an excellent investment with meaningful returns.
An IBMP is the core of predictive maintenance for HVAC. But not all IBMPs are alike. An industry-leading platform, like onPoint, ensures that you have the insights and capabilities you need to implement an advanced predictive maintenance plan and realize the full benefits of smart building technology.
onPoint uses data points such as fan speed, damper positions, and outside air temperature to predict optimal temperature ranges and identify the potential causes of deviation. Its highly reliable predictions allow you to take meaningful action as needed to optimize HVAC performance. To limit false alarms, onPoint analyses utilize machine learning and historical trends to ensure data integrity and filter out extraneous information.
onPoint’s data-driven predictive maintenance for HVAC allows you to create powerful efficiency strategies, realize significant savings, improve operations, and enhance the occupant experience. With onPoint, you get more out of your equipment and your building.
Rebecca Butler, Senior Manager of Customer Onboarding at Buildings IOT, manages the delivery of smart building products to the marketplace. She holds a master’s degree in building systems engineering, and has deep experience delivering software solutions across building automation, IoT analytics, and demand response applications.
If you’re reading this article, chances are you’re considering implementing a predictive...