Interact Analysis has just published a report titled The Market for Predictive Maintenance in Motor Driven Systems that outlines three main points for why predictive maintenance will soon see exponential growth.
First, the organization says that by 2024, the market for predictive maintenance in motor driven systems will reach $906.1 million. Second, enhanced demand for remote monitoring as a result of COVID-19 means there will be no slowdown in market growth. Finally, SaaS is likely to be the main business model for provision of predictive maintenance.
Projected to hit a valuation of nearly $1 billion by 2024, growth in the market for predictive maintenance hardware and software in motor driven systems is fueled by the emergence of smart sensing, its dedicated software and the new business models which have emerged to support this technology says Interact Analysis. Legacy condition monitoring systems along with portable monitoring devices have paved the way for this rapid growth.
According to the report, the price point which smart sensors are being offered make them a particularly attractive solution. Most smart sensors use a capacitive MEMS sensor to take readings. These types of sensors are commonly found in consumer electronics and, having achieved economies of scale in these applications, enabling the smart sensor concept for industrial applications.
Condition monitoring technologies like portable monitoring devices and hard-wired sensors, have been used for years in process industries such as oil & gas, chemicals and pharmaceuticals.
Interact Analysis says for predictive maintenance to be performed, a level of intelligence must exist somewhere in the plant infrastructure. 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 used to enhance the understanding of the application being measured. Increasingly, machine learning algorithms are being used in software, and in some rare cases, the hardware. In these cases, embedded machine learning takes place on the smart sensor itself to determine what data is relevant before transmitting that data to the software for deeper analysis. Interact Analysis says this latter point is an important trend for smart sensors.
The organization also found that the most important trend impacting industrial automation is the digitalization of these systems and the equipment within. Predictive maintenance is a low hanging fruit in the IIoT space. Vendors of smart sensors are already seeing their businesses doubling or tripling year over year.
