Augury Updates Its IoT-Based Guaranteed Diagnostics™ Product with Production Loss Guarantee for Critical Rotating Assets

In June 2020, Augury, a specialist in equipment health monitoring using the Internet of Things (IoT) and Artificial Intelligence (AI), introduced Guaranteed Diagnostics™. Launched together with Munich Re subsidiary Hartford Steam Boiler (HSB), a specialist in equipment insurance, this warrantee offering provides an insurance-backed equipment repair or replacement service for machine operators in the event of equipment breakdown that could have been predicted by Augury’s predictive maintenance platform.

In December 2023, Augury took this offering a step further: again with HSB’s backing, the company launched its Production Loss Guarantee, an extension of Guaranteed Diagnostics™. As well as expanding the types of equipment covered by the Guaranteed Diagnostics™ service, the offering also offers financial compensation for downtime and productivity loss caused by machine failure, capped at US$100,000 per machine/per incident and a total of US$150,000 per machine/per year. According to the announcement, this offering is provided to new customers at no additional cost for 1 year.

Augury’s Auguscope solution started as a wired sensor solution connected to a mobile device for route-based monitoring. The solution has since evolved into a wireless solution consisting of Bluetooth® sensors connected to cellular gateways. These gateways backhaul data to the Augury platform for AI-based predictive maintenance, from which specific recommended actions and timelines are sent to customer systems. The “secret sauce” lies in the close integration of Augury’s sensors and the software, powered by AI analytics. The solution is sold as a monthly service offering.

Machine downtime is commonly cited as one of the most serious problems for manufacturing operations. Estimates of the financial burden on operations vary widely and is an inexact science; Augury’s own estimate is that a compression failure at a bottling plant may cost US$275,000, while a fluid pump failure at a pharmaceutical plant may cost US$160,000. Most predictive maintenance solutions today are designed to facilitate early inspection and repair of machinery to ensure that these high costs are not incurred, promising to make it less likely that things will go wrong. Guaranteed Diagnostics™ goes a step further, promising compensation in the event that things do go wrong. Augury notes how this raises the conversation “from the maintenance realm to the boardroom.”

Already in 2020, Augury noted how the “growing trust in the accuracy and consistency of predictions” made Guaranteed Diagnostics™ possible. The company’s growing shift toward wireless IoT solutions demonstrates the increasing capabilities of these devices. While battery concerns remain one of the biggest issues to address, there is increasingly little concern over the reliability of devices. In the last 3 years, this has led to a huge increase in wireless retrofit IoT sensors, particularly in brownfield environments, which are sometimes cited to account for 80% of the manufacturing installed base.

AI is the second component of this. Many companies are turning to AI as an essential component of a predictive maintenance solution, and predictive maintenance is one of the obvious use cases where AI is more than simply a buzzword. The wide variety of data coming off machines, plus the difficulties in deciphering time-series data and in extracting and correlating the features relevant to create an AI model make the value proposition clear. In examples of traditional software programming, data scientists may take a couple of years to build an analytical model; with AI, this process can be brought down to weeks.

While most AI for predictive maintenance today is located in the cloud, there is increasing interest in understanding how to combine this with AI/Machine Learning (ML) analysis at different layers of the edge. Augury is assessing the possibilities for this technology, with the goal of reducing how much data are transmitted—one of the greatest benefits being an increase in sensor battery life. This technology is by no means mainstream in industrial environments today, but there is a growing ecosystem of vendors building solutions optimized for resource-constrained devices. Horizontal model builders will increasingly partner with vertical solution providers as the next evolutionary leap for edge AI/ML, which will lead to an explosion in intelligence at the edge in the coming years.

HSB is the final interesting part of the Guaranteed Diagnostics™ solution. A specialist in equipment insurance covering industrial, commercial, and residential, the company has launched a number of IoT-based service offerings over the past few years. In addition to its partnership with Augury, HSB Applied Technology Solutions (ATS) launched Steam-as-a-Service with Miura Boiler and Armstrong International, relying on Miura’s IoT-connected boilers and analytics. This comes in addition to its acquisition of Relayr in 2018, a middleware platform specifically targeted toward enabling IoT-based as-a-Service transformation for its customers. Insurance company activities with the IoT have primarily been seen in consumer markets, but it is clear that the IoT is increasingly making waves with enterprise and industrial markets, with significant partnerships in the last 2 to 3 years in the industrial sector and in the supply chain.

Augury’s Guaranteed Diagnostics™ offering in partnership with HSB offers a convincing blueprint for how IoT predictive maintenance can transform industrial markets, entirely changing the paradigm around machine failures. Companies interested in the predictive maintenance space should take note of the following trends to ensure that they are offering leading solutions:

• Embedded Sensors: While retrofit sensors are an excellent way of dealing with brownfield environments, a clear trend in Industrial IoT (IIoT) is that Original Equipment Manufacturers (OEMs) are embedding sensors and connectivity into their machines. Leading predictive maintenance companies will explore partnerships with OEMs to understand how to create solutions based on OEMs’ equipment.
• Understand How to Use AI: AI is a trendy term, but many companies in the industrial space are not familiar with how to implement solutions. Companies should understand that AI is not a single technology, but a collection of capabilities that will create the most value when orchestrated between the cloud and multiple layers of the edge.
• Create Value for Customers: This seems an obvious point, but many IoT solutions for predictive maintenance often lead to more questions than answers for industrial companies. How to manage and maintain fleets of devices in the field? How to ensure data are translated into insights and recommendations?

The predictive maintenance market is increasingly flooded with IoT solutions, often with limited differentiation. Leading vendors differentiate themselves with a focus on outcomes and take on the complexity of building, integrating, and predicting themselves.