Carrier Harnesses Amazon’s Machine Learning to Revolutionize HVAC Predictive Maintenance: Reducing Downtime and Enhancing Customer Experience

HVAC solutions leader Carrier Corporation has long been dedicated to improving product reliability, but in recent years the company has faced challenges due to their threshold-based systems. These systems, lacking the capacity to process large amounts of data, were inefficient and unable to accurately predict equipment failures, leading to unexpected downtime and impacting customer experience adversely.

Recognising these issues, Carrier turned to a partnership with the Amazon Machine Learning Solutions Lab. This decision was driven by Amazon’s role as a pioneering leader in machine learning technologies, with proven capacity to provide scalable and robust solutions.

The partnership materialised into an innovative machine learning model, harnessing the power of over 50 terabytes of historical sensor data that the company had amassed. The crux of this model rests on the shift from Carrier’s traditional threshold-based systems to a more predictive approach.

In the past, equipment was evaluated based on certain established thresholds. If the device crossed these thresholds, an alert would be issued. However, this offered little room for prediction and was reactive rather than proactive. The new ML model, on the other hand, uses patterns within the sensor data to predict equipment faults with an impressive precision rate of 91%. This predictive approach allows dealers to anticipate equipment failure, facilitating a more proactive response that can help reduce downtime significantly.

A key component of this framework is AWS Glue, Amazon’s fully managed extract, transform, and load (ETL) service. AWS Glue has been utilised for data processing paralleling, effectively handling the massive datasets. Meanwhile, Amazon SageMaker, a fully managed service that provides developers with the ability to build, train, and deploy machine learning models, was instrumental in feature engineering and establishing a supervised deep learning model.

However, the journey of revolutionising HVAC predictive maintenance was not without its challenges. The team had to grapple with issues relating to data scalability, model scalability, and model precision. Overcoming these obstacles involved intricate problem-solving and innovation, using the strengths of Amazon’s machine learning platform.

Scalability was of paramount importance, considering the volume of the growing historical sensor data. Furthermore, thousands of connected HVAC units are added to the system, with each one continuously streaming data. The cloud-based solutions of Amazon made it possible to manage this data influx.

In conclusion, this innovative predictive maintenance tool that marries Carrier’s HVAC knowledge with Amazon’s machine learning prowess has enhanced the customer experience drastically, paving the way for a new era in HVAC solutions. Early detection of potential faults in the equipment, even before they become imminent, reduces downtime and empowers both the company and its customers.

This venture between Carrier and Amazon also demonstrates the rewards of amalgamating machine learning with conventional industries, signaling a paradigm shift that could potentially extend beyond HVAC to various other sectors. In essence, it underscores the transformational power of machine learning technologies and their potential to significantly impact businesses and customers alike.

With Amazon’s machine learning solutions, Carrier Corporation has paved the way for an era of smarter, more efficient HVAC systems that prioritize customer experience and deliver reliability at every turn. Truly, the future of HVAC looks brighter, smarter, and more intuitive.