Interact Analysis’s new report on the market for predictive maintenance highlights the potential for a new relationship between component manufacturers, OEM machine builders, and end users.

  • By 2024, the market for predictive maintenance in motor driven systems is forecast to reach a valuation of $906.1 million
  • Enhanced demand for remote monitoring as a result of COVID-19 means there will be no slowdown in market growth
  • SaaS is likely to be the main business model for provision of predictive maintenance, and also eases concerns over data ownership

Interact Analysis, my new favorite market research firm, has announced an in-depth examination of the predictive maintenance market. It forecasts a boom in the sector, propelled by the emergence of smart sensors able to monitor crucial parts of a motor-driven system that are not covered by legacy maintenance devices and methods. Advanced smart sensors will allow delivery of viable cloud-based predictive maintenance service packages using a SaaS business model.

One reason I like Interact Analysis right now is methodology. In addition to 40+ hours of primary research interviews, Interact Analysis has utilized data from national manufacturing surveys, as well as data developed for other research areas. This data, combined with the information gathered from interviews, is the base at which estimates are developed.

The report shows that the market for predictive maintenance in 2019 was $117.5 million, largely made up from legacy predictive maintenance products such as portable monitoring devices. Many of these devices will maintain strong growth in the coming decade but will be used in tandem with new technologies such as smart sensors, the latter fueling an expected boom in market value of predictive maintenance technology, up to almost $1 billion in 2024. The significant fall in price of the capacitive based microelectromechanical systems (MEMS) found in Smart Sensors will be one of the drivers of this market.

I like their methodology and analysis—except for forecasting. Predicting future sales is so fraught with uncertainty that I take it as an interesting guide. Evidently sensor manufacturers reported doubling of sales over the two previous years. Look at the numbers and you can see that Interact assumed that doubling to continue through 2024.

When I read through the report synopsis, I was struck by the reliance on smart sensing as a foundation to the market growth for predictive maintenance. I missed a point. They have detected the beginnings of a trend that I have not yet seen. Software-as-a-Service applied to these intelligent devices. Selling the data, not the sensor, so to speak. I’m interested in your feedback on this development. And whether it can drive this market to a billion dollars.

Back to the report:

Smart sensors, which typically monitor sound, temperature, and vibration, may not provide the depth of data offered by some legacy devices, but they have significant advantages. Whereas most legacy devices are attached to motors, IA predicts that only 53% of smart sensors will be attached to motors by 2024. The rest will be attached to other machine components which are also subject to the wear and tear of daily use. This means that the application of predictive maintenance will be far more widespread in the factories of the future.

Blake Griffin, lead analyst on predictive maintenance at Interact Analysis, says: “Smart sensor technology coupled with IIoT capabilities give component manufacturers and OEM machine builders the scope to offer end users an anticipatory service package. For most providers of predictive maintenance, the logical business model will be software as a service. A side benefit of SaaS is that it ties all technologies together under a single solution – thereby eliminating concerns regarding data ownership. Additionally, advancements in embedded machine learning will improve the ability for predictive maintenance to be installed in new or non-standard applications that are less well understood, further fueling growth.”

Adrian Lloyd, CEO of Interact Analysis, adds: “Modern predictive maintenance technology is currently at the beginning of an exponential growth trajectory. Now is a more important time than ever for suppliers to understand key trends at play so they may work at carving out their share of this market – forecast to be worth nearly $1 billion by 2024.”

Griffin further explained the background in a Blog Post. Following are some excerpts.

What are Smart Sensors?

Smart sensors are a fairly new technology that are placed on equipment to gather various data points, most commonly vibration and temperature measurements. Smart sensors then transmit this information wirelessly to a data collector or gateway. When analyzed, this data is particularly useful for assessing the health of equipment as usually the level of vibration and temperature increases as equipment becomes faulty.

How is this Different from Condition Monitoring?

In a traditional condition monitoring system, very little effort is made to determine when equipment will fail, instead relying on set parameters to determine when an asset is at risk of failing. The problem with this approach is that it limits the number of applications which can be monitored. If parameters must be set for an alarm to be triggered, those parameters must be well understood. This decreases the reliability of these systems in applications that are not well understood.

For predictive maintenance to be performed, a level of intelligence must exist somewhere in the plant infrastructure, whether in the form of software, hardware or even application expertise by an experienced operator. A historical log of how the equipment being measured has performed must be utilized to assess if it is trending towards a failure. Increasingly, machine learning algorithms are being utilized to enhance the understanding of the application being measured. This technology utilizes the historical data produced by the smart sensor to better understand and recognize patterns. Having an automated solution for pattern recognition allows for quicker and more reliable detection of anomalies within the data. This not only expands the number of applications able to be monitored beyond just well understood ones, it also increases the amount of time operation managers have to resolve a piece of equipment that is trending towards failure.

Key Driver: A Push for the Realization of Digitalization and IIOT

The most important trend impacting industrial automation is the digitalization of these systems and the equipment within. Over the last 6-7 years, remarkable breakthroughs in technologies that help improve plant efficiency, productivity and reliability have been developed, although uptake so far has been challenging due to the cautious nature of end users when it comes to adopting new technologies.

While these vendors have released software and services aimed at harnessing the benefits of IIoT, it is clear that in order to make use of these solutions, a substantial increase in the number of connected devices is needed. Smart sensors represent an important piece of this puzzle. Since the advent of smart sensors, major automation vendors like ABB, Siemens, WEG, and Nidec have all released their own versions, presumably recognizing the enabling behavior of this technology. We expect this trend to continue as the product is desperately needed in order for manufacturers to begin generating tangible benefits from IIoT technology.

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