- By Craig Lentzkow
- August 02, 2021
There is a huge base of installed machines in the world, typically referred to as “brownfield." This represents a tremendous opportunity for a predictive maintenance solution that can be “bolted-on” existing equipment to provide condition-based monitoring. For this solution to succeed, it must include: support for many machines in a single eco-system; simple, quick and remote commissioning; flexible choice of attached sensor devices; and built-in security.
Predictive maintenance is a type of condition-based maintenance that monitors assets, such as equipment and machines, using sensor devices. These sensors supply real-time data which can be used to predict when the asset will require maintenance.
For OEMs and end users, the financial benefits of predictive maintenance monitoring of their machines can be dramatic.
If failing equipment can be repaired or replaced in a timely manner, expensive downtime or even catastrophic failure can be avoided.
There is a huge base of installed machines in the world, typically referred to as “brownfield." This represents a tremendous opportunity for a predictive maintenance solution that can be “bolted-on” existing equipment to provide condition-based monitoring.
For this solution to succeed, it must include: support for many machines in a single eco-system; simple, quick and remote commissioning; flexible choice of attached sensor devices; and built-in security.
Predictive maintenance benefits
There's nothing new about Predictive Maintenance. DCS, PLC and SCADA vendors have been supporting it for several decades. However, Internet and cloud technologies have opened up fresh markets. By side-stepping proprietary solutions and working "open source" in the IIoT/Industry 4.0 space, a new dimension in monitoring and asset management has become viable.
Assets are costly and downtime is expensive. For OEMs and end users, the financial benefits of Predictive Maintenance can be dramatic (Source: Accelix Community):
10x Return on Investment (ROI)
25-30% reduction in maintenance costs
70-75% elimination of breakdowns
35-45% reduction in downtime
20-25% increase in production
These benefits accrue in a variety of ways. For example, a machine builder can retrofit machines in the field with sensors and a sensor “edge device." The sensors may be accelerometers to monitor vibration, or thermocouples to monitor machine temperature. This data has value—a change in vibration or temperature can indicate a bearing failure or a motor failure. The sensor edge device collects the data from the monitoring sensors and transmits it to an IT or cloud-based application for display and analysis.
Cloud-based predictive maintenance solutions can operate in isolation from a machine’s existing control system and therefore have no impact on existing automation. Their primary goal is to collect and aggregate data from equipment-based sensors that are installed anywhere in the world. This data can be used to identify upcoming problems. Failing equipment can be repaired or replaced in a timely manner.
With appropriate applications, the direct maintenance benefits can improve machine uptime thus improving production yields. For a machine vendor, design improvements can be implemented more quickly too, as weaknesses in existing equipment are identified from the collective experience of a multitude of machines.
With the global market for Predictive Maintenance expected to grow from $3.8 B in 2020 to $13.9 B annually by 2026, there's clearly a huge demand for effective solutions. The legacy of Covid 19 will add to the pressure. North America is projected to be dominant in the Predictive Maintenance market through 2026, with the US market alone expected to reach $4.2B by 2026. (Source: Global Industry Analysts Inc.)
Enormous potential in Brownfield
Where suppliers have hundreds of individual machines installed, perhaps globally, the potential is huge. Cloud-based systems make it easy for such a vendor to monitor their machines located anywhere without leaving their offices. 24/7 monitoring makes it very easy to learn operating profiles.
At the top end, high value IIoT/Industry 4.0 solutions are clearly going to be needed. These visions will be easier to implement on new machines than in the past. New machines, particularly in greenfield applications, will most likely have IIoT Predictive Maintenance support designed in.
At the lower end however, a vast untapped market exists. Brownfield equipment offers enormous potential for predictive maintenance.
Potential customers lie in almost every industrial market, including the following:
Retrofitting is key
Retrofitting machines in the field with sensors and a sensor “edge device” is key to building a low-cost predictive maintenance system that is both secure and simple. The sensor edge device aggregates data from a wide choice of sensors, handles local communication processing, and provides an Internet uplink to the cloud. Once in the cloud, data can be fetched by a customer application for additional analysis and processing.
The sensor edge device acts as a gateway between local sensor data and the cloud. Because the sensor edge device bypasses the existing control system, it is inherently secure from any intrusions that would impact security and safety in the production systems. And by bypassing existing controls, there is no need to interface with or reprogram the PLC.
Ideally, the sensor edge device would leverage a standard sensor communications technology, such as IO-Link (IEC 61131-9), to maximize the choice and variety of sensors that can be supported. There are thousands of IO-Link sensors in the market today, covering measurement variables that include: temperature, vibration, humidity, position, corrosion, pressure, level, flow, and many others.
An added benefit of IO-Link is that it provides automated parameter settings. Once an IO-Link sensor is plugged into the sensor edge device, it is automatically onboarded to the cloud. Data is immediately available for browser display using Dashboards. This simple plug-and-play connection gets data into the cloud where it can be accessed by user’s choice of analysis and processing software.
Hilscher has recently developed such a sensor edge device, called sensorEDGE, that provides the sensor-to-cloud connections needed for retrofit applications. Each sensorEDGE aggregates data from up to eight IO-Link sensors and multiple sensorEDGE devices can be deployed in an installation. The solution represents a low-cost and easy-to-implement way to get data from sensors retrofitted to a machine, into the cloud for monitoring worldwide. Machine manufacturers could develop a “Retrofit Kit” that includes the sensorEDGE and appropriate sensors for the application.
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