For many years, the concept of predictive maintenance has been a topic of debate. Recently, however, something has changed and the results are undeniable. Thanks to a new era of intelligent sensors and sophisticated software algorithms, predicting machine failures before they occur has become a reality. This allows preventive action to be taken, thus avoiding any disruption to factory operations.
Predictive maintenance has long been talked about, but recent developments have brought about a significant change.
The field of Predictive Maintenance is merging with the Industrial Internet of Things, which is leading to a rapid increase in associated revenues.
An in-depth analysis of the predictive maintenance market reveals that revenue growth is skyrocketing. A compound annual growth rate of 40.6% is forecast over the entire forecast period.
What is driving this change?
The advanced technology behind smart sensors and software offerings is a contributing factor, which will be discussed later. However, new business models also play a key role.
Historical heritage of predictive maintenance
There has been a lot of talk about predictive maintenance in relation to IIoT lately, which gives the impression that a new concept has emerged.
Actually, predictive maintenance as a practice is not new, but its integration with IIoT is. Predictive maintenance based on older technologies is already established in certain industries.
One of the conventional products for predictive maintenance is the portable monitoring device, which is widely used to assess the well-being of industrial assets.
These devices are typically used in industrial motor systems to obtain data as information:
Electrical
Vibration
Acoustic
Electromagnetic
by connecting the device directly to the system.
The market has grown and will continue to grow in the future.
Although IIoT-driven predictive maintenance may slightly reduce the market for portable monitoring devices, it is mainly targeting new industrial sectors.
Intelligent sensors
Small wireless devices known as smart sensors, equipped with built-in computing power, mostly rely on capacitive MEMS technology.
This technology has significantly decreased in cost due to its widespread use in consumer electronics and economies of scale. As a result, improvements in sensor computing power have led to a boom in industrial predictive maintenance solutions.
Battery life is also crucial and recent advancements in battery density have enabled sensors to function for several years.
The range of industrial components that can be effectively monitored by smart sensors has expanded beyond just electric motors.
As technology hurdles such as limited bandwidth and high noise levels are overcome, these sensors are being utilized in various other applications such as:
Gearboxes
Pumps
Bearing blocks.
IIoT systems heavily depend on the deployment of numerous smart sensors throughout factories. However, without advanced smart software, they hold little value.
Advanced software
For effective predictive maintenance, the data collected by sensors must be analysed.
In traditional systems, this analysis is performed by maintenance technicians who inspect the data collected by handheld monitoring devices.
Machine learning algorithms are increasingly used because they have the ability to identify patterns, resulting in rapid detection of abnormal data.
This improves the monitoring of new and unfamiliar applications, and gives earlier warning of machine failures.
In some cases, machine learning is even embedded in the sensor itself, allowing it to determine what data is meaningful before transmitting it for further analysis.
This trend will become increasingly important in the future.
Next-gen business models
The challenge for suppliers in the predictive maintenance industry has been to find a profitable business model.
The interests of users and manufacturers are at odds here. While industrial equipment users are looking to extend the life of their equipment, original equipment manufacturers (OEMs) lose potential replacement revenue by extending equipment life.
Predictive maintenance requires OEMs to find ways to maintain profitability without sacrificing revenue.
Currently, predictive maintenance systems are often sold on a per-unit basis, and access to analytical software is offered on a cost-per-sensor basis.
This approach does not resolve the above-mantioned conflict of interest and therefore has limited potential.
A promising new business model is machines as a service (MaaS), similar to software as a service (SaaS).
Performance-based pricing involves predetermining targets and pricing the contract based on the achievement of those targets.
MaaS is one of the emerging new business models that will quickly propel us into an era where predictive maintenance is widely used in factories and machines.
Preventive maintenance: Investigating market expansion reasons