This article was written by Daphne Tomlinson, Independent Senior Research Associate at Memoori.
A wide range of HVAC equipment and service providers are competing for a slice of the IoT enabled business from commercial real estate owners and operators. The proliferation of machine learning and IoT platforms aimed at energy management, remote diagnostics and preventative maintenance can be overwhelming and make selection difficult for facility managers, building owners and operators.
Machine learning and IoT platforms are at or nearing the "Peak of Inflated Expectations", according to Gartner's Hype Cycle for Emerging Technologies 2017 and the hype around proactive and preventative maintenance in general is in danger of obscuring the value that predictive maintenance (PdM) technology can bring to facility management in commercial buildings.
The main advantage of PdM compared to preventative maintenance is that it examines the current state of the machine and the trend with which the machine develops over time whereas preventative maintenance uses a fixed cycle for the replacement of machines and machine repairs, which can often cause unnecessary replacement of machinery or machine parts.
Several recent case studies involving the use of PdM for diagnosing potential HVAC equipment failures in commercial buildings have demonstrated their value in reducing the total cost of ownership and highlighted the disruptive price point which IoT based solutions offered by startup companies are bringing to the marketplace.
Maintenance represents the bulk of the costs occurring in a commercial building's lifetime but only 12% of commercial and industrial buildings have a PdM program, according to Augury. PdM has historically been limited in applicability by its complexity and cost of deployment. However, enabled by the advent of IoT platforms, wireless sensors and machine learning, startup companies have entered the arena with more affordable solutions.
New entrants, such as Petasense and Augury in the US and Movus in Australia are collaborating with established facilities management, HVAC and real estate service firms to implement more cost effective solutions.
Petasense, an Industrial IoT startup headquartered in Silicon Valley, has been utilizing the Electric Imp IoT Platform to successfully accelerate the development of its machine learning based predictive maintenance technology. Petasense’s technology features the Mote – a wireless, triaxial vibration sensor – which embeds Electric Imp technology for WiFi connectivity. The Petasense and Electric Imp clouds integrate to collect vibration sensor data, so that it can be trended and analyzed using machine learning algorithms.
Hugo Fiennes, CEO at Electric Imp said “By building on our secure IoT Platform, Petasense was able to simplify the deployment and lifecycle management of its predictive maintenance sensors, while reducing upfront costs by up to 50 percent – a savings that can be passed down to customers.”
The Petasense solution embedded with the Electric Imp platform is already being implemented by Jones Lang LaSalle (JLL) to monitor critical HVAC equipment at a Life Science client facility in Redwood City, California. JLL decided to innovate and implement IoT based predictive maintenance as a way to differentiate and gain competitive advantage. They rolled out the Petasense solution and the scope of the project spanned all critical rotating machines across two buildings. It included all the air handlers, exhaust fans, chillers and compressors. The Industrial IoT and machine learning technology makes it possible for JLL to continuously monitor, analyze and predict equipment health with actionable diagnostics in real time.
“Petasense delivers on-demand predictive maintenance at a disruptive price point,” said Sean O’Connor, reliability engineer at JLL.
In November 2017, Augury announced Augury Halo, a continuous diagnostics platform for industrial and commercial facilities. This cloud-based diagnostics solution monitors mechanical equipment and predicts failures before they happen. The system’s ability to provide actionable insights from machine data leads to increased equipment life, improved machine reliability and more efficient operations.
Augury diagnoses and predicts machine performance using vibration and ultrasonic sensors paired with machine learning algorithms. Based on an ever-growing Malfunction Dictionary that includes tens of thousands of machines, their algorithms have been trained to identify the first sign of a malfunction and provide the user with actionable insights and recommendations for next steps.
Augury's predictive maintenance solution has been deployed across multiple facility types, such as hospitals, higher education, K-12 schools and manufacturing facilities to reduce downtime, energy usage and operational costs. Customers include Johnson Controls, Trane, Carrier, Mueller, Aramark and AECOM.
Brisbane-headquartered company MOVUS is the developer of an Industrial IoT sensor and machine monitoring solution called FitMachine. In just under two years, MOVUS has developed and launched the platform, which uses AI to help organisations proactively monitor, manage, and maintain the condition of their industrial machinery. Buildings implementing their solution include the University of Queensland and the Queensland Brain Institue (QBI), who have deployed FitMachines on multiple types of HVAC equipment within their facilities.
The solutions from these suppliers aim to provide a compelling return on investment by reducing the need for manual inspections and turning unplanned outages into planned situations.
They address the specific issue of the high cost of monitoring, diagnosing and predicting potential malfunctions in HVAC equipment, using traditional PdM equipment. From that point of view, they have the edge in the crowded digital commercial buildings space.